Aplicationartificialintelligence_Souz12233a_2020

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<p>UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE</p><p>CENTRO DE TECNOLOGIA</p><p>PÓS-GRADUAÇÃO EM ENGENHARIA DE PRODUÇÃO</p><p>APLICATION OF ARTIFICIAL INTELLIGENCE FOR WIND</p><p>TURBINE OPERATION AND MAINTENANCE: PROPOSAL OF</p><p>FRAMEWORK</p><p>MATEUS GUILHERME MELO DE SOUZA</p><p>NATAL</p><p>2020</p><p>UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE</p><p>CENTRO DE TECNOLOGIA</p><p>PÓS-GRADUAÇÃO EM ENGENHARIA DE PRODUÇÃO</p><p>APLICATION OF ARTIFICIAL INTELLIGENCE FOR WIND</p><p>TURBINE OPERATION AND MAINTENANCE: PROPOSAL OF</p><p>FRAMEWORK</p><p>This thesis is the result of the studies as a requisite for</p><p>acquiring the title of Master of research in Industrial</p><p>Engineering of the Federal University of Rio Grande</p><p>do Norte.</p><p>Supervisor: Prof. Dr. Mario Orestes Aguirre</p><p>González</p><p>MATEUS GUILHERME MELO DE SOUZA</p><p>NATAL</p><p>2020</p><p>Souza, Mateus Guilherme Melo de.</p><p>Aplication of artificial intelligence for wind turbine</p><p>operation and maintenance: proposal of framework / Mateus</p><p>Guilherme Melo de Souza. - 2021.</p><p>94f.: il.</p><p>Dissertação (Mestrado) - Universidade Federal do Rio Grande</p><p>do Norte, Centro de tecnologia, Programa de Pós graduação em</p><p>Engenharia de Produção, Natal, 2021.</p><p>Orientador: Dr. Mario Orestes Aguirre González.</p><p>1. Artificial Intelligence - Dissertação. 2. Operation and</p><p>Maintenance - Dissertação. 3. Systematic Literature Review -</p><p>Dissertação. 4. ANN - Dissertação. 5. LSTM - Dissertação. I.</p><p>González, Mario Orestes Aguirre. II. Título.</p><p>RN/UF/BCZM CDU 658.5</p><p>Universidade Federal do Rio Grande do Norte - UFRN</p><p>Sistema de Bibliotecas - SISBI</p><p>Catalogação de Publicação na Fonte. UFRN - Biblioteca Central Zila Mamede</p><p>Elaborado por RAIMUNDO MUNIZ DE OLIVEIRA - CRB-15/429</p><p>ACKNOWLEDGMENTS</p><p>This work is dedicated to my family as they always got my back in moments of</p><p>doubt. I also would like to thank the professor Mario González for pushing me to the limit and</p><p>for believing that I could explore the research theme further and further, you helped me finding</p><p>my purpose. Moreover, I also thank my friends of the Creation research group since I would</p><p>not have accomplished this research without them. You guys are amazing!</p><p>This study received support from the Brazilian National Council for Scientific and</p><p>Technological Development (CNPq) a federal organization under the administration of the</p><p>Ministry of Science and Technology, which is dedicated to promoting scientific research and</p><p>formation of human resources in Brazil.</p><p>RESUMO</p><p>O aquecimento global alertou à comunidade internacional da necessidade de uso de fontes</p><p>renováveis e limpas na geração de energia elétrica. Nesse cenário, a energia eólica se encontra</p><p>em expansão. Como essa fonte de geração depende de um equipamento central, o aerogerador,</p><p>a sua operação e manutenção é fundamental para a viabilidade do negócio, e por isso, a</p><p>aplicação de novas tecnologias, como a Inteligência Artificial, pode trazer maior</p><p>competitividade do setor. O objetivo do estudo é apresentar um framework para a aplicação de</p><p>Inteligência Artificial na operação e manutenção de parques eólicos. Para tanto, foi realizada</p><p>uma pesquisa teórica e de campo. A pesquisa teórica considerou uma revisão tradicional da</p><p>literatura e uma revisão bibliográfica sistemática. O estado da arte foi identificado pela análise</p><p>detalhada de 51 artigos obtidos na Plataforma Periódicos da Capes. Foram identificados os</p><p>equipamentos estudados, métodos e métricas adotadas e etapas da aplicação da IA. A pesquisa</p><p>de campo foi realizada pela aplicação do framework em um parque eólico, mediante a</p><p>simulação de uma aplicação de monitoramento da condição de rolamentos através da</p><p>modelagem da temperatura, mediante dados SCADA. Três modelos de redes neurais foram</p><p>testados: Rede Neural Feedforward, Rede Neural Autorregressiva e Long Short-Term Memory</p><p>O modelo de Long Short-Term Memory apresentou a melhor performance dentre os algoritmos</p><p>testados, mesmo quando comparado com outros estudos, o que mostra que ele pode ser usado</p><p>para esse tipo de aplicação. O framework proposto é dividido em quatro macro-etapas: Seleção</p><p>da aplicação, preparação de dados, desenvolvimento do modelo e avaliação de resultados.</p><p>Palavras-chave: Inteligência Artificial, Aerogerador, Operação e Manutenção, Revisão</p><p>bibliográfica sistemática, ANN, LSTM.</p><p>ABSTRACT</p><p>Global warming has alerted the international community to the need to of renewable and clean</p><p>energy sources in the generation of electricity. In this scenario, wind energy is expanding. As</p><p>this source depends on a central equipment, the wind turbine, its operation and maintenance are</p><p>fundamental for the viability of the business and, therefore, the application of technologies,</p><p>such as Artificial Intelligence, can improve the competitiveness of the sector. The objective of</p><p>this study is to present a framework for the application of Artificial Intelligence in the operation</p><p>and maintenance of wind farms. For this, theoretical and field research were carried out. The</p><p>theoretical research complemented a traditional literature review and a systematic literature</p><p>review. State-of-the-art was identified through the analysis of 51 articles obtained from the</p><p>Periodicos Capes Platform. The research identified the equipment studied, data, methods and</p><p>metrics adopted in the application of AI. The field research was carried out by applying the</p><p>framework to a wind farm, simulating an application of condition monitoring for bearings</p><p>through the modelling of its temperature using SCADA data. Three neural networks models</p><p>were tested: Feedforward Neural Network, Autoregressive Neural Network and Long Short-</p><p>Term Memory (LSTM). The LSTM model presented the best performance among the tested</p><p>algorithms, even when compared to other studies, which shows that it can be used for this type</p><p>of application. The proposed framework is composed by four macro-steps: Selecting</p><p>application, data preparation, model development and evaluation of results.</p><p>Keywords: Artificial Intelligence, Wind Turbine, Operation and Maintenance, Systematic</p><p>Literature Review, ANN, LSTM.</p><p>FIGURE INDEX</p><p>Figure 1.1: Global Energy matrix evolution. ............................................................................ 15</p><p>Figure 1.2: Countries with wind power as the most competitive energy source. ..................... 15</p><p>Figure 1.3: 17 objectives of the Agenda 2030. ......................................................................... 18</p><p>Figure 1.4: Average capacity factor by country. ...................................................................... 18</p><p>Figure 1.5: Number of publications about artificial intelligence applications for wind turbine</p><p>maintenance according to a survey carried out in the Periodicos Capes Platform. .................. 19</p><p>Figure 2.1: Research procedure. ............................................................................................... 22</p><p>Figure 2.2: Systematic Literature Review procedure. .............................................................. 23</p><p>Figure 3.1: Evolution of offshore wind turbines since 1991. ................................................... 26</p><p>Figure 3.2: Main components of a typical large-scale wind turbine. ....................................... 26</p><p>Figure 3.3: Relationship between AI, ML and deep learning. ................................................. 31</p><p>Figure 3.4: Typical neuron model. ........................................................................................... 33</p><p>Figure 3.5: A plane separating data using SVM ....................................................................... 36</p><p>Figure 4.1: Trend of the publications about the theme per year. .............................................. 40</p><p>in order to act in the most important alarm</p><p>when more than one trigger at the same period. Stetco et al. (2019) reviewed machine learning</p><p>applications for wind turbine condition monitoring. They analysed 144 articles, concluding that</p><p>most models use SCADA or simulation data; Machine Learning models are applied mainly for</p><p>classification, with less studies using regression; And that Neural networks, SVM and Decision</p><p>Trees are the most common algorithms used.</p><p>Hence, in this research, it was found studies that developed applications of neural</p><p>networks and other AI methods in a variety of applications for wind turbine maintenance. Here</p><p>are the main applications found in the studies according to its main goal:</p><p> Condition monitoring systems: It is the most common application that</p><p>appeared in the studies, they are systems designed to monitor the equipment</p><p>health, identifying faults before they happen and, in some cases, diagnose</p><p>it. According to Helbing and Ritter (2018), condition monitoring systems</p><p>can be divided into model-based approaches (numerical model of the wind</p><p>turbine or subcomponents), signal processing approach (vibration-based</p><p>signal processing and analysis) and data-driven approaches. Normal</p><p>behaviour models are a highly studied data-driven approach and it is similar</p><p>to Statistical Process Control. It consists in creating a model capable of</p><p>mimic the normal behaviour of parameters based on other variables, the</p><p>39</p><p>predicted values are than compared with the actual values measured in the</p><p>sensors, generating a prediction error, or residual. The residuals are than</p><p>analysed in residual control charts as large values or trends may be an</p><p>indicator of incipient faults. Another approach to be mentioned is the</p><p>Remaining useful life prediction that estimates how much time is left for the</p><p>machine or the component until a system malfunctioning occurs, which does</p><p>not require an in-depth understanding of the physics of the system as it is</p><p>uses historical failure data (LI et al., 2019).</p><p> Maintenance optimization: Applications that result in improving the</p><p>maintenance strategies of single wind turbines or the whole wind farm.</p><p>Usually, they use some sort of condition monitoring method with addition</p><p>of further analysis, such as clustering (HAMEED; WANG, 2012),</p><p>scheduling (YANG; HUANG; HUANG, 2016) or cost (LU et al., 2018).</p><p> Study of indicators: The use of data-mining techniques to study parameters</p><p>and indicators of wind turbines in order to evaluate its performance (SU;</p><p>HU, 2018) or propose new indicators to help maintenance decision makers</p><p>(ASTOLFI et al., 2015).</p><p> Inspection systems: Systems created with the goal of providing non-</p><p>continuous inspections, detecting faults or defects through a test</p><p>(JIMENEZ; MUÑOZ; MARQUES, 2018). There are also Applications that</p><p>allow the use of machines with this end such as drones (SHIHAVUDDIN</p><p>et al., 2019) or robots (WANG et al., 2013) for inspection of components.</p><p>40</p><p>CHAPTER 4: STATE OF THE ART</p><p>This chapter presents the state of the art of the topic as a result of the systematic</p><p>literature review. It is organized in a bibliometric analysis of the selected articles and their</p><p>classification according to their characteristics.</p><p>4.1 Descriptive analysis</p><p>After analyzing the articles that underwent the filtering established in the SLR</p><p>method, the graph presented in Figure 4.1 was developed. It shows the number of publications</p><p>per year. As can be seen in the figure, the first paper was published in 1999 and after that there</p><p>is a 11-year window without publication of articles on the topic until 2011, that came up with</p><p>2 articles published.</p><p>Figure 4.1: Trend of the publications about the theme per year.</p><p>Source: Author, 2020.</p><p>For this moment on, there are publications in every year, varying from 1 in 2015 to</p><p>14 in 2018, year with the most publications. The year of 2019 is the second one with 11</p><p>publications, however, it is worth noticing that the search for articles was held in June 2019, so</p><p>this year has potential to surpass 2018 as the one with most publications about this topic in</p><p>Periodicos Capes Platform.</p><p>1</p><p>0 0 0 0 0 0 0 0 0 0 0</p><p>2</p><p>3</p><p>2</p><p>4</p><p>1</p><p>6</p><p>7</p><p>14</p><p>11</p><p>Number of publications</p><p>41</p><p>4.1.1 Geographic distribution</p><p>This session addresses the geographical distribution of the institutions which the</p><p>authors of the selected articles represent, considering countries. The Figure 4.2 presents the</p><p>number publications by each country. It is important to highlight that some articles had</p><p>contributions from authors from more than one continent, this explains the fact that the sum of</p><p>the percentages of contributions between continents is greater than the total number of articles.</p><p>Figure 4.2: Geographic distribution of the institutions that the authors represent.</p><p>Source: Author, 2020.</p><p>From the figure, one can see that China has been pushing the research about the</p><p>theme with 18 publications in total, representing 35% of participation in the studies. It can be</p><p>explained by the fact that China is the biggest wind energy market in the world. USA is the</p><p>second in the number of publications, since it came up with 5, representing around 10% of the</p><p>studies. UK and Spain are both in third place with 4 publications and 8% of the studies each.</p><p>4.1.2 Publications by journal</p><p>When it comes to journals, there is 36 different scientific journals that had</p><p>publications about the research theme and came up after filtering. The journal named Energies</p><p>is the one with most publications, having 9 in total. It is followed by the Renewable Energy</p><p>journal with 6 in total and Wind Energy with 3 publications. The remaining journals ended up</p><p>with 1 or 2 publications each.</p><p>42</p><p>4.2 Content analysis</p><p>In the case of this research these applications were analysed and then classified</p><p>according to their main goal. Figure 4.3 shows a pie chart of the distribution of papers by</p><p>applications. As said previously in the Session 3.4, there are 4 literature review studies. The</p><p>articles of Vesely (2017) and Kusiak (2016) are opinion articles published in Power and Nature</p><p>scientific journals respectively, these are not considered in the pie chart of the Figure 4.3 since</p><p>it considers only the articles that are applied research.</p><p>Figure 4.3: Distribution of articles according to application.</p><p>Source: Author, 2020.</p><p>4.2.1 Studies developed by equipment</p><p>The applied research papers were classified by equipment, having different</p><p>objectives, data and artificial intelligence methods used by them. Most of the studies were</p><p>oriented for building applications for the drivetrain/gearbox with 15 papers in total, followed</p><p>by the generator with 8 studies, it can be explained by the Figure 4.4, which presents a</p><p>relationship between equipment, failure frequency and downtime per failure. It can be seen that</p><p>generator, gearbox and drivetrain have the biggest downtime per failure. There are also 7 studies</p><p>for blades, 2 for the pitch system, 3 for the sensors and 12 for the whole wind turbine, with</p><p>some studies focusing in more than one equipment at the same time.</p><p>33</p><p>4</p><p>2</p><p>6</p><p>Condition Monitoring</p><p>Systems</p><p>Maintenance</p><p>Optimization</p><p>Study of indicators</p><p>Inspection Applications</p><p>43</p><p>Figure 4.4: Failure annual frequency and downtime per failu re for the wind turbines.</p><p>Source: IGBA et al. (2015).</p><p>4.2.1.1 Blades</p><p>Due to its operational characteristics, the papers that proposed applications for the</p><p>blades focused on the development of systems to improve inspections using signals (sound,</p><p>guided waves), machines (robots, drones) and other approaches.</p><p>Wang et al. (2013) used neural networks in order to optimize the path of a two-leg</p><p>robot around the whole blade using the blade 3d design as model. Gantasala, Luneno and</p><p>Aidanpää (2017) studied a system capable of detecting imbalance in a non-rotating beam using</p><p>its natural frequencies on neural networks, and finite elements analysis. They suggest that the</p><p>system could be applied to wind turbine blades.</p><p>Jiménez, Muñoz and Marques (2018) used guided waves to build an inspection</p><p>system for identification of delamination faults through a series of machine learning algorithms,</p><p>having the best result with ANN, they applied it in a wind turbine blades in laboratory</p><p>conditions. Jiménez et al. (2019) propose an inspection system that uses guided waves in the</p><p>task of identifying ice imbalance in wind turbine blades, having the best results with SVM and</p><p>KNN, they also applied it in a wind turbine blade in laboratory conditions.</p><p>Chen et al. (2019) present a condition monitoring method for ice accretion</p><p>detection that reduces SCADA data imbalance (difference between normal operating data and</p><p>44</p><p>abnormal operating data) using triplet loss as data pre-processing and Deep neural networks for</p><p>modelling.</p><p>Shihavuddin et al. (2019) proposed a suggestion-based system that facilitates the</p><p>identification of four wind turbine blade damage using drone images, they applied image</p><p>augmentation in order to extent the dataset.</p><p>Liu et al. (2019) developed a wind turbine blade strain prediction inspection system</p><p>method using a combination of genetic algorithm and neural networks, having load, load</p><p>positions and distributions as data applied to a prototype.</p><p>4.2.1.2 Drivetrain and gearbox</p><p>The studies that aimed at wind turbine drivetrain and gearbox proposed applications</p><p>related to condition monitoring systems, however, they used different approaches and goals,</p><p>like monitoring the whole gearbox or just specific components. Some studies aimed at just</p><p>monitoring the equipment for fault detection, not focusing on fault diagnosis. Elhor et al.</p><p>(2009), proposed a vibration-based normal behaviour model for the gearbox using auto-</p><p>associative neural networks.</p><p>Schleichtingen and Santos (2011) made a comparative analysis between linear</p><p>regression, feedforward neural networks and auto-associative neural networks in the task of</p><p>building a normal behaviour model for monitoring the gearbox bearing temperature, generator</p><p>bearing temperature and generator stator temperature using SCADA data of offshore wind</p><p>turbines, in order to detect faults in these components through residual error between the</p><p>modelled temperature and the real sensor temperature.</p><p>Strdczkiewicz and Barszcz (2016) proposed a method for detecting gear faults in</p><p>the gearbox using data related to vibration, load and rotation speed applied in a combination of</p><p>backpropagation neural networks and linear regression.</p><p>Bangalore et al. (2017) built a normal behaviour model for the gearbox bearing</p><p>temperature and gearbox lubrication oil temperature using neural networks and SCADA data.</p><p>They focus mainly on data pre-processing and post-processing, presenting methods for dealing</p><p>with data-filtering, data discontinuity. They also use Malahanobis distance of residuals as the</p><p>mean to detect incipient faults.</p><p>Guo, Fu, and Yang (2018) used SCADA data in convolutional neural networks in</p><p>order to model bearing temperature of the gearbox and then compare it statistically with other</p><p>45</p><p>turbines of the same wind farm in order to identify over-temperature bearing faults. In the same</p><p>line of thought, Fu et al. (2019) present a normal behaviour model for the gearbox bearing</p><p>temperature using SCADA data in an approach combining convolutional neural networks and</p><p>long short-term memory (LSTM), also aimed at detecting over-temperature faults in bearings.</p><p>Li et al. (2019) proposes a combination of regression and ANN models to predict</p><p>the remaining useful life of gearbox bearings, simulating it using vibration data from a test rig.</p><p>On the other hand, some other studies go a step further and, instead of just detecting</p><p>faults earlier, they also aimed at diagnosing it. Ren and Qu (2014) propose a method for shaft</p><p>centreline orbit condition monitoring using simulated annealing algorithm and Hopfield neural</p><p>networks.</p><p>Tang et al. (2014) used manifold learning for pre-processing and SVM for</p><p>classification in order to build a fault diagnosis system for the wind turbine drivetrain using</p><p>vibration data from a real wind turbine.</p><p>Yang, Wang and Zhong (2016) presented a simulation experiment that uses</p><p>Extreme Learning Machine (ELM) to perform multi-fault classification of wind turbine</p><p>gearboxes using vibration data.</p><p>Wu et al. (2017) used wavelet transform and principal component analysis (PCA)</p><p>for pre-processing vibration data of a test rig and Improved Extreme Learning Machine (IELM)</p><p>for model a system capable of diagnose faults in wind turbine gearboxes. Yu, Huang and Xiao</p><p>(2018) present a wind turbine gearbox fault diagnosis method that uses convolutional neural</p><p>networks with batch regularization for training and testing, and compare it with other machine</p><p>learning algorithms, they used the same group of data presented in Wu et al. (2017), which</p><p>might indicate collaboration between studies.</p><p>Zhong et al. (2018) propose a data-driven multi-fault diagnosis method for the wind</p><p>turbine gearbox that uses Hilber-Huang transforms for vibration data pre-processing and</p><p>Extreme Learning Machine for modelling, they verify its effectiveness through data taken from</p><p>a wind turbine simulator.</p><p>As mentioned before, the study of Bach-Andersen, Romer-Odgaard and Winther</p><p>(2018) also have fault diagnosis as the final goal of their research.</p><p>Zhao et al. (2018) established a method for fault detection and analysis in the</p><p>generator and the gearbox using SCADA data as input of a model composed by deep auto-</p><p>encoder (DAE) neural networks that uses the relationship between signals, its reconstruction</p><p>error and an adaptive threshold to identify faults and where they are located.</p><p>46</p><p>Wang et al. (2019) propose a Dual-Extreme Learning Machine fault diagnosis</p><p>framework for single and multi-fault diagnosis in the drivetrain, the results were than</p><p>demonstrated using vibration from a test rig.</p><p>4.2.1.3 Generator</p><p>The papers that aimed at studying wind turbine generators or its specific</p><p>components focused specially in building condition monitoring systems. As said previously in</p><p>the gearbox section, Shleichtingen and Santos (2011) and Zhao et al. (2018) also focused on</p><p>specific components of the wind turbine generator.</p><p>Verma and Kusiak (2012) used four data-mining algorithms in order to predict wind</p><p>turbine generator brushes faults using SCADA data, having the best result with Boosting-tree</p><p>algorithm. Kusiak and Verma (2012) proposed a neural network normal behaviour model</p><p>approach that uses SCADA data to identify generator bearing faults in wind turbines.</p><p>In order to focus on the specific problem of detecting islanding faults in doubly-fed</p><p>induction generators (DFIG) of wind turbines, Abd-Elkader, Allam and Tageldim (2014) used</p><p>simulation to propose a method that first uses Fourier transform to process voltage and current</p><p>measurements and then fit it into a neural network that is responsible for identifying quality</p><p>disturbances or actual islanding operation.</p><p>Adouni et al. (2016) present a fault detection and identification method for low-</p><p>voltage-ride-through faults in wind turbine DFIG generators, they made an experiment for</p><p>simulating voltage amplitudes and phases with a test rig, and applied it in an artificial neural</p><p>network capable of detecting and diagnose this specific fault.</p><p>In order to detect faults in generator bearings, Ali et al. (2018) presents an online</p><p>vibration-based fault diagnosis method using vibration history data from real wind turbines</p><p>generators, they did time-domain and frequency domain feature extraction and applied it to an</p><p>adaptive resonance theory</p><p>two neural network in order to classify it into healthy state, degraded</p><p>state and failure state, in an unsupervised learning approach.</p><p>Moreover, the study of Amina, Tayeb and Mouloud (2016) proposed fast fuzzy-</p><p>logic based open switch fault detection for the rotor side converter of a doubly fed induction</p><p>generator (DFIG) wind turbine system using simulated three phase rotor currents data.</p><p>47</p><p>4.2.1.4 Pitch System</p><p>Two studies focused on the development of condition monitoring applications for</p><p>the pitch system. Chen, Matthews and Tavner (2013) present a supervised learning fault</p><p>prognosis procedure for wind turbine pitch system using SCADA data of six known wind</p><p>turbine pitch faults to train an Adaptive Neuro-Fuzzy Inference System, after that, they tested</p><p>it in other wind farm and compared it to the general SCADA alarm approach. Talebi, Sadrnia</p><p>and Darabi (2014) utilize a dynamic model for simulating mechanical and electric components</p><p>of wind turbines and suggest a fault detection system that detect faults on the generator angular</p><p>velocity sensor, pitch angle sensor and pitch angle actuators, using Recurrent Neural Networks</p><p>(RNN).</p><p>4.2.1.5 Sensors</p><p>Three studies focused on the development of applications of fault detection in wind</p><p>turbine sensors. Talebi, Sadrnia and Darabi (2014) was already mentioned in the last session.</p><p>Kavaz and Barutcu (2018) developed a technique for detection of calibration drifts in</p><p>temperature sensors of wind turbines using SCADA data, they had best results with auto-</p><p>associative backpropagation neural networks for distinguishing faulty situations and multi-</p><p>input single output backpropagation neural network to isolate the faulty sensor. Bakri, Koumir</p><p>and Boumhidi (2019) propose a fault detection and isolation scheme for rotation speed sensors</p><p>and generator output using extreme learning machine, they used simulation in order to test the</p><p>effectiveness of the proposed approach.</p><p>4.2.1.6 Whole Wind Turbine</p><p>Twelve papers focused on the study of artificial intelligence applications for the</p><p>whole wind turbine, having different approaches. Six studies aimed on the development of</p><p>condition monitoring applications for the wind turbine. Kusiak and Verma (2011) applied</p><p>association rule mining algorithm to identify the most frequent status patterns in wind turbines</p><p>using SCADA data from 100 wind turbines, then they used PCA for dimensionality reduction</p><p>and data-mining algorithms in order to predict the status patterns of the wind turbines, having</p><p>the best result with Random Forest Algorithm.</p><p>Choi and Kim (2017) used SCADA data to investigate the operating behaviour of</p><p>wind turbines by clustering data through Gustafsson-Kessel Fuzzy Clustering Method,</p><p>48</p><p>identifying the actual operating point of the wind turbine according to the distance in relation</p><p>to the cluster centres.</p><p>Lind et al. (2017) compared a stochastic approach with a neural network model in</p><p>the task of reconstructing the offshore wind turbine tower top acceleration signal, showing that</p><p>the stochastic approach outperformed the neural network approach. They also provided the</p><p>open-source code for the application.</p><p>Yang et al. (2018) proposes a method of early fault detection in wind turbines that</p><p>applies data-mining techniques in SCADA data to select the most important variables and then</p><p>build a model-based EWMA control chart and Multivariate EWMA in order evaluate their fault</p><p>detection ability.</p><p>Huang et al. (2018) presents a global condition monitoring method based on copula</p><p>function and autoregressive neural networks, they first used the copula function to build a joint-</p><p>probability density function of both power and wind speed with SCADA data, then established</p><p>a fault-free condition monitoring model with the autoregressive neural network, the fault is</p><p>identified with a statistical analysis of the model’s predictions.</p><p>Sun and Sun (2018) introduce a hybrid method which combines the analysis of</p><p>variance of SCADA parameters using RNN to evaluate the health status of wind turbines, they</p><p>separate the data in several different categories according to its variance ranges and then LSTM</p><p>model is trained in each category. The models are then compared with the actual parameter</p><p>values and after that, they adopt a weighted assessment method to define the health status of</p><p>the wind turbine.</p><p>As can be seem, most of these studies used model-based approaches in the task of</p><p>predicting the health condition of wind turbines, only Choi and Kim (2017) used a clustering</p><p>approach in the task of identifying the operational point of wind turbines.</p><p>Four studies focused on building applications that aimed at improving the</p><p>maintenance strategies of wind farms in a more holistic way, not only in condition monitoring</p><p>but also giving an overview of where and when the maintenance activity should be carried out.</p><p>Hammed and Wang (2012) proposed a method to optimize the maintenance</p><p>strategies of offshore wind farms using SOM neural networks to separate wind turbines into</p><p>clusters and then predict the expected power output per cluster using Backpropagation neural</p><p>networks, the maintenance activities can then be designed for the whole cluster instead for a</p><p>single wind turbine.</p><p>Yang, Huang and Huan (2016) used wind turbine SCADA data to present a three-</p><p>phase method to detect emerging faults in wind turbines that integrates power-curve residual</p><p>49</p><p>control charts with auto-associative neural networks to detect anomalous components by the</p><p>comparison of mean squared error of the components, after that a schedule chart is obtained</p><p>consisting in anomalous schedule, preventive schedule and ideal schedule.</p><p>The only study that brought results associated with maintenance cost reduction was</p><p>the one proposed by Lu et al. (2018). They studied a condition-based maintenance method that</p><p>uses neural networks to evaluate the major components life percentage and a conditional failure</p><p>probability of the component that represents the deterioration of the Wind Turbine, the</p><p>maintenance method is defined as an optimized two-level threshold failure probability to find</p><p>when the optimal condition based maintenance actions will be carried out, they use failure</p><p>information and maintenance costs of offshore wind turbines to illustrate the effectiveness of</p><p>the approach and then compare it to time-based maintenance information from other studies.</p><p>In order to improve management of power companies, Yeh et al. (2019) proposes a</p><p>machine learning-based method that predicts long cycle maintenance time of wind turbines.</p><p>Operation, maintenance and event codes were collected from wind turbines and then a hybrid</p><p>network model is built composed by convolutional neural networks (CNN) and support vector</p><p>machines (SVM).</p><p>Two studies attained at studying maintenance indicators of wind farms. Astolfi et</p><p>al. (2015) applied post-processing data mining methods on SCADA data of onshore wind farms</p><p>in order to formulate innovative indicators of goodness of performance, then they create a</p><p>malfunctioning index, detail malfunctioning index, stationary index and misalignment index</p><p>for the wind turbines. Su and Hu (2017) applied data mining techniques in a wind farm Scada</p><p>data to analyse its reliability characteristics. They study reliability indexes to determine the</p><p>components that influence the most on the wind turbines reliability, comparing the wind farm</p><p>indicators with the ones in EU and analyse the seasonality of the failure rate and correlation</p><p>with temperature.</p><p>Figure 4.5 shows the main applications for each equipment that were found in the</p><p>studies.</p><p>50</p><p>Figure 4.5: Main applications found in the studies by equipment.</p><p>Source: Adapted from Turbosquid (2020).</p><p>4.2.2 Scada vs vibration data for condition monitoring</p><p>Most of the applications</p><p>found for condition monitoring in this research has used</p><p>SCADA or vibration data. SCADA is low frequency data, generally recorded every 10 seconds</p><p>and averaged over 10 minutes, which means poor performance since it brings difficulties related</p><p>to the loss of noise characteristics and, on top of that, sometimes there are missing values and</p><p>imperfections in the data collected by the system. On the other hand, wind turbines are mostly</p><p>equipped with the SCADA system, being a built-in part of every large-scale wind turbine, which</p><p>means no additional cost for data acquisition (Kusiak, 2016; Kavaz, Barutcu, 2018).</p><p>SCADA data often appears in building normal behaviour models for early fault</p><p>detection; however, its disadvantages make it necessary to spend big effort in the pre-processing</p><p>phase of the study (BANGALORE, 2017). Some SCADA pre-processing procedure that</p><p>appears in the studies are outline removal, data scaling (SCHLEICHTINGEN, SANTOS;</p><p>2011), Wrapper Method for parameter selection (KUSIAK, VERMA, 2012; YANG et al.,</p><p>2018) and Principal Component Analysis (KUSIAK, VERMA, 2011). Nevertheless, it is the</p><p>most common data used when it comes to evaluate studies on real operating wind turbine data.</p><p>Even though SCADA data is more accessible, vibration data is still widely used,</p><p>especially when aiming at going a step further than just fault detection, targeting fault diagnosis.</p><p>However, there is difficulties associated with this kind of studies. Fault diagnosis generally</p><p>51</p><p>needs ground truth labelled data, containing the operational parameters of the machine to serve</p><p>as training data for supervised learning approaches, which is not easy to obtain from operating</p><p>large-scale wind turbines, hence, it is usually taken from test rigs or simulations where</p><p>researchers can induce faulty components without jeopardizing the operation. It is a problem</p><p>for testing the availability of these studies in operating wind turbines. In order to deal with this</p><p>issue, one possible approach can be the one presented in Bach-Andersen, Romer-Odgaard and</p><p>Winther (2018) who used vibration data from gearbox of onshore and off-shore wind turbines</p><p>and created a supervised dataset through the opinion of specialists. With this dataset they built</p><p>a model for fault diagnosis using convolutional neural networks. Furthermore, some vibration</p><p>pre-processing methods are cited in the studies such as Fourier Transform (YU, HUANG,</p><p>XIAO, 2018), Hilber-Huang Transform (ZHONG et al, 2018) and Wavelet Transform (WU et</p><p>al., 2017; JIMÉNEZ, MUÑOZ, MÁRQUEZ, 2018; JIMÉNEZ et al., 2019). The Figures 4.6</p><p>and 4.7 show the distribution of articles that used SCADA and vibration for condition</p><p>monitoring, as well as their final objective.</p><p>Figure 4.6: Distribution of articles that used SCADA or vibration data for condition monitoring.</p><p>Source: Author, 2020.</p><p>Figure 4.7: Distribution of articles that used Vibration or SCADA data for condition monitoring according to</p><p>their main goal.</p><p>Source: Author, 2020.</p><p>16</p><p>4</p><p>6</p><p>SCADA Vibration</p><p>Real WT Other sources</p><p>52</p><p>4.2.3 Evaluation metrics</p><p>There is a variety of evaluation metrics to present the results of the studies, what</p><p>can make it difficult for comparing their results and seek the best performance between them.</p><p>The metrics were divided into three groups and they are going to be presented in the following</p><p>sections.</p><p>4.2.3.1 Algorithm performance metrics</p><p>Algorithm performance metrics are used in order to evaluate the performance of the</p><p>method in classification or regression tasks, especially in the model selection phase of the study.</p><p>Some authors use these metrics as the final result of the research. Usually, classification models</p><p>use metrics that evaluate the performance of the algorithm based on the number of True</p><p>Positives (TP - Number of samples classified as positive that are actually positive), True</p><p>Negatives (TN - Number of samples classified as negative that are actually negative), False</p><p>Positives (FP - Number of samples classified as positives that are actually negative) and False</p><p>Negatives (FN - Number of samples classified as negatives that were actually positives). On</p><p>the other hand, regression models use metrics related to the error between the real and modelled</p><p>parameter. Some of these metrics are presented in the Table 4.1 (STETCO et al., 2019):</p><p>Table 4.1: Some classification and regression metrics found in the studies.</p><p>Classification Regression</p><p>Accuracy</p><p>𝑇𝑃 + 𝑇𝑁</p><p>𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁</p><p>Mean squared error (MSE)</p><p>1</p><p>𝑁</p><p>∗ (𝑌 (𝑖) − 𝑌 (𝑖))²</p><p>Precision</p><p>𝑇𝑃</p><p>𝑇𝑃 + 𝐹𝑃</p><p>Root mean squared error</p><p>(RMSE)</p><p>1</p><p>𝑁</p><p>∗ (𝑌 (𝑖) − 𝑌 (𝑖))²</p><p>Recall</p><p>𝑇𝑃</p><p>𝑇𝑃 + 𝐹𝑁</p><p>Mean absolute percentage error</p><p>1</p><p>𝑁</p><p>∗</p><p>𝑌 (𝑖) − 𝑌 (𝑖)</p><p>𝑌 (𝑖)</p><p>F1 score</p><p>2 ∗ 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙</p><p>𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙</p><p>Mean absolute error</p><p>1</p><p>𝑁</p><p>∗ |𝑌 (𝑖) − 𝑌 (𝑖)|</p><p>Source: Author, 2020.</p><p>53</p><p>4.2.3.2 Time and cost-based metrics</p><p>Some studies go a step further than evaluating the algorithm and, after some post-</p><p>processing and analysis, present the results in terms of time and cost. Time-based metrics</p><p>appears in this research as the ones that present the results in terms of early warning before</p><p>abnormal operation happens or even the time for fault detection. They normally appear in</p><p>studies that use the normal behaviour model approaches allied with the residual control charts</p><p>that aims at detecting abnormal behaviour or faults, sometimes comparing it to the actual</p><p>SCADA alarm system. It seems that this metric demonstrates how the implementation of the</p><p>proposed approach can impact in the maintenance planning activity directly.</p><p>Moreover, only Lu et al. (2018) present a cost-based metric, bringing the idea of</p><p>monetary savings in the application of their approach. Since only one study brings this idea, it</p><p>can be seen as a gap in the research. Future research should focus case studies of the application</p><p>of artificial intelligence in real wind turbine operations and present results in terms of savings.</p><p>It might enhance the appreciation of the wind turbine industry to this area, making it more open</p><p>to share data resources because it brings direct attention for improvements in the</p><p>competitiveness of the maintenance activities.</p><p>54</p><p>CHAPTER 5: CONCEPTUAL FRAMEWORK FOR APPLICATION OF</p><p>ARTIFICIAL INTELLIGENCE IN WIND TURBINE OPERATION AND</p><p>MAINTENANCE</p><p>When studying these applications, there are some factors that can influence in the</p><p>research success, hence, this paper suggests a conceptual framework with a pathway for</p><p>researchers and developers for implementing artificial intelligence applications for wind turbine</p><p>maintenance by analysing the steps taken in the papers. The framework is divided in 10 steps,</p><p>and the implementation of the studies can follow different pathways depending on the tasks</p><p>addressed to the AI methods. Figure 5.1 shows the proposed framework while the Table 5.1</p><p>shows the relation of papers that mention each step.</p><p>Figure 5.1: Conceptual framework with the main factors to consider when building Artificial Intelligence</p><p>Applications for wind turbine operations and maintenance.</p><p>Source: Author, 2020.</p><p>55</p><p>Table 5.1: Steps mentioned by the articles selected in for literature reviewing.</p><p>Authors GD DP PS/FE MSV FD RMP PP C</p><p>Elhor et al. (1999) X X X X X</p><p>Schlechtingen and Santos (2011) X X X X X X X</p><p>Kusiak and Verma (2011) X X X X X</p><p>Hameed and Wang (2012) X X X X</p><p>Verma and Kusiak (2012) X X X X X</p><p>Kusiak and Verma (2012) X X X X</p><p>Chen et al. (2013) X X X X X X X</p><p>Wang et al. (2013) X X X</p><p>Tang et al. (2014) X X X X</p><p>Abd-Elkader et al. (2014) X X</p><p>Talebi et al. (2014) X X X X</p><p>Ren and Qu (2014)</p><p>Astolfi et al. (2015) X X X</p><p>Yang et al. (2016a) X X X X X</p><p>X</p><p>Strdczkiewicz and Barszcz (2016) X X X X X X</p><p>Amina et al. (2016) X X X</p><p>Adouni et al. (2016) X X X X</p><p>Yang et al. (2016) X X X X X</p><p>Choi and Kim (2017) X X X X X</p><p>Bangalore et al. (2017) X X X X X X X</p><p>Lind et al. (2017) X X X X</p><p>Gantasala et al. (2017) X X X X</p><p>Wu et al. (2017) X X X X X</p><p>Zhao et al. (2018) X X X X X</p><p>Lu et al. (2018) X X X X</p><p>Su and Hu (2017) X X</p><p>Bach-Andersen et al. (2018) X X X X</p><p>Yang et al. (2018) X X X X X</p><p>Ali et al. (2018) X X X X X</p><p>Jiménez et al. (2018) X X X X X</p><p>Guo et al. (2018) X X X X X</p><p>Huang et al. (2018) X X X X</p><p>Kavaz and Barutcu (2018) X X X X X</p><p>Sun and Sun (2018) X X X X X X</p><p>Yu et al. (2018) X X X X X</p><p>Zhong et al. (2018) X X X X X X</p><p>Fu et al. (2019) X X X X</p><p>Jiménez et al. (2019) X X X X X</p><p>Chen et al. (2019) X X X X X</p><p>Shihavuddin et al. (2019) X X X X</p><p>Yeh et al. (2019) X X X</p><p>Bakri et al. (2019) X X X X</p><p>Yang et al. (2019) X X X X X</p><p>Li et al. (2019) X X X X X</p><p>Liu et al. (2019) X</p><p>Source: Author, 2020.</p><p>The acronyms stand for GT: Getting data, DP: Data pre-processing, PS/FE:</p><p>Parameter selection and feature extraction, MSV: Model selection and evaluation, FD: Fit best</p><p>56</p><p>model with new data, RMP: Retrain model periodically, PP: Post-processing, C: Comparison</p><p>with other studies.</p><p>5.1 Getting data</p><p>Artificial intelligence algorithms need great amount of data to work properly,</p><p>sometimes it is necessary to train models with long term parameters in order to extract natural</p><p>operating characteristics of the turbine (STRDCZKIEWICZ, BARSZCZ, 2016). Therefore,</p><p>when building applications for wind turbine O&M, one must check data availability before</p><p>defining the study scope.</p><p>According to Kusiak (2016), wind industry is still very closed when it comes to data</p><p>availability since companies usually hold most of the data acquired in the wind turbines</p><p>operation. SCADA data is a good option because it does not require additional cost for data</p><p>acquisition since almost all wind turbines are equipped with this system. Vibration data is more</p><p>difficult to be acquired from operating large-scale wind turbines.</p><p>According to Strdczkiewicz and Barszcz (2016), it is difficult to access raw</p><p>vibration data, therefore, researchers sometimes might go for test rigs or simulations to have</p><p>enough data for their studies. Other kinds of data that appears in this research are drone pictures,</p><p>sound and guided waves for blade applications and current for the electric system.</p><p>5.2 Define how AI can help in the approach</p><p>Artificial intelligence methods can be useful in wind farm O&M approaches</p><p>through 4 main tasks: regression, for building normal behaviour models and remaining useful</p><p>life prediction; classification for faults and abnormal behaviour detection; clustering for</p><p>grouping up wind turbine behaviour patterns; and data-mining for extract useful information</p><p>and create indicators from data.</p><p>These tasks can be combined within the same approach, being used strategically for</p><p>achieving the goal of the study. For example, Hammed and Wang (2012) used clustering for</p><p>separating wind turbines into clusters of performance, and regression for model the power</p><p>output of the wind turbines, comparing the power output of the wind turbines in the same cluster</p><p>in order to optimize the wind farm maintenance strategy.</p><p>57</p><p>5.3 Data pre-processing</p><p>Data pre-processing is an essential step in this workflow. Machine learning models</p><p>learn based on the data that they are exposed to in the training stage, so it is important that the</p><p>data are error free which is difficult to find in the real world. For example, SCADA data usually</p><p>are discontinuous and containing inconsistencies, which lead to inaccuracies in Neural</p><p>Networks models (BANGALORE et al., 2017). According to Schlechtingen and Santos (2011),</p><p>data needs to be pre-processed in order to apply neural networks because even though they are</p><p>able to handle fuzzy or incomplete data, they are sensitive to invalid data, which could bring its</p><p>generalisation performance down.</p><p>The most common data pre-processing steps are data scaling, and outlier removal.</p><p>Classification tasks often require ways to deal with unbalanced data classes because is difficult</p><p>to find operational data of unhealth operating wind turbines. In order to do so, they adopt steps</p><p>such as oversampling to increase the amount of data labelled as faulty.</p><p>Machine learning models can also be used in this step. For example, Bangalore et</p><p>al. (2017) used the cluster filter method proposed in Kusiak and Verma (2013) to remove data</p><p>that do not belong to the overall normal behaviour of wind turbines.</p><p>5.4 Parameter selection and Feature extraction</p><p>Parameter selection (or feature selection) is important to build AI applications.</p><p>Scada data usually gets more than 100 parameters of a wind turbine and, for accurate</p><p>predictions, the dimensionality of the dataset needs to be reduced because not all parameters of</p><p>the dataset are significant for the desired task, hence, selecting the most important among them</p><p>improves the performance of the model as well as reduces the computational cost (KUSIAK</p><p>VERMA, 2011). Data-mining algorithms such as boosting tree, PCA and wrapper approach are</p><p>widely used for parameter selection.</p><p>However, according to Bangalore et al. (2017) using data mining approaches for</p><p>this task may lead to selection of a large number of input parameters, thus, physical and domain</p><p>knowledge can be applied in order to keep this number in a reasonable amount.</p><p>When it comes to feature extraction, Mörchen (2003) states that it should be applied</p><p>in time series in order to compress it, keeping only important information. Therefore, cleaning</p><p>noise and removing correlations, usually improves the performance of data-mining algorithms.</p><p>In this research, feature extraction techniques appear often in studies that use vibration data.</p><p>According to Stetco (2019), an interesting approach is the auto-encoders, which are deep NN</p><p>that converts input data in a low dimension representation and a decoder that transform it to</p><p>58</p><p>proper outputs. An example is the study of Yang et al. (2014) that compared some feature</p><p>extraction approaches in vibration signals in order to achieve better accuracy for single and</p><p>multi-fault detection in the gearbox.</p><p>5.3 Model selection and validation</p><p>Machine learning techniques encompasses various methods that can be set in</p><p>different ways. In order to find the best configuration, one has to fit the models with test and</p><p>validation datasets, evaluate the best performance in the validation dataset, and then select the</p><p>configuration that reduces the error function the most to generalize better to unseen data.</p><p>Neural networks can be set in different types, architectures and hyperparameters. In</p><p>order to find the best Neural Network configuration, it is recommended to perform at least 10</p><p>runs changing the network architecture, choosing the one that gives the best network</p><p>generalization. It reduces the risk of finding solutions that do not generalize properly</p><p>(SCHLECHTINGEN, SANTOS, 2011; TARASSENKO, 1998). Therefore, setting the best</p><p>network configuration is a trial-and-error process that is essential to find the one that delivers</p><p>the best performance.</p><p>It is also important to test different methods. Jiménez et al. (2018) compared</p><p>different classifiers and compare the best performance among them. It is also adopted in Wu et</p><p>al. (2017), Bach-Andersen et al. (2018), Yu et al. (2018), Zhong et al. (2018), Jimenez et al.</p><p>(2019), Chen et al. (2019), Shihavuddin et al. (2019) and Bakri et al. (2019). The K-fold cross</p><p>validation process is used in some studies to iteratively evaluate the algorithm</p><p>with K different</p><p>partition of training and validation datasets (JIMÉNEZ et al., 2019; KUSIAK, VERMA, 2012;</p><p>BERMEJO et al., 2019).</p><p>5.4 Fit best model to unseen data</p><p>Once the best model is selected it is time to fit it to unseen data, and evaluate the</p><p>results. Shihavuddin et al. (2019) states that it is important to save a reasonable number of</p><p>unseen samples to evaluate the performance of the model in the test phase. In normal behaviour</p><p>models, the predictions of the unseen data are confronted with the real outputs in the figure of</p><p>the residual error, which is going to be useful in building residual control charts. The residual</p><p>error is given by the Equation 1, where 𝑌 represents measured data and 𝑌 represents modelled</p><p>data of the i-th sample.</p><p>59</p><p>𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑒𝑟𝑟𝑜𝑟 (𝑖) = 𝑌 (𝑖) − 𝑌 (𝑖) (5.1)</p><p>5.5 Retrain Model periodically</p><p>As the wind turbines are exposed to harsh conditions and endure for long periods,</p><p>it is expected that it degrades with time, and its normal operating conditions as well. Thus, in</p><p>order to maintain the continuous monitoring of the wind turbine components, models might</p><p>have to be retrained as the time goes by. Stetco et al. (2019) states that the performance of the</p><p>models can degrade due to failing hardware responsible for feed data to the models, thus,</p><p>retraining models may be an option. They also say that classification models might not need</p><p>such an effort since they can be set to learn online with new data.</p><p>On the other hand, retraining regression models built with normal behaviour wind</p><p>turbine data may be troublesome because new data may pursue different behaviour from the</p><p>dataset which the model was once trained with. Schleichtingen and Santos (2011) used data</p><p>from 3 months after bearings replacement as training data for modelling the bearing</p><p>temperature. In generalizing this approach for every normal behaviour model, one can conclude</p><p>that they could be retrained every time the component that is being monitored is replaced so the</p><p>model would acquire the normal behaviour data for the new component.</p><p>When it comes to clustering, Choi and Kim (2017), state that operating behaviour</p><p>of wind turbines change along the period of operation, in this sense clusters centres can also</p><p>change, so they should be updated regularly (monthly or weekly) by training the model with</p><p>new data. The change in clusters centres can also be a measure of how the wind turbine degrades</p><p>with time.</p><p>5.6 Post Processing</p><p>In order to present a complete approach, further analysis can be implemented as</p><p>post-processing methods to provide decision-making potential. Astolfi et al. (2015) states that,</p><p>the more detailed the scale of the phenomenon to analyse, the more complex and smart the post</p><p>processing data should be. One example of applications that usually requires post-processing</p><p>are the normal behaviour models. According to Lind et al. (2017) and Bangalore et al. (2017)</p><p>the normal behaviour models for condition monitoring of wind turbines can be upgraded by</p><p>both pre-processing and post-processing, improving their training and reducing false alarms in</p><p>order to provide a good tool for decision making. Some example of post-processing adopted by</p><p>the papers are anomaly detection charts (SCHLECHTINGEN, SANTOS, 2011; TALEBI et al.,</p><p>60</p><p>2014; BANGALORE et al., 2017; ZHAO et al. 2018; YANG et al., 2018; HUANG et al., 2018),</p><p>study of indicators (ASTOLFI et al., 2015), maintenance schedule charts (YANG et al., 2016a)</p><p>and cost analysis (LU et al., 2018).</p><p>5.7 Comparison with other studies</p><p>Some papers show a comparison between methods in their results, analysing the</p><p>performance of different approaches, AI algorithms, and even other studies with the same goal.</p><p>It seems like a good practice to prove the performance of the proposed approach against other</p><p>studies, establishing benchmarking. This step is taken by Chen et al. (2013), Bangalore et al.</p><p>(2017), Lu et al. (2018), Ali et al. (2018) and Bakri et al. (2019).</p><p>61</p><p>CHAPTER 6: CONCEPTUAL FRAMEWORK APPLICATION AND LEARNING</p><p>LESSONS</p><p>This chapter describes the field research stage. It shows the process of selecting the</p><p>application to be simulated; the structuring of the dataset for modeling; description of the</p><p>method and simulation. The field research step-by-step follows the flowchart in Figure 2.1.</p><p>6.1 Selection of application</p><p>The application was selected through an interview with a specialist in the field of</p><p>operation and maintenance of wind farms who works in Brazil. Topics related to the current</p><p>status of condition monitoring systems in wind farms, data availability, most critical equipment</p><p>and bottlenecks were addressed, always taking into account how new condition monitoring</p><p>tools that may contribute to operations and maintenance activities. The questions discussed in</p><p>the interview are available in Appendix 1.</p><p>Regarding the current status of condition monitoring systems in wind farms, this</p><p>step showed that there are systems that monitor the performance of the wind turbine as a whole,</p><p>identifying which ones may be faulty according to their current and historical performance. In</p><p>addition, the SCADA system monitors sensitive parameters of the equipment, stopping the</p><p>operation if any of them exceeds limits defined by the manufacturer through an alarm system.</p><p>The SCADA system creates a database with the measurement of parameters and the moments</p><p>of alarms. The creation of a database with the history of this equipment may allow the</p><p>development of AI applications for monitoring purposes, if the data is available.</p><p>As for the criticality of the equipment, it was considered by the specialist that the</p><p>transmission system is the most critical among all, both for its monetary value and for the</p><p>frequency in which interventions are needed. The SCADA system does the job of general</p><p>monitoring, stopping the operation in case of occurrence of parameters outside the limits</p><p>specified through the alarm system. However, more specific components are still subject to</p><p>unforeseen failures by SCADA, especially the bearings, which have frequent failures. In other</p><p>words, artificial intelligence applications developed for the monitoring of bearing failure can</p><p>be of great value when planning the maintenance of this wind farm in Brazil, and for this reason,</p><p>it is simulated by this research.</p><p>Thus, considering that condition monitoring applications for wind turbine bearings</p><p>would be welcomed for the Brazilian wind farm under study and that SCADA data would be</p><p>62</p><p>available for simulation, we crossed this information with the studies analyzed in the systematic</p><p>literature review, coming up with 4 studies that had the same characteristics. The results of</p><p>these studies are presented in the Table 6.1.</p><p>Table 6.1: Some quantitative results of studies that used SCADA data to build applications of condition</p><p>monitoring of wind turbine bearings.</p><p>Source: Author, 2020.</p><p>6.2 Application of the conceptual framework</p><p>In this section the application of the framework is presented with s simulation on a</p><p>wind turbine from the wind farm in order to test a condition monitoring application for wind</p><p>turbine bearing maintenance. Four turbines presented failure in the front generator bearing, as</p><p>presented in the Table 6.2.</p><p>Table 6.2:2Front generator bearing faults that happened in the wind farm.</p><p>Turbine Operation start Day of bearing replacement</p><p>Period</p><p>T16 14/03/2013 26/05/2017</p><p>1534</p><p>T46 14/03/2013 01/06/2017</p><p>1540</p><p>T23 24/01/2012 12/07/2018</p><p>2361</p><p>T59 16/07/2013 11/05/2019</p><p>2125</p><p>Source: Author, 2020.</p><p>The wind turbine that presented the earliest bearing fault in terms of time of</p><p>operation is selected is the T16, and due to that, it is going to be used as object of simulation</p><p>for this research.</p><p>Author Goal Metric Results</p><p>Schlechtingen</p><p>and Santos (2011)</p><p>Normal behaviour models</p><p>for failure detections in</p><p>generator and gearbox</p><p>bearings and generator</p><p>Early fault detection</p><p>Gearbox bearing:186 days,</p><p>Generator stator: 70 days,</p><p>Generator bearing: 25 days.</p><p>Kusiak and Verma (2012)</p><p>Fault prediction in wind</p><p>turbine generator bearings</p><p>Early fault detection 1.5 hours</p><p>Early fault detection Gearbox bearing: 3 months</p><p>Root mean squared error Generator bearing: 0.77</p><p>Fu et al (2019)</p><p>Condition monitoring of</p><p>wind turbine generator</p><p>bearing</p><p>Root mean squared error 0.78-2.02</p><p>Fault detection in gearboxes</p><p>through monitoring its</p><p>bearing and oil temperature</p><p>Bangalore et al. (2017)</p><p>63</p><p>6.2.1 Getting data</p><p>In the development of the artificial intelligence application, it is first necessary to</p><p>check the availability of the dataset. The SCADA data of the wind turbines was available for</p><p>this study. It is an industrial control system that monitor assets geographically dispersed, and</p><p>have centralized data acquisition and storage. In the case of wind farms, they allow operators</p><p>to control and store parameters for wind turbines, electrical substations and anemometric towers</p><p>from remote sensing, storing data related to environmental variables, state variables and</p><p>equipment parameters (PEDROSA, 2016). These systems became common in modern wind</p><p>turbine technology due to their adequate cost, simplicity of implementation and interpretation,</p><p>and versatility (MANA; PICCIONI; TERZI, 2017). Figure 6.1 shows a schematic of the</p><p>SCADA systems architecture in wind farms.</p><p>Figure 6.1: SCADA system architecture in wind farms.</p><p>Source: Pedrosa (2016).</p><p>Therefore, as SCADA data of the wind farm is available for the study, it is going to</p><p>be used for simulation of artificial intelligence methods in the task of building normal behaviour</p><p>models for bearings.</p><p>6.2.2 Define how AI can help in the approach</p><p>For the purpose of building normal behaviour models for wind turbine bearings</p><p>condition monitoring using SCADA data, artificial intelligence methods are useful in the task</p><p>of regression or classification. As there is not ground truth labelled data specifying weather the</p><p>bearing is in normal or abnormal conditions, it is imperative to build normal behaviour models</p><p>based on regression. The 4 approaches mentioned in the Table 6.1 use artificial intelligence</p><p>64</p><p>methods to create normal behaviour models for monitoring the temperature of the bearings and</p><p>this research follows the same line.</p><p>6.2.3 Parameter selection</p><p>In order to build a normal behaviour model for bearing monitoring, the parameters</p><p>selected in Schleichtingen and Santos (2011) were used. In their article, they used physical</p><p>domain knowledge to select them, which means no additional use of statistical or data-driven</p><p>methods in this stage. They also had the best results in terms of early fault detection among the</p><p>studies that were analysed.</p><p>A simple network configuration is required with parameters in the input, hidden</p><p>layers and an output layer, which facilitates networks constructions, analysis and training. Chart</p><p>6.1 shows the parameters used as input signals for the gearbox and generator bearing</p><p>temperatures modelling while Figure 6.2 shows the location of the monitored parameters.</p><p>Chart 6.1: Input data for building the normal behaviour model proposed by Schlechtingen and Santos (2011).</p><p>Modelled parameter Input Signals</p><p>Generator Bearing Temperature</p><p>Nacelle Temperature (1)</p><p>High-speed shaft velocity (4)</p><p>Winding temperature (6)</p><p>Power output (7)</p><p>Gearbox Bearing Temperature</p><p>Nacelle temperature (1)</p><p>Oil sump temperature (2)</p><p>High-speed shaft velocity (4)</p><p>Power output (7)</p><p>Ambient temperature (8)</p><p>Source: Adapted from Schlechtingen and Santos (2011).</p><p>Figure 6.2: Sensor location of the SCADA monitoring parameters selected by Schleichtingen and Santos (2011)</p><p>for building the normal behaviour models.</p><p>Source: (SCHLECHTINGEN; SANTOS, 2011).</p><p>65</p><p>Where:</p><p>1. Nacelle Temperature in °C;</p><p>2. Gearbox oil chunk temperature in °C;</p><p>3. Gearbox bearing temperature in °C;</p><p>4. High velocity shaft rotation in RPM;</p><p>5. Generator bearing temperature in °C;</p><p>6. Generator stator temperature in °C;</p><p>7. Power output in KW;</p><p>8. Ambient temperature in °C.</p><p>As only the generator bearing had faults in this period, the simulation phase of this</p><p>study will leave the gearbox bearing model aside, so the fault prediction ability of the method</p><p>can be evaluated.</p><p>The heatmap of the correlation between parameters the generator dataset is</p><p>presented in the Figure 6.3. From the figure, the bearing temperature is highly correlated with</p><p>active power and winding temperature, and less correlated with shaft rotation and nacelle</p><p>temperature.</p><p>Figure 6.3: Generator bearing data correlation for the wind turbine.</p><p>Source: Author, 2020.</p><p>66</p><p>According to Schleichtingen and Santos (2011) the parameters with less correlation</p><p>are used for stabilization of the network. Both configurations with and without the less</p><p>correlated parameters were tested in this research, with the inclusion of them showing better</p><p>results.</p><p>6.2.4 Data Pre-processing</p><p>In the signal pre-processing phase, the input data is treated so that the database is</p><p>adequate to serve for neural networks. Schleichtingen and Santos (2011) presents the following</p><p>sequence for data treatment and standardization:</p><p> Data validity check: the data used in the model must be checked, filtering</p><p>outliers and unexpected variations.</p><p> Data normalization: Equation 1 is used for data normalization, where S is</p><p>the normalized variable and V is the actual variable.</p><p>𝑆 =</p><p>𝑉 − 𝑉𝑚𝑖𝑛</p><p>𝑉𝑚𝑎𝑥 − 𝑉𝑚𝑖𝑛</p><p>(6.1)</p><p> Missing data processing: rows with missing data are deleted from the</p><p>dataset.</p><p> Lag removal: the cross-correction function is used to identify delays in the</p><p>linear dependency between functions in order to build a database consistent</p><p>in terms of input-output.</p><p>At the beginning the dataset contains 321866 data points and, after pre-processing,</p><p>it became a 259365 data-points dataset. Therefore, the pre-processing phase took around 20%</p><p>of the dataset which is quite extensive.</p><p>6.2.4 Model Selection</p><p>In selecting the method that best performs in modelling the generator bearing</p><p>temperature it is first necessary to determine the separation for training, validation and testing</p><p>datasets in order to guarantee that the training phase encompasses the normal operation bearing</p><p>data and have plenty of datapoints. Second, the network types, structures and hyperparameters</p><p>are chosen. At the end, the models are trained and evaluated with the validation dataset.</p><p>67</p><p>6.2.4.1 Separation in training, validation and testing datasets</p><p>In separating the datasets for training, Schleichtingen and Santos (2011) suggest</p><p>that 3 months of operational data should be used because, as the time goes by, the normal</p><p>operation of the machine deteriorates and then the parameters taken from the SCADA data may</p><p>not be useful to build a normal behaviour model.</p><p>In order to identify the most suitable period to serve as training, seasonality analysis</p><p>with the most correlated parameters with the generator bearing temperature were performed.</p><p>The figures 6.4 and 6.5 show the relation between the means of active power and winding</p><p>temperature with the generation bearing temperature mean grouped by month, respectively. The</p><p>figures show how the means of the parameters varied with months, showing seasonality effect.</p><p>Figure 6.4: Seasonality analysis between active power and front generator bearing temperature.</p><p>Source: Author, 2020.</p><p>68</p><p>Figure 6.5: Seasonality analysis between winding temperature and front generation bearing temperature.</p><p>Source: Author, 2020.</p><p>This seasonality effect may affect the training period, as it should have the whole</p><p>operational regime in order to generalize well for all operational conditions. Hence, in this</p><p>research, the first year of operation is taken as training data while the following year is taken as</p><p>validation data. The Figure 6.6 and 6.7 show the periods of training and validation data before</p><p>and after pre-processing, it also shows when the failure occurred with the hashed line in black.</p><p>Figure 6.6: Training dataset (pink), validation dataset (green) and failure (dashed black line) before pre-</p><p>processing.</p><p>Source: Author, 2020.</p><p>69</p><p>Figure 6.7: Training dataset (pink), validation dataset (green) and failure (dashed black line) after pre-processing.</p><p>Source: Author, 2020.</p><p>6.2.4.2 Models tested</p><p>In order to select the model that best performs when submitted to unseen data, the</p><p>mean squared error (RMSE) is selected as evaluation metric. Three kids of networks were tested</p><p>as they appear in the studies presented in the Table 6.1:</p><p> Feed-Forward Neural Network, presented in Schleichtingen and Santos</p><p>(2011) and Kusiak and Verma (2012).</p><p> Autoregressive Neural Networks, shown in Schleichtingen and Santos</p><p>(2011);</p><p> LSTM Neural Network presented in Fu et al. (2019).</p><p>6.2.4.2.1 Feed forward neural network (Full Signal Reconstruction)</p><p>The neural networks of the feed-forward type consist of neurons that are organized</p><p>in layers, the first being the input layer, the last, the output layer and those that are in between</p><p>are the hidden layers (SVOZIL; KVASNIEKA; POSPICHAL, 1997). The work of</p><p>Schleichtingen and Santos (2011) and Kusiak and Verma (2012) use feed-forward neural</p><p>networks to reconstruct the bearing temperature signal, however, they use different network</p><p>architectures and hyperparameters. The feedforward NN presented in Schleichtingen and</p><p>Santos (2011), is put into use. It consists of two layers, the hidden layer and the output layer, as</p><p>shown in Figure 6.8. It is recommended that the optimal number of neurons in the hidden layer</p><p>should be defined by experiment, executing the method at least 10 times, changing only the</p><p>number of neurons, and thus the number of neurons is selected for the best result, with the aim</p><p>70</p><p>of decreasing the risk of finding solutions that do not generalize well (RAFIQ; BUGMANN;</p><p>EASTERBROOK, 2001; SCHLECHTINGEN; SANTOS, 2011).</p><p>Figure 6.8: Feed-forward neural network architecture.</p><p>Source: Author, 2020.</p><p>The neural network training method applied is the gradient descent with</p><p>momentum. Gradient descent is an iterative method that changes the weights of the neural</p><p>network in order to reduce the generalization. The weights are initialized randomly (usually a</p><p>number between -0.01 and +0.01), and then, at each iteration, they are approximated in the</p><p>direction that most reduces the generalization error, until it arrives in an acceptable error value</p><p>(RUSSEL; NORVING, 2013). The moment is a term added to the gradient descent to accelerate</p><p>and stabilize learning, which can have a satisfactory effect on the process (HAYKIN, 2008).</p><p>6.2.4.2.2 Autoregressive Neural Networks</p><p>Some signals are strongly autocorrelated, as future time values can be predicted by</p><p>learning signal autocorrelations. The autoregressive approach used in Schleichtingen and</p><p>Santos (2011) consists in the same inputs used in the neural network presented in the section</p><p>before, but adding the previous bearing temperature also as input signal, as presented in the</p><p>Figure 6.9. The same training method, parameter initialization and activation function used in</p><p>the Full Signal Reconstruction neural network is applied for this one as well.</p><p>71</p><p>Figure 6.9: Autoregressive Feed-forward neural network architecture.</p><p>Source: Author, 2020.</p><p>6.2.4.2.3 Long Short-Term Memory Neural Network</p><p>Recurrent neural networks are able to process arbitrary length sequences by</p><p>memorizing dynamic information of input parameters (SUN, SUN; 2019). Long short-term</p><p>memory neural network is an improved model of the recurrent neural network (RNN). In this</p><p>model, the neurons in the hidden layer of the neural network have a feedback mechanism that</p><p>is able to transmit context, so the model can process sequence data. It uses a length of</p><p>sequenced data as training set and predicts the output of the following moment after completing</p><p>the training (FU et al., 2019).</p><p>As a variant of recurrent neural network, LSTM exploits the internal memory from</p><p>input data to analyse the intrinsic dependencies. Generally speaking, the dependencies in the</p><p>time series can be classified into two types: short-term dependency and long-term dependency.</p><p>The difference from RNN lies in the submodules. The standard LSTM cell is composed by</p><p>three gates, including the forget gate, input gate and output gate (SUN, SUN; 2018). The LSTM</p><p>design allows it leave invalid information aside in the long period. These models are built as</p><p>following the steps:</p><p>The LSTM cell would be regulated to update with three gates:</p><p> The input gate receives information of the current sample;</p><p> The forget gate determines how much of which information would be</p><p>relevant from historical data;</p><p> The output gate controls the context output from the cell;</p><p>In the context of this research, the model built to predict the bearing temperatures</p><p>is set according to the following:</p><p>72</p><p> The LSTM neural network contains 100 units in the first hidden layer and</p><p>50 units in the second hidden layer, both with the Relu activation function;</p><p> The input sequence is set to be 12, or two ours of data;</p><p> The training method is the stochastic gradient descent with momentum.</p><p>6.2.4.4 Results on the validation dataset</p><p>The models presented in the subsection above had the following results in terms of</p><p>Mean Squared Error (MSE) and Root Mean Absolute Error (MAPE):</p><p>Table 6.3:3 Performance results of the networks in the validation dataset.</p><p>Model MAE</p><p>RMSE</p><p>Feed-forward Neural Network 1.879</p><p>2.320</p><p>Autoregressive Neural Network 0.605</p><p>1.004</p><p>LSMT Neural Network 0.353</p><p>0.411</p><p>Source: Author, 2020.</p><p>From this table, the LSTM model performed better than both Autoregressive and</p><p>Full Signal Reconstruction NN’s, being superior in the two metrics observed. Therefore, the</p><p>LSTM model is selected to go further, being applied to the rest of the data. The Figure 6.10</p><p>shows the comparison between the modelled temperature and the real bearing temperature taken</p><p>by the SCADA system. Most of the time, the model follows the real bearing temperature</p><p>closely, which indicate that deviations of normal behaviour of the bearing temperature should</p><p>be identified by the method.</p><p>Figure 6.10: Modelled bearing temperature x temperature measured by the SCADA.</p><p>Source: Author, 2020.</p><p>73</p><p>6.2.5 Post processing</p><p>After selecting the model with best results in the validation dataset, two post</p><p>processing methods were applied to identify incipient faults in the bearing. Both methods use</p><p>residual control charts to identify faults, varying only the threshold definition.</p><p>According to Montgomery (2008), the control charts present a characteristic of a</p><p>sample versus its number or time that it was measured. It usually contains a centre line, which</p><p>corresponds to the mean value of the characteristic; Upper and Lower control limits, which are</p><p>thresholds that, if the process is under control, limit the measured characteristic inside their</p><p>limits so nearly all points are located between them.</p><p>In the case of this research, the characteristic is the residual error between the</p><p>modelled and the measured SCADA data, while the control limits are defined as constant</p><p>threshold, as presented in Schleichtingen, Santos and Achiche (2013), and an adaptive</p><p>threshold, proposed by Talebi Sadrnia and Darabi (2014) and Patan (2008), with some</p><p>modifications.</p><p>6.2.5.1 Constant threshold</p><p>The choice of the limit value is a</p><p>tradeoff between the sensitivity of the model in</p><p>the identification of faults and the number of false alarms (SCHLECHTINGEN; SANTOS,</p><p>2011). For this research, the constant threshold limit selection method discussed in</p><p>Schleichtingen, Santos and Achiche (2013) is implemented, which define the limit in a</p><p>probabilistic way. The error is considered to be normally distributed and should fall between</p><p>upper and lower control limits.</p><p>The threshold is set as a constant multiple of the standard deviation. The choice of</p><p>constant influences the sensitivity of the application and can be adjusted if necessary. Choosing</p><p>generously can increase the number of false alarms, generating an unwanted enhance in</p><p>unscheduled maintenance activities. It can lead to loss of system credibility among</p><p>professionals. In addition, diagnostic failures can cause the unnecessary exchange of</p><p>components, causing additional maintenance to correct errors (SCHLECHTINGEN, SANTOS,</p><p>ACHICHE, 2013).</p><p>Figure 6.11 below shows the example of Schleichtingen, Santos and Achiche</p><p>(2013), presenting a graph of the probability distribution and the evolution of the forecast error</p><p>during a day. It shows the region considered to be in normal operation (center) and the regions</p><p>considered to be in abnormal operation (extremes).</p><p>74</p><p>Figure 6.11: Probability density function graph of the residual error and daily residual error mean.</p><p>Source: (SCHLECHTINGEN; SANTOS; ACHICHE, 2013).</p><p>Hence, for this simulation, the constant threshold is set to be 3 times the standard</p><p>deviation of the residuals taken in the training dataset, and may be set to other values if changes</p><p>the sensibility of the model are needed. The control graph is tested with the values of the daily</p><p>average of the forecast errors. To avoid false alarms, the alarm is only triggered when the daily</p><p>average of the forecast error breaks the limit at least 3 times within a week, recommended by</p><p>the article.</p><p>6.2.5.2 Adaptive Threshold</p><p>Even after all the pre-processing phase, training and testing, modelling uncertainty,</p><p>variations in data and noise still exist. Therefore, in order to avoid too many false alarms, the</p><p>threshold might be set to larger values, which may diminish the fault detection sensitivity since</p><p>bigger thresholds can leave real problems undetected. Hence, an adaptive threshold might be a</p><p>good solution since it is supposed to vary in time along with disturbances or uncontrolled effects</p><p>(PATAN, 2008).</p><p>Taking that into account, this research also implements the approach presented in</p><p>Patan (2008) and Talebi, Sadrnia and Darabi (2014) which developed an adaptive threshold</p><p>based on the estimation of statistical parameters on past observations of residuals, however,</p><p>with one modification, the addition of a constant (𝑎) times the standard deviation (𝜎). The</p><p>75</p><p>proposed method is described in the following equations, assuming that the residual is an</p><p>approximation of the normal distribution:</p><p>𝑇(𝑘) = 𝑡 ∗ �̅�(𝑘) ± 𝑚(𝑘) + 𝑎 ∗ 𝜎</p><p>(6.2)</p><p>�̅�(𝑘) = ζ ∗ 𝑣(𝑘) + (1 − ζ) ∗ 𝑣(𝑘 − 1)</p><p>(6.3)</p><p>𝑚(𝑘) = ζ ∗ 𝑚(𝑘) + (1 − ζ) ∗ 𝑚(𝑘 − 1)</p><p>(6.4)</p><p>Where 𝑇 is the threshold value at the moment 𝑘; 𝑣(𝑘) and 𝑚(𝑘) are the variance</p><p>and the mean of the last 𝑛 samples, respectively, ζ is the momentum parameter, which is</p><p>considered close to 1. 𝛽 is the significance level, that a residual exceeds the random value 𝑡 :</p><p>𝛽 = 𝑃</p><p>𝑟(𝑘) − 𝑚</p><p>𝑣</p><p>> 𝑡</p><p>(6.5)</p><p>The value of 𝑡 is tabulated, therefore, assuming a significance level 𝛽 = 0.003,</p><p>the value of 𝑡 obtained is 3.</p><p>6.2.5.3 Post processing results</p><p>At first, that visualization on the validation dataset is performed in order to better</p><p>see the behaviour of the residual error. Figure 6.12 shows the residual error on a 10 min</p><p>frequency as is taken from the SCADA data. The error varies too much, which may be difficult</p><p>to analyse since it brings lots of high values at once, therefore, as said in the section 6.2.5.1, the</p><p>residual will be analysed on a daily mean basis. Figure 6.13 and 6.14 presents the error averaged</p><p>hourly and daily, respectively.</p><p>76</p><p>Figure 6.12: 10 min residual error on the validation dataset.</p><p>Source: Author, 2020.</p><p>Figure 6.13: 1-hour residual error mean on the validation dataset.</p><p>Source: Author, 2020.</p><p>77</p><p>Figure 6.14: 1-day residual error mean on the validation dataset with constant and adaptive thresholding.</p><p>Source: Author, 2020.</p><p>The Figure 6.14 shows that the model behaves well in the validation dataset. Even</p><p>though the error surpasses the adaptive limit in August, it is not considered a false alarm since</p><p>it does not surpass it 3 times within a week, as can seem in the Figure 6.15. The figure shows</p><p>the table taken from the simulation results, with the data when the limit violation occurred in</p><p>the first column, the measured bearing temperature in the second column, the prediction made</p><p>by the model in the third column and the error with the difference between the measured and</p><p>the prediction made by the model at this timestamp. The error in this sample is large (-5.288)</p><p>compared to the behaviour of the dataset and it might be an indicator of invalid data.</p><p>The adaptive threshold is violated in 2 occasions because of this sample as can be</p><p>seen in the Figure 6.16. Besides the data presented in the Figure 6.15, the Figure also shows the</p><p>upper and lower limits of the adaptive threshold in the fifth and sixth positions. The lower limit</p><p>of the adaptive threshold is clearly affected by this, which is a downside of the adaptive</p><p>thresholding approach. And, as said before the alarm is still not triggered as it does not happen</p><p>3 times within a week.</p><p>Figure 6.15: Error surpasses the constant threshold in August.</p><p>Source: Author, 2020.</p><p>78</p><p>Figure 6.16: Error surpasses the adaptive threshold in August and May.</p><p>Source: Author, 2020.</p><p>The Figure 6.17 shows the method applied in the rest of the dataset, including the</p><p>period of fault. The line in black represents the time when the bearing was changed due to fault.</p><p>The figure shows that the model performed well but was not able to alarm before the fault.</p><p>Figure 6.17: 1-day residual error mean data applied to the test dataset.</p><p>Source: Author, 2020.</p><p>Figure 6.18: Error surpasses the limit in October-2015.</p><p>Source: Author, 2020.</p><p>No alarm is triggered at first, hence, the constant threshold with 3 standard</p><p>deviations may be too generous for this level of model performance, and a more restricted one</p><p>is also tested, with 2 standard deviations for both constant and adaptive thresholding. Figure</p><p>6.19 shows the application of two standard deviations for both constant and adaptive thresholds.</p><p>Figure 6.19: 1-day residual error mean data applied to the test dataset with 2 standard deviations as limit.</p><p>79</p><p>Source: Author, 2020</p><p>Figure 6.20: Error surpasses the constant limit in August, October and November-2015; January, July, August,</p><p>September and October-2016.</p><p>Source: Author, 2020.</p><p>Figure 6.21: Error surpasses the adaptive limit in March-2017 and October-2015.</p><p>Source: Author, 2020.</p><p>It also does not present any alarm since no value surpasses the threshold limit 3</p><p>times within a week. Therefore, as no alarm was triggered in these thresholding approaches,</p><p>some questions arise. The method is designed to model the bearing temperature and, in order</p><p>80</p><p>to perceive its faults, the fault that happened in this point should present variations in the</p><p>behaviour of its temperature as failure mode.</p><p>6.2.6 Retraining</p><p>In order to maintain the application useful, the model should be retrained in order</p><p>to measure the operating conditions of the new bearing. Therefore, the model was retrained</p><p>with 1-year operation data after the failure, as presented in</p><p>the Figure 6.22. The result in this</p><p>validation dataset is similar to the ones presented above, with 0.371 of MAE and 0.431 for</p><p>RMSE. Hence, the new bearing temperature model has similar performance to the old one, and</p><p>might be able to follow the bearing temperature as well.</p><p>Figure 6.22: Training dataset (pink), validation dataset (green) and failure (dashed black line) for the retraining</p><p>phase.</p><p>Source: Author, 2020.</p><p>6.2.6 Comparison with other studies</p><p>In the very last part of the framework, the comparison with other studies is</p><p>undertaken. Three studies were considered in this phase, Kusiak and Verma (2012), Bangalore</p><p>et al. (2017) and Lu et al. (2019) because they have the same goal of predicting bearing</p><p>temperature and present their results explicitly in terms of performance metrics. Table 6.4 is an</p><p>adaptation brought from Bangalore et al. (2017), it brings the results of studies that aimed at</p><p>modelling wind turbine bearings temperature and compare the studies in terms of the</p><p>performance of the model in the validation phase.</p><p>81</p><p>Table 6.4:4 Comparison with other studies in terms of mean absolute error and mean squared error</p><p>Author MAE RMSE</p><p>Kusiak and Verma (2012) 0.693</p><p>Bangalore et. al. (2017) 0.44 0.77</p><p>Lu et al. (2019) (Gearbox bearing model) 0.781</p><p>This research 0.353 0.411</p><p>Source: Author, 2020.</p><p>From the table, the LSTM model presented in this research has better results when</p><p>compared to previous studies in terms of both mean absolute error and root mean squared error</p><p>and, even though the fault could not be identified, it is supposed to follow the bearing</p><p>temperature better than the mentioned models as it performed better when exposed to unseen</p><p>data.</p><p>6.3 Framework description</p><p>The application of the framework step-by-step with real wind turbine data provided</p><p>insights to improve it. First, the data pre-processing phase does note has to happen before the</p><p>parameter selection task. It usually happens in vibration-based studies since it is necessary to</p><p>separate features of vibration from data and it using time and frequency-based transformations.</p><p>However, in studies that use SCADA data, data pre-processing usually comes after parameter</p><p>selection, in both statistical and domain-knowledge selections, as happened in the application</p><p>of the framework of this chapter.</p><p>Second, the comparison with other studies does not have to come after the post</p><p>processing phase. In some studies, the comparison between studies take into account the results</p><p>in the model selection and validation phase, comparing the performance of the algorithms in</p><p>classification or the error metrics in regression tasks. Therefore, the final framework for wind</p><p>turbine application development is the one presented in the Figure 6.23. It is divided in 4</p><p>dimensions: Selecting application, data preparation, model development and evaluation of</p><p>results.</p><p>82</p><p>Figure 6.23: Framework for application of Artificial Intelligence in Wind Turbine Operation and Maintenance.</p><p>Source: Author, 2020.</p><p>The selecting application phase encompasses the process of defining the application</p><p>to be simulated by getting access to data, determining the main goal and how artificial</p><p>intelligence applications can help. The data preparation phase encompasses the processes</p><p>responsible for getting the data ready for AI models: data pre-processing, parameter selection</p><p>83</p><p>and feature extraction. These steps do not have a clear sequence since SCADA and vibration-</p><p>based applications have different paths for data preparation.</p><p>The model development phase is the stage of selecting and improving the AI model</p><p>and fitting it to up-to-date data, having the model selection and validation, fitting best model</p><p>with new data and model retraining as a clear cycle of continuous improvement of the</p><p>application performance when exposed to new data.</p><p>The last phase, the evaluation of results, takes into account the outcomes of the</p><p>simulation encompassing information extracted from the simulation, the decision-making</p><p>criteria and the comparison with other studies. Different from the previous framework, the</p><p>comparison with other studies can be held also after the model selection phase, as happened in</p><p>the simulation of this chapter, since it is important to compare studies according to their model’s</p><p>performance when exposed to new data.</p><p>84</p><p>CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS</p><p>In order to contain climate change, nations around the world must take measures</p><p>towards sustainable energy development. In this scenario, wind energy poses as the main green</p><p>energy resource in the next years (see Figure 1.1), being the most competitive in most of the</p><p>countries. In order to maintain this pattern, wind turbine O&M activities should be improved</p><p>continuously since the wind energy depends on this equipment to transform kinetic energy from</p><p>the wind into electricity, and also because O&M corresponds to a big slice of the investments</p><p>in wind farm projects.</p><p>In this context, artificial Intelligence plays an important role in improving</p><p>competitiveness of wind turbine O&M activities as improvements in processing, data</p><p>acquisition and storage made it possible to build AI applications for this end. Therefore, taking</p><p>this into account, the main objective of this research was to answer the research questions: How</p><p>to apply artificial intelligence tools in wind turbines operations and maintenance? How</p><p>would be an AI application that is relevant for wind turbine maintenance of a Brazilian</p><p>wind farm?</p><p>In order to answer these research questions, seven specific objectives were defined</p><p>and accomplished. The first one was the identification of state-of-the art knowledge about the</p><p>theme held in the chapter 4, in the exploratory research phase, that consisted in a systematic</p><p>literature review with the analysis of 51 articles. that came up with the following insights:</p><p> China has been on top of research about this theme with 18 publications in</p><p>total, representing 35% of participation in the studies. USA is the second in</p><p>the number of publications with 5, followed by UK and Spain with 4</p><p>publications each.</p><p> Artificial neural networks are the most used methods and have been used</p><p>for different purposes like regression, classification and clustering tasks.</p><p> The studies were classified according to their main goal in four different</p><p>types of applications, condition monitoring, maintenance optimization,</p><p>study of indicators, and inspections systems. Condition monitoring</p><p>approaches had the largest number of applications with different approaches</p><p>such as normal behaviour models and remaining useful life prediction,</p><p>aiming at fault detection or fault diagnosis.</p><p>85</p><p> SCADA data is widely used because it is more accessible than other sorts</p><p>of data since every wind turbine is equipped with this system, however, it</p><p>has problems related to poor performance of acquisition due to its low</p><p>frequency data, which brings issues for modelling. Moreover, vibration data</p><p>is widely used for fault diagnosis tasks, even though it is difficult to be</p><p>obtained. Nevertheless, researchers sometimes go to test rigs or simulations</p><p>to test the effectiveness of the proposed approach.</p><p> The studies aimed at building applications for the whole wind turbine and</p><p>for its subcomponents, with the gearbox being the equipment with most</p><p>studies followed by the generator and the blades.</p><p> As only one study brought results in terms of monetary savings in adopting</p><p>AI in wind turbines operations and maintenance, it can be perceived as a</p><p>gap in the research and should be explored by further studies in a way of</p><p>bringing even more attention from the industry to the theme.</p><p>The second specific objective was to develop a conceptual</p><p>Figure 4.2: Geographic distribution of the institutions that the authors represent. .................. 41</p><p>Figure 4.3: Distribution of articles according to application. ................................................... 42</p><p>Figure 4.4: Failure annual frequency and downtime per failure for the wind turbines. ........... 43</p><p>Figure 4.5: Main applications found in the studies by equipment. .......................................... 50</p><p>Figure 4.6: Distribution of articles that used SCADA or vibration data for condition</p><p>monitoring. ............................................................................................................................... 51</p><p>Figure 4.7: Distribution of articles that used Vibration or SCADA data for condition</p><p>monitoring according to their main goal. ................................................................................. 51</p><p>Figure 5.1: Conceptual framework with the main factors to consider when building Artificial</p><p>Intelligence Applications for wind turbine operations and maintenance. ................................ 54</p><p>Figure 6.1: SCADA system architecture in wind farms. .......................................................... 63</p><p>Figure 6.2: Sensor location of the SCADA monitoring parameters selected by Schleichtingen</p><p>and Santos (2011) for building the normal behaviour models. ................................................ 64</p><p>Figure 6.3: Generator bearing data correlation for the wind turbine. ....................................... 65</p><p>Figure 6.4: Seasonality analysis between active power and front generator bearing</p><p>temperature. .............................................................................................................................. 67</p><p>Figure 6.5: Seasonality analysis between winding temperature and front generation bearing</p><p>temperature. .............................................................................................................................. 68</p><p>Figure 6.6: Training dataset (pink), validation dataset (green) and failure (dashed black line)</p><p>before pre-processing. .............................................................................................................. 68</p><p>Figure 6.7: Training dataset (pink), validation dataset (green) and failure (dashed black line)</p><p>after pre-processing. ................................................................................................................. 69</p><p>Figure 6.8: Feed-forward neural network architecture. ............................................................ 70</p><p>Figure 6.9: Autoregressive Feed-forward neural network architecture. ................................... 71</p><p>Figure 6.10: Modelled bearing temperature x temperature measured by the SCADA. ........... 72</p><p>Figure 6.11: Probability density function graph of the residual error and daily residual error</p><p>mean. ........................................................................................................................................ 74</p><p>Figure 6.12: 10 min residual error on the validation dataset. ................................................... 76</p><p>Figure 6.13: 1-hour residual error mean on the validation dataset. .......................................... 76</p><p>Figure 6.14: 1-day residual error mean on the validation dataset with constant and adaptive</p><p>thresholding. ............................................................................................................................. 77</p><p>Figure 6.15: Error surpasses the constant threshold in August. ............................................... 77</p><p>Figure 6.16: Error surpasses the adaptive threshold in August and May. ................................ 78</p><p>Figure 6.17: 1-day residual error mean data applied to the test dataset. .................................. 78</p><p>Figure 6.18: Error surpasses the limit in October-2015. .......................................................... 78</p><p>Figure 6.19: 1-day residual error mean data applied to the test dataset with 2 standard</p><p>deviations as limit. .................................................................................................................... 78</p><p>Figure 6.20: Error surpasses the constant limit in August, October and November-2015;</p><p>January, July, August, September and October-2016. ............................................................. 79</p><p>Figure 6.21: Error surpasses the adaptive limit in March-2017 and October-2015. ................ 79</p><p>Figure 6.22: Training dataset (pink), validation dataset (green) and failure (dashed black line)</p><p>for the retraining phase. ............................................................................................................ 80</p><p>Figure 6.23: Framework for application of Artificial Intelligence in Wind Turbine Operation</p><p>and Maintenance. ...................................................................................................................... 82</p><p>TABLE INDEX</p><p>Table 4.1: Some classification and regression metrics found in the studies. ........................... 52</p><p>Table 5.1: Steps mentioned by the articles selected in for literature reviewing. ...................... 55</p><p>Table 6.1: Some quantitative results of studies that used SCADA data to build applications of</p><p>condition monitoring of wind turbine bearings. ....................................................................... 62</p><p>Table 6.2:1Front generator bearing faults that happened in the wind farm. ............................. 62</p><p>Table 6.3:1Performance results of the networks in the validation dataset. ............................... 72</p><p>Table 6.4:1Comparison with other studies in terms of mean absolute error and mean squared</p><p>error .......................................................................................................................................... 81</p><p>CHART INDEX</p><p>Chart 2.1: Research classification. ........................................................................................... 21</p><p>Chart 2.2: Key-words combination for searching for papers in the Periodicos Capes platform.</p><p>.................................................................................................................................................. 23</p><p>Chart 3.1: Artificial intelligence definitions separated by categories. ..................................... 28</p><p>Chart 6.1: Input data for building the normal behaviour model proposed by Schlechtingen and</p><p>Santos (2011). ........................................................................................................................... 64</p><p>ACRONYMS</p><p>O&M Operations and Maintenance</p><p>AI Artificial Intelligence</p><p>SCADA Supervisory control and data acquisition</p><p>SLR Systematic Literature Review</p><p>ML Machine Learning</p><p>ANN Artificial Neural Networks</p><p>NN Neural Network</p><p>ANFIS Adaptive Neural Fuzzy Inference System</p><p>SVM Support Vector Machines</p><p>KNN K-Nearest Neighbours</p><p>LSTM Long Short-Term Memory</p><p>ELM Extreme Learning Machine</p><p>PCA Principal Component Analysis</p><p>DAE Deep Auto-Encoder</p><p>DFIG Doubly-fed Induction Generator</p><p>RNN Recurrent Neural Networks</p><p>EWMA Exponentially Weighted Moving Average</p><p>SOM self-Organizing Maps</p><p>CNN Convolutional Neural Networks</p><p>MSE Mean Squared Error</p><p>RMSE Root Mean Squared Error</p><p>MAPE Mean Absolute Percentage Error</p><p>MAE Mean Absolute Error</p><p>FFNN Feed-Forward Neural Network</p><p>ARNN Autoregressive Neural Network</p><p>SUMMARY</p><p>ACKNOWLEDGMENTS .......................................................................................................... 3</p><p>RESUMO ................................................................................................................................... 4</p><p>ABSTRACT ............................................................................................................................... 5</p><p>FIGURE INDEX ........................................................................................................................</p><p>framework for the</p><p>application of AI in wind turbine operations and maintenance, held in Chapter 5. After</p><p>analysing the articles and mapping the steps taken by them, a framework that summarizes the</p><p>procedure of developing applications of AI in wind turbine operations and maintenance was</p><p>proposed. The framework is composed by 10 steps: Getting data; define how AI can help; data</p><p>pre-processing; feature selection and extraction; model selection and validation; fit best model</p><p>with new data; post-processing; model retraining; comparison with other studies; and extract</p><p>information from data.</p><p>The third specific objective was to disclosure relevant applications for a Brazilian</p><p>wind farm within the studies and compare methods, considering main goal, data needed to</p><p>compose the study, complexity and results. It was performed in the Section 6.1 in an interview</p><p>with a Brazilian wind farm O&M specialist. In this interview, the application for wind turbine</p><p>bearing condition monitoring was selected to be simulated since the necessary data was</p><p>available for use, the methods are not too complex and the fact that this kind of application is</p><p>relevant for the wind farm in question.</p><p>After that, the fourth and fifth specify objectives were achieved by validating the</p><p>framework procedure with the application of it in a Brazilian wind farm. A condition</p><p>monitoring application using normal behaviour model for the generator bearing temperature</p><p>was conducted, using SCADA data from the turbine that had the earliest generator bearing</p><p>failure. The steps were followed according to practices taken from the articles:</p><p>86</p><p> The generator bearing temperature was modelled using SCADA data of</p><p>Nacelle temperature, high speed shaft rotation, power output and winding</p><p>temperature as in Schleichtingen and Santos (2011);</p><p> General data pre-processing steps were performed and separation in</p><p>training, validation and testing dataset was discussed;</p><p> FFNN, ARNN and LSTM NN were used to model the parameter, with the</p><p>LSTM having superior performance when exposed to unseen data, even</p><p>when compared with other studies;</p><p> Constant and adaptive thresholds were implemented as pos-processing</p><p>methods; however, the model could not identify the fault before it happens</p><p>in both approaches.</p><p> The adaptive threshold approach is sensitive to high gradients in the</p><p>residual.</p><p>Even though the approach could not identify the faulty bearing properly, it</p><p>presented better results in terms of performance when exposed to unseen data, which indicates</p><p>that it mimics the behaviour of the bearing temperature accordingly.</p><p>The last specific objective was achieved by re-modelling the conceptual framework</p><p>with considerations learned after the simulation, and it was achieved in the Section 6.3. In this</p><p>phase, four dimensions were created: selecting application, model preparation, model</p><p>development and evaluation of results. The data pre-processing, and parameter selection and</p><p>feature extraction phases were rearranged as they do not have to follow a clear sequence since</p><p>sometimes the data pre-processing comes before the parameter selection and feature extraction</p><p>phase (case of vibration studies) and the opposite also happens. The comparison with other</p><p>studies phase was also linked to the model selection and validation one since studies can also</p><p>be compared in this step, in terms of algorithm performance.</p><p>The proposed model is supposed to mimics the bearing temperature and can only</p><p>be able to identify faults if the bearing temperature disturbances happen as failure mode.</p><p>Therefore, further studies with turbines that have a clear history of this kind of fault are</p><p>important to test the effectiveness of the proposed framework and algorithms.</p><p>87</p><p>REFERENCES</p><p>ABD-ELKADER, A. G.; ALLAM, D. F.; TAGELDIN, E. Islanding detection method for DFIG wind turbines</p><p>using artificial neural networks. Electrical Power and Energy Systems, v. 62, p. 335-343, 2014.</p><p>ABEEÓLICA. Boletim Anual de Geração Eólica 2017. 2018.</p><p>ABEEÓLICA. Energia eólica chega a 14,71 GW de capacidade instalada. Disponível em:</p><p>. 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IEEE Access, v. 7, p. 773-781. 2019.</p><p>93</p><p>APPENDIX 1 – QUESTIONS OF THE INTERVIEW WITH THE WIND FARM O&M</p><p>SPECIALIST</p><p>Q - How are the applications used in the day-to-day operation and maintenance of wind</p><p>turbines that assist in activities such as decision making and equipment monitoring?</p><p>Q - What are their characteristics?</p><p>Q - Do these tools allow the creation of a database?</p><p>Q - Which system is most concerned with the time of operation and maintenance, that is,</p><p>which system is the most critical?</p><p>Q - Are the existing tools efficient in monitoring this system today?</p><p>Q - Are there any bottlenecks that can be improved by other applications?</p><p>Q - Among the reviewed tools in the systematic bibliographic review, are there any that can</p><p>contribute to monitoring the condition of this system? Which one?</p><p>Q – The database is available for the development of this application?</p><p>6</p><p>TABLE INDEX .......................................................................................................................... 8</p><p>CHART INDEX ......................................................................................................................... 9</p><p>ACRONYMS ........................................................................................................................... 10</p><p>SUMMARY ............................................................................................................................. 11</p><p>CHAPTER 1: INTRODUCTION ............................................................................................. 14</p><p>1.1 Background .................................................................................................................... 14</p><p>1.2 Objectives ....................................................................................................................... 17</p><p>1.2.1 Main objective .......................................................................................................... 17</p><p>1.2.2 Specific objectives .................................................................................................... 17</p><p>1.3 Justification of research .................................................................................................. 17</p><p>1.4 Thesis structure ............................................................................................................... 19</p><p>CHAPTER 2: RESEARCH METHOD .................................................................................... 21</p><p>2.1 Research procedure ........................................................................................................ 21</p><p>2.2.1 Exploratory research ................................................................................................ 22</p><p>2.2.2 Development of framework for the application of ai in wind turbine operations and</p><p>maintenance .............................................................................................................. 23</p><p>2.2.3 Identification of relevant ai application for a brazilian wind farm maintenance ..... 23</p><p>2.2.4 Data acquisition ........................................................................................................ 24</p><p>2.2.5 Implementation of the framework for the application suggested by the o&m</p><p>specialist ................................................................................................................... 24</p><p>2.2.6 Framework restructuring after practical experience ................................................. 24</p><p>CHAPTER 3: LITERATURE REVIEW .................................................................................. 25</p><p>3.1 Wind energy .................................................................................................................... 25</p><p>3.2 Artificial intelligence ...................................................................................................... 28</p><p>3.3.1 ARTIFICIAL INTELLIGENCE METHODS AND CONCEPTS .............................. 32</p><p>3.3.1 Artificial neural networks (ann) ............................................................................... 32</p><p>3.3.2 Support vector machines (svm) ................................................................................ 35</p><p>3.3.3 Decision trees ........................................................................................................... 36</p><p>3.3.4 Ensemble learning .................................................................................................... 37</p><p>3.3.5 K-nearest neighbours ................................................................................................ 37</p><p>3.3.5 Fuzzy logic ............................................................................................................... 37</p><p>3.4 Ai applications for wind turbine maintenance ............................................................... 37</p><p>CHAPTER 4: STATE OF THE ART ...................................................................................... 40</p><p>4.1 Descriptive analysis ........................................................................................................ 40</p><p>4.1.1 Geographic distribution ............................................................................................ 41</p><p>4.1.2 Publications by journal ............................................................................................. 41</p><p>4.2 Content analysis ............................................................................................................. 42</p><p>4.2.1 Studies developed by equipment .............................................................................. 42</p><p>4.2.2 Scada vs vibration data for condition monitoring .................................................... 50</p><p>4.2.3 Evaluation metrics .................................................................................................... 52</p><p>CHAPTER 5: CONCEPTUAL FRAMEWORK FOR APPLICATION OF ARTIFICIAL</p><p>INTELLIGENCE IN WIND TURBINE OPERATION AND MAINTENANCE .................. 54</p><p>5.1 Getting data .................................................................................................................... 56</p><p>5.2 Define how ai can help in the approach .......................................................................... 56</p><p>5.3 Data pre-processing ........................................................................................................ 57</p><p>5.4 Parameter selection and feature extraction ..................................................................... 57</p><p>5.3 Model selection and validation ....................................................................................... 58</p><p>5.4 Fit best model to unseen data ......................................................................................... 58</p><p>5.5 Retrain model periodically ............................................................................................. 59</p><p>5.6 Post processing ............................................................................................................... 59</p><p>5.7 Comparison with other studies ....................................................................................... 60</p><p>CHAPTER 6: CONCEPTUAL FRAMEWORK APPLICATION AND LEARNING</p><p>LESSONS ................................................................................................................................. 61</p><p>6.1 Selection of application .................................................................................................. 61</p><p>6.2 Application of the conceptual framework ...................................................................... 62</p><p>6.2.1 Getting data .............................................................................................................. 63</p><p>6.2.2 Define how ai can help in the approach ................................................................... 63</p><p>6.2.3 Parameter selection .................................................................................................. 64</p><p>6.2.4 Data pre-processing .................................................................................................. 66</p><p>6.2.4 Model selection ........................................................................................................ 66</p><p>6.2.5 Post processing ......................................................................................................... 73</p><p>6.2.6 Retraining ................................................................................................................. 80</p><p>6.2.6 Comparison with other studies ................................................................................. 80</p><p>6.3 Framework description ................................................................................................... 81</p><p>CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS</p><p>........................................... 84</p><p>REFERENCES ......................................................................................................................... 87</p><p>APPENDIX 1 – QUESTIONS OF THE INTERVIEW WITH THE WIND FARM O&M</p><p>SPECIALIST ............................................................................................................................ 93</p><p>14</p><p>CHAPTER 1: INTRODUCTION</p><p>1.1 Background</p><p>Electricity generation is an essential topic when it comes to socioeconomic</p><p>development, being the central role in some nation’s economy (GONZÁLEZ; GONÇALVES;</p><p>VASCONCELOS, 2017; LIMA; FERREIRA; VIEIRA, 2013). Recently, this topic generates</p><p>big concerns in nations worldwide related to the global power matrix because the energy</p><p>production is the main source of fossil fuels, which are pointed by the scientific community as</p><p>the reason of climate changes.</p><p>The climate changes can be considered as one of the greatest challenges for human</p><p>kind as it has potential to transform all ecosystems. According to a report published by the</p><p>Intergovernmental Panel on Climate Change (united nations body for accessing science related</p><p>to the climate change) in 2018, maintaining the increase in temperature below 1.5°C more than</p><p>pre-industrial levels is fundamental for human kind to adapt to the climate changes and,</p><p>therefore, the reduction of emissions in the industrial processes is needed.</p><p>In this sense, according to the International Renewable Energy Agency (2018), the</p><p>renewable energy sources have been increasing in the global energy matrix and this trend will</p><p>continue while the fossil fuels will reduce its participation, as is presented in the Figure 1.1.</p><p>However, despite this ongoing shift to renewable energy sources, it still seems not enough. The</p><p>report published by the International Energy Agency in 2018 points that after three years of</p><p>stability, the carbon dioxide emissions related to the production of energy has increased 1.6%</p><p>in 2017. It pushes us further away from a consistent path towards the objectives set in the Paris</p><p>Agreement, which aims at limiting the increase in the average world temperature bellow 2°C.</p><p>Therefore, the development of renewable energy, such as solar and wind energy, is important</p><p>for the sustainable development of the planet.</p><p>15</p><p>Figure 1.1: Global Energy matrix evolution.</p><p>Source: IRENA, 2020.</p><p>When it comes to the renewable energy growth presented in the Figure 1.1, one can</p><p>highlight the development of the wind energy source that will represent the great portion of</p><p>energy production in the total world energy matrix in 2050 (IRENA, 2020). According to Vestas</p><p>(2020), the wind energy is already the most competitive in two thirds of the world, as can be</p><p>seem in the Figure 1.2. In this context, the Brazil wind energy market is the biggest and most</p><p>promising one in Latin America (GONZÁLEZ; GONÇALVES; VASCONCELOS, 2017;</p><p>SANTOS; GONZÁLEZ, 2019). The country is the 7° in the world in installed capacity with</p><p>more than 14 GW (ABEEÓLICA, 2019; GWEC, 2019). Moreover, only in 2017, the Brazilian</p><p>wind energy industry avoided 20.97 million of ton of 𝐶𝑂 emissions and attracted around R$</p><p>11 billion in investments, generating 30 thousand jobs. It is also strategic in the production of</p><p>electricity, mainly for the northeast region since it goes through long periods of draughts that</p><p>jeopardize the hydro energy production (ABEEOLICA, 2018). Hence, the development of wind</p><p>energy in Brazil can be strategic for environmental and economic reasons</p><p>.</p><p>Figure 1.2: Countries with wind power as the most competitive energy source.</p><p>Source: Vestas, 2020.</p><p>16</p><p>According to Amina et al. (2017), wind energy technology is considered the most</p><p>competitive among the renewable energy sources. The wind energy consists in converting the</p><p>kinetic energy from the wind into electric energy through the wind turbine. Therefore,</p><p>emphasizing the central role that this equipment plays in the wind energy, its operation and</p><p>maintenance (O&M) influences in its competitiveness. Corroborating with this statement,</p><p>Hameed and Wang (2013), Kusiak and Verma (2011) and Wang et al. (2014), say that the costs</p><p>associated with wind turbine operation and maintenance are high, and reductions in this cost</p><p>would lead to higher probability of the projects to be lucrative, enhancing wind power</p><p>competitiveness. In this same line, Vesely (2017) stands that the O&M costs are responsible for</p><p>around 30% of the Levelized Cost per KWh (indicator that measures the life cycle cost of the</p><p>project per its power production).</p><p>Artificial intelligence can be an important tool for O&M optimization. The</p><p>improvements in processing and data acquisition made artificial intelligence (AI) more famous</p><p>in society and, in striving to improve operation and maintenance competitiveness, a variety of</p><p>fields of the industry is looking towards AI as a way for building new tools to improve</p><p>performance, which could not be different in wind energy. Vesely (2017) states that companies</p><p>like GE, Siemens and SAP have been investing in research and development of machine</p><p>learning tools for fault prediction in wind turbines. Furthermore, Vestas announced a data</p><p>acquisition system 600 times faster than traditional SCADA (Supervisory, Control and Data</p><p>Acquisition System) in its MVOW Smart Turbine Portfolio (MHI VESTAS, 2018), which</p><p>enables much more data acquisition that can serve as fuel for building machine learning</p><p>applications.</p><p>After all, taking into account the importance of wind energy in achieving the goals</p><p>of reduction in carbon dioxide emissions set in the Paris Agreement; the strategic role it has</p><p>when it comes to energy security in Brazil as well as the enhance in investments in the sector;</p><p>the influence O&M has on wind power competitiveness; and the AI potential for building tools</p><p>for wind turbine maintenance; this thesis urge to answer the following research questions: How</p><p>to apply artificial intelligence tools in wind turbines operations and maintenance? How</p><p>would be an AI application that is relevant for wind turbine maintenance of a Brazilian</p><p>wind farm?</p><p>17</p><p>1.2 Objectives</p><p>After presenting the research questions, the main objective and the specific</p><p>objectives are identified as following.</p><p>1.2.1 Main Objective</p><p>This research has the main goal of proposing a framework for AI applications in</p><p>wind turbines.</p><p>1.2.2 Specific Objectives</p><p> Identify state-of-the-art knowledge about AI applications for wind turbine</p><p>operation and maintenance.</p><p> Compare methods, considering main goal, data needed to compose the study,</p><p>complexity and results.</p><p> Develop a conceptual framework for the application of AI in wind turbine</p><p>operations and maintenance.</p><p> Validate the framework procedure by the application of it in a Brazilian wind</p><p>farm, develop algorithms for simulation;</p><p> Modelling the final framework with considerations learned after the</p><p>simulation;</p><p>1.3 Justification of research</p><p>The justification is analysed considering the socioeconomics, environmental and</p><p>academic aspects.</p><p>The 2030 Agenda for the sustainable development is a plan of action firmed in 2015</p><p>by worldwide leaders in the United Nations that aims at eradicating poverty, protect the planet</p><p>and guarantee prosperity and peaceful life for people. It is composed by 17 objectives for</p><p>sustainable development with bold indicators to be achieved by the year of 2030, as it is</p><p>presented in the Figure 1.3 (ONU, 2020). This research aims at contributing to this Agenda,</p><p>especially in two specific objectives, the objective 7 related to affordable and clean energy and</p><p>the objective 9 related to industry, innovation and infrastructure.</p><p>18</p><p>Figure 1.3: 17 objectives of the Agenda 2030.</p><p>Source: (UN, 2020).</p><p>According to Bloomberg</p><p>NEF (2016), the consistent winds of Brazil, especially in</p><p>the northeast region, make the Brazilian wind farms present the best capacity factors in the</p><p>world, as it is shown in the Figure 1.4. Moreover, this characteristic will not be changing with</p><p>climate changes since it is expected that the climate transformations would be favourable for</p><p>the availability of wind power projects (PEREIRA et al., 2013).</p><p>Figure 1.4: Average capacity factor by country.</p><p>Source: BLOOMBERG NEF, 2016.</p><p>As a result of this favourable environmental conditions, it is expected that the wind</p><p>energy will continue to grow in Brazil. According to ABEEólica (2018), the wind energy is</p><p>expected to enhance its installed capacity to 17 GW until 2023, moreover, Bernardes et al.</p><p>19</p><p>(2018) mentions Bloomberg NEF (2016) and ANEEL (2018) when stating that the new</p><p>technologies already available for the last auctions reduced the cost of wind energy around</p><p>400%, which shows increasing competitiveness in the country.</p><p>In the academy, the relationship between wind turbines maintenance and AI is a</p><p>relatively new line of research that has been gaining relevance. The evaluation of articles used</p><p>in the systematic literature review phase of this research came up with 51 peer-reviewed articles</p><p>in English in the Periodicos Capes Platform (gathers the main bases like Web of Science,</p><p>Scopus, among others), with the first one published in 1999, it can be seen a growing trend in</p><p>the research about the topic, which show its actual relevance for the academic community.</p><p>Figure 1.5: Number of publications about artificial intelligence applications for wind turbine maintenance</p><p>according to a survey carried out in the Periodicos Capes Platform.</p><p>Source: Author, 2020.</p><p>Hence, the research performed in this thesis justifies itself environmentally and</p><p>socioeconomically because it aims at contributing to the objectives 7 and 9 of the 2030 Agenda,</p><p>creating knowledge that can be used to enhance wind energy competitiveness through</p><p>improvements in wind turbine maintenance efficiency, therefore, fomenting the development</p><p>of clean energy. It also justifies itself academically because this theme is relatively new to the</p><p>academic community, and it can contribute to generation of knowledge to the field of wind</p><p>energy, when it comes to wind turbine maintenance.</p><p>1.4 Thesis structure</p><p>This thesis is divided into 7 chapters. After this introduction, the Chapter 2 brings</p><p>the research method, classifying it according to the research genre, objective, argumentation,</p><p>approach and scientific procedure, it also will present the step-by-step procedure carried out for</p><p>building the systematic literature review and the field research. Next, the Chapter 3 presents the</p><p>traditional literature review about wind energy, artificial intelligence and wind turbine</p><p>maintenance. The fourth chapter presents state-of-the knowledge about the research theme,</p><p>11</p><p>14</p><p>7</p><p>6</p><p>1</p><p>4</p><p>2</p><p>3</p><p>2</p><p>00000000000</p><p>1</p><p>20</p><p>19</p><p>20</p><p>18</p><p>20</p><p>17</p><p>20</p><p>16</p><p>20</p><p>15</p><p>20</p><p>14</p><p>20</p><p>13</p><p>20</p><p>12</p><p>20</p><p>11</p><p>20</p><p>10</p><p>20</p><p>09</p><p>20</p><p>08</p><p>20</p><p>07</p><p>20</p><p>06</p><p>20</p><p>05</p><p>20</p><p>04</p><p>20</p><p>03</p><p>20</p><p>02</p><p>20</p><p>01</p><p>20</p><p>00</p><p>19</p><p>99</p><p>N</p><p>um</p><p>be</p><p>r o</p><p>f</p><p>pu</p><p>bl</p><p>ic</p><p>at</p><p>io</p><p>ns</p><p>20</p><p>showing the classification of articles according to region and journal that they are published, as</p><p>well as discussions about the content found in the articles related to data used, sources, wind</p><p>turbine equipment that were focused, main goal of applications, results and metrics for</p><p>evaluation. The Chapter 5 brings proposed framework, showing the steps mapped for building</p><p>AI applications for Wind Turbine maintenance. Chapter 6 presents the field research, the</p><p>process for selecting the application and the step-by-step of the simulation. Chapter 7 shows</p><p>the final considerations and future studies.</p><p>21</p><p>CHAPTER 2: RESEARCH METHOD</p><p>This chapter presents the method used in the development of this research. It</p><p>describes the research procedure, showing the steps taken in order to develop the literature</p><p>review and the field research.</p><p>According to Demo (1985), the methodology is the instrumental part of the science,</p><p>accounting for the proceedings, tools and path of science activity. There are criteria to</p><p>characterize and classify the research methods (CRESWELL, 2009; DEMO, 1985; LAKATOS;</p><p>MARCONI, 2003; YIN, 2001). The Chart 2.1 presents a structure adapted from González</p><p>(2010), where it is possible to classify the research method directly.</p><p>Chart 2.1: Research classification.</p><p>Source: Adapted from González (2010).</p><p>Therefore, this study can be classified as a practical research, with explainable</p><p>objective, inductive argumentation, quantitative approach and uses the simulation method in</p><p>order to achieve its main goal.</p><p>2.1 Research procedure</p><p>The research procedure of this thesis encompasses 6 stages as it is presented in the</p><p>Figure 2.1: 1) Exploratory research (traditional and systematic literature review); 2)</p><p>Development of conceptual framework for the application of AI in wind turbine maintenance;</p><p>Criteria Classification Choice</p><p>Theoretical</p><p>Methodological</p><p>Empirica</p><p>Practical X</p><p>Exploratory</p><p>Descriptive</p><p>Explanatory X</p><p>Inductive X</p><p>Deductive</p><p>Hypotetical-Deductive</p><p>Dialetic</p><p>Quantitative X</p><p>Qualitative</p><p>Quantitative-qualitative</p><p>Survey</p><p>Simulation X</p><p>Research-action</p><p>Case study</p><p>Study of cases</p><p>According to research genre (DEMO, 1985)</p><p>According to research objective (YIN, 2001)</p><p>According to argumentation (MARCONI; LAKATOS, 2003)</p><p>According to approach (CRESWELL, 2009)</p><p>According to research proceedure (YIN, 2001)</p><p>22</p><p>3) Identification of relevant AI application for a Brazilian Wind Farm maintenance; 4) Data</p><p>acquisition; 5) Application of the framework for the AI application proposed and; 6) Structuring</p><p>the framework with practical experience obtained in the 5 stage.</p><p>Figure 2.1: Research procedure.</p><p>Source: Author, 2020.</p><p>2.2.1 Exploratory research</p><p>This step was undertaken in building the theoretical background and the state-of-</p><p>the-art knowledge of this research, and it was carried out through 2 methods, the traditional</p><p>literature review and the systematic literature review. The traditional literature review was</p><p>performed through the reading of books, thesis, articles and websites about wind energy, AI</p><p>and wind turbine O&M, in order to present the history of wind power and artificial intelligence</p><p>and the main concepts behind this study. The systematic literature review was performed by</p><p>selecting articles about the theme, with the goal of identifying state-of-the-art knowledge.</p><p>The systematic literature review is a reliable and replicable method that describes</p><p>the means for obtaining the results, allowing structured and unbiased generation of knowledge</p><p>about the theme (PAI, et al; 2004; WEBSTER; WATSON, 2002; SAMPAIO; GONZÁLEZ,</p><p>2017). In this thesis, it was performed in 5 stages, as it is presented in the Figure 2.2.</p><p>In the first stage, after reading masters and PhD thesis about the theme, it was found</p><p>key-words associated to it and the research question was defined as: How to apply artificial</p><p>intelligence tools in wind turbines operations and maintenance? How would be an AI</p><p>application that is relevant for wind turbine maintenance of a Brazilian wind farm? After</p><p>that, the second stage was initiated, it consists in searching for papers in the Periodicos Capes</p><p>platform. The Periodicos Capes Platform contains 130 scientific bases, including Scopus, Web</p><p>of Science and Science Direct, and other content dedicated to the study of patents, books,</p><p>encyclopedias and norms. The search for papers was performed with a combination of key-</p><p>23</p><p>words related to AI and wind energy, as it is presented in the Chart 2.2. The combinations were</p><p>set to be found only on the article’s titles. The search for articles was filtered in order to find</p><p>only peer-reviewed articles written in English.</p><p>Figure 2.2: Systematic</p><p>Literature Review procedure.</p><p>Source: Author, 2020.</p><p>Chart 2.2: Key-words combination for searching for papers in the Periodicos Capes platform.</p><p>Source: Author, 2020.</p><p>In Article selection phase, the abstracts of the articles found in the search were read,</p><p>discarding the ones that did not have relationship with the theme and the repeated ones, leading</p><p>to 51 articles in total. The selected articles were then fully read, having their relevant</p><p>information collected in Excel Spreadsheets in order to facilitate classification and analysis.</p><p>2.2.2 Development of framework for the application of AI in wind turbine operations</p><p>and maintenance</p><p>After the systematic literature review stage, a framework for applying AI in wind</p><p>turbine maintenance was developed. In order to do so, the main steps taken in the articles were</p><p>mapped, showing an overview of the path for building an artificial intelligence application for</p><p>wind turbine maintenance which can serve as a guide for researchers and developers.</p><p>2.2.3 Identification of relevant AI application for a Brazilian wind farm maintenance</p><p>In this step, an interview with a wind farm O&M specialist was undertaken in order</p><p>to define applications in the systematic literature review that would be relevant for the</p><p>AI WIND ENERGY</p><p>Artificial Intelligence Wind Energy</p><p>Neural Networks Wind Power</p><p>Deep Learning Wind Turbines</p><p>Machine Learning Wind Farms</p><p>Data Mining Wind Park</p><p>IOT</p><p>AND</p><p>Key Words</p><p>24</p><p>maintenance routine of the wind farm. The main applications found in the SLR were presented,</p><p>showing their functionalities, data used to build them and the main results.</p><p>Furthermore, the interview also included topics related to the tools used for wind</p><p>turbine operation and maintenance used in wind farms, data availability, the most critical</p><p>equipment and bottlenecks of operation. The questions asked in the interview are presented in</p><p>the Appendix 1.</p><p>After this discussion, the application for temperature condition monitoring of wind</p><p>turbine bearings was selected due to its relevance for the Brazilian wind farm and viability to</p><p>be simulated.</p><p>2.2.4 Data acquisition</p><p>In the same interview, after defining the applications in the SLR that would be</p><p>relevant for the wind farm, the availability of data was also discussed in order to verify which</p><p>wind turbine data would be available for simulating the application. Issues related to data</p><p>acquisition and data pre-processing were also taken into account. It was concluded that SCADA</p><p>data would be available for the wind farm under study since it does not bring difficulties related</p><p>to data gathering.</p><p>2.2.5 Implementation of the framework for the application suggested by the O&M</p><p>specialist</p><p>Once the availability of the data is checked, the framework proposed in 2.2.2 was</p><p>performed. The step-by-step procedure was followed accordingly, using the methods mapped</p><p>in the systematic literature review. The simulations were performed using Python 3.7 in Jupyter</p><p>Notebook environment. After simulations, the analysis of the application of the framework is</p><p>performed addressing the results and the viability of implementation.</p><p>2.2.6 Framework restructuring after practical experience</p><p>In this phase, learning lessons from the simulation of the conceptual framework are</p><p>discussed and the steps considered in it were rearranged in order to better attend the</p><p>observations and come up with a more concise step-by-step procedure, proposing a final</p><p>framework.</p><p>25</p><p>CHAPTER 3: LITERATURE REVIEW</p><p>This chapter comes with the main content related to the research theme, presenting</p><p>definitions about wind energy, history, and the main wind turbine components; it will also</p><p>present knowledge about Artificial intelligence and the main methods found in the SLR.</p><p>3.1 Wind energy</p><p>The wind energy can be understood as the conversion of the wind kinetic energy</p><p>into useful energy. It is a process that has being used for centuries since it is estimated that 2000</p><p>years ago the Chinese used windmills for pumping water up and corn processing. Following</p><p>this timeline, in the XIV century the Dutch adopted windmills for draining soil, and in the</p><p>century XIX, millions of windmills were installed in the US in order to pump water (LETCHER,</p><p>2017).</p><p>It was also in the XIX century that it was noticed the first wind machines in the US,</p><p>they were used in the operation of equipment, however, the electric generation to the grid started</p><p>only in the 70’s, launched by the necessity of replacement of fossil fuels for renewable energy</p><p>sources (LETCHER, 2017). The interest for this “new” type of energy generation boosted its</p><p>development.</p><p>According to ESBJERG (2020), the wind turbines have been increasing both in</p><p>power and height since the 90’s, as can be seen in the Figure 3.1. It shows the evolution of</p><p>offshore wind turbines that, since the 90s, the wind turbines increased in 7 times their height</p><p>and in 33 times their power output.</p><p>According to Hossain, Abu-Siada and Muyeen (2018), modern wind turbines are</p><p>complex electromechanics systems composed by some components and subcomponents. The</p><p>Figure 3.2 shows an illustration of a typical wind turbine, its equipment and systems.</p><p>26</p><p>Figure 3.1: Evolution of offshore wind turbines since 1991.</p><p>Source: Esbjerg, 2020.</p><p>Figure 3.2: Main components of a typical large-scale wind turbine.</p><p>Source: Hossain, Abu-Siada and Muyeen (2018)</p><p>The wind turbines are usually located in remote areas on and off-shore, which turns</p><p>in severe operation conditions due to the harsh environment that they are exposed to, which</p><p>lead to failure propensity. The wind turbine main components and the failures that they are</p><p>exposed to are discussed as follows (HOSSAIN; ABU-SIADA; MUYEEN, 2018):</p><p> Rotor: it is composed by blades and hub and is propense to failures such as</p><p>asymmetry, fatigue, cracks, superficial roughness increases, stiffness</p><p>reduction and blade deformation.</p><p> Gearbox: it is responsible for the transmission of the velocity of the rotor to</p><p>the generator. It is one of the most critical components of the wind turbines,</p><p>27</p><p>being responsible for a lot of wind turbine downtime. The most usual</p><p>failures in the gearbox include broken gear teeth and bearing failure. These</p><p>failures lead to increase in the bearing temperature and lubricant oil</p><p>temperature, which shows the importance of these parameters to the gearbox</p><p>condition monitoring.</p><p> Main shaft: it connects the drivetrain with the gearbox, having failures</p><p>caused by corrosion, misalignment, cracks and coupling problems, which</p><p>affect the natural rotation of the shaft and leads to a ripple effect in other</p><p>subcomponents.</p><p> Hydraulic system: it is used for mechanical break control, pitch and yaw</p><p>system adjustments, even though there are also some cases of electrical</p><p>hydraulic systems. It is subject of failures such as oil leak and valve</p><p>blocking.</p><p> Mechanical break: it is normally coupled with the main shaft and is used in</p><p>cases of extreme rotation velocity of the rotor or in cases of other component</p><p>failure. The main issue about the mechanical break is the failures in the disk</p><p>break due to stress or overheating, which can be diagnosed by temperature</p><p>and vibration monitoring.</p><p> Tower: the tower that maintains the nacelle in the proper height for energy</p><p>production, it is subject of failures related to structure damages such as</p><p>cracks and corrosion.</p><p> Electric Machine (generator): is the component responsible for the</p><p>conversion of mechanic energy into electricity, therefore, the failures in this</p><p>component can be mechanic or electric related. The electric failures are</p><p>related to open circuits, isolation damage and electric imbalance, while the</p><p>main mechanical failures are shaft damage and imbalance and bearing</p><p>damages.</p><p> Electronic Power Converter: the reliability</p><p>of this component gets more</p><p>complex with the increase in the power of the wind turbines. They are</p><p>subject of failures in the capacitors, circuit board and transistors.</p><p> Sensors: the wind turbine have lots of sensors for continuous parameter</p><p>monitoring such as temperature, voltage and current. Failures related to</p><p>28</p><p>sensors unbalance, physical failures and processing failures can harm the</p><p>performance of the wind turbine.</p><p> Control system: it regulates the wind turbine operation and has failures</p><p>associated with hardware and software. Hardware failures include failures</p><p>in sensors, actuators, controlling boards and communication links, they can</p><p>be detected through model-based methods. The software failures include</p><p>buffer overflow, lack of memory, resource lost and can be detected through</p><p>software diagnostics.</p><p>3.2 Artificial intelligence</p><p>The artificial intelligence is very broad in the field of computer science which</p><p>makes it tricky to be defined since it is composed by different lines of thought. In order to define</p><p>Artificial Intelligence broadly, Russel and Norving (2013) present its definition in 4</p><p>dimensions, as it is presented in the Chart 3.1. The superior part of the chart is dedicated to</p><p>thinking, the inferior part is dedicated to action, the left side is dedicated to mimic the human</p><p>behaviour and the right side is related to rationality or do something right with the available</p><p>information. The approaches centred in the human kind empirical sciences that involve</p><p>hypotheses and experimental confirmation while the rationalist approaches are related to</p><p>combinations between math and engineering.</p><p>Chart 3.1: Artificial intelligence definitions separated by categories.</p><p>Source: Russel and Norving (2013).</p><p>29</p><p>Therefore, the definition of artificial intelligence goes through: (i) Thinking</p><p>humanly: the ability of a machine of thinking as humans and execute activities such as decision</p><p>making, problem resolution and learning. (ii) Acting humanly: the ability of a machine of acting</p><p>like human in a way that humans cannot differ between the machine and a person (Turing test).</p><p>(iii) Thinking rationally: machines with the ability to solve complex problems through logical</p><p>reasoning. (iv) Acting rationally: intelligent action of agents with the capacity of operating</p><p>autonomously, perceive the environment, adapt, create and follow objectives (RUSSEL;</p><p>NORVING, 2013).</p><p>According to Russel and Norving (2013), the first paper acknowledged as being</p><p>part of the artificial intelligence field is the one published by McCulloch and Pittts (1943) that,</p><p>based on the knowledge of the brain neuron behaviour, the Turing computing theory and</p><p>positional logics, presented a model of artificial neurons that, if combined in proper networks,</p><p>could be able to learn.</p><p>The paper of Turing (1950) is considered as the most influent in the field of artificial</p><p>intelligence. It proposes the Turing Test, that evaluate the ability of a machine in expressing</p><p>indistinguishable human behaviour. It is still relevant in artificial intelligence research in the</p><p>XXI century. Moreover, this paper has presented concepts of machine learning, genetic</p><p>algorithms, reinforcement learning and child programming.</p><p>Artificial intelligence was defined as a separate field from other sciences (such as</p><p>mathematics, control, operational research and decision theory) in 1956, at Dartmounth College</p><p>in the US. On that occasion, John McCarthy, Nathaniel Rochester, Claude Shannon and Marvin</p><p>Minsky organized a two-month Artificial Intelligence study seminar called the “Artificial</p><p>Intelligence Summer Research Project”. They gathered a total of 10 scholars on the topic in this</p><p>seminar who came up with the term artificial intelligence and introduced important characters</p><p>from the field (NILSSON, 2009; RUSSEL; NORVING, 2013).</p><p>From then on, there was a period of great enthusiasm in artificial intelligence research</p><p>(1952-1969). McCulloch and Pitts (1943) work has continued in other studies related to neural</p><p>networks, such as the research of Rosenblatt (1962), who created artificial neural networks</p><p>using perceptron. The evolution of general-purpose methods of logical reasoning enabled the</p><p>development of applications such as the Shakey robotics project, in 1966, developed at the</p><p>Stanford Reserch Institute (SRI), being the first project to integrate logical reasoning and</p><p>movement (RUSSEL; NORVING, 2013). In the field of natural language processing, the mini-</p><p>block world created through the SHRDLU program was able to identify commands by phrases</p><p>and execute them by manipulating blocks (RUSSEL; NORVING, 2013; WINOGRAD, 1972).</p><p>30</p><p>After this initial enthusiasm and encouraging prognosis with simple problem-solving</p><p>systems, artificial intelligence research ran into difficulties when they were applied to more</p><p>robust problems, leading governments to reduce incentives for research in the area in the United</p><p>States and the United Kingdom. (RUSSEL; NORVING, 2013).</p><p>This reality changed with the expert systems, consisting of programming aimed at</p><p>solving problems combined with several rules with a specific purpose. As an example, the</p><p>system known as MYCIN that used more than 400 rules in identifying infections. The</p><p>popularization and commercialization of these systems in large corporations made investments</p><p>in expert systems expand in the 1980s, consolidating Artificial Intelligence as an industry.</p><p>Shortly after that, the artificial intelligence market slowed down, in a period known as the AI</p><p>Winter (NILSSON, 2009; RUSSEL; NORVING, 2013).</p><p>In the 80s the neural networks have returned to the academy, as well as the adoption</p><p>of scientific method in studies related to artificial intelligence. There was a separation between</p><p>AI and cognitive science, leading the modern research in neural networks to be separated into</p><p>two fields, one aimed at creating effective network models and understanding their</p><p>mathematical properties, and the other concerned with modeling the properties of real neurons.</p><p>The AI emerged as a science when researchers started to adopt the scientific method in their</p><p>work, studying hypotheses through empirical experiments and analyzing the results</p><p>statistically, allowing replicable experiments. It has also become more common to adopt</p><p>existing theories in different applications of relevance instead of proposing new theories. In</p><p>addition, this period was also marked by the creation of Bayesian networks and the emergence</p><p>of the data-mining industry (RUSSEL; NORVING, 2013).</p><p>In the 1990s, the effort was directed towards the development of intelligent agents,</p><p>they became popular mainly on the internet with the “bots”, search engines, recommendation</p><p>systems and websites. Improvements in sensors, control and planning allowed agents to have</p><p>contact with different fields, such as autonomous cars (RUSSEL; NORVING, 2013)</p><p>In the 21st century, the AI-related studies are more concerned with the abundance</p><p>and quality of data than with the algorithms themselves. This is due to the growth in data</p><p>acquisition and availability, and the discovery that poor algorithms that are executed by a large</p><p>amount of data perform better than robust algorithms with little data (RUSSEL; NORVING,</p><p>2013).</p><p>The growth and popularization of terms such as artificial intelligence, machine</p><p>learning and deep learning makes it difficult for differentiate its concepts. In the Figure 3.3,</p><p>31</p><p>Jeffcock (2018) states that, as discussed earlier, artificial intelligence started around the 50’s,</p><p>and it is defined as any technique that allows computers to mimic human behavior, the figure</p><p>also shows machine learning as a subpart if artificial intelligence and deep learning as a subpart</p><p>of machine learning.</p><p>Figure 3.3: Relationship between AI, ML and deep learning.</p><p>Source: Jeffcock (2018).</p><p>Other</p><p>authors have also tried to map the areas of AI, as in Rosa (2011) and Rowe</p><p>(1988). For them, artificial intelligence includes some areas that fall into:</p><p>• Natural language processing: machines that communicate in human languages,</p><p>understanding what is communicated and generating responses;</p><p>• Artificial vision: machines capable of looking at the environment through</p><p>cameras and recognizing what it is that appears in the image;</p><p>• Logical inference: machines that recognize complicated interrelated facts and</p><p>obtain logical conclusions from them;</p><p>• Planning: machines that plan sequences of actions aiming to achieve goals;</p><p>• Expert systems: machines that, based on complicated rules for various</p><p>situations, are able to help human beings with different tasks.</p><p>• Robotics: machines that move and handle real-world objects.</p><p>According to Kalogirou (2002), generally the term artificial intelligence can be</p><p>defined as the ability of a machine or artifact to perform functions that characterize human</p><p>thought, therefore, in computing, this term has been applied to programs and systems that can</p><p>perform more complex tasks than direct programming, even if it is far from human thinking.</p><p>32</p><p>He further divides AI into two branches with regard to the applications of artificial intelligence</p><p>in renewable energies: neural networks and expert systems.</p><p>Artificial intelligence, as an area of knowledge, seeks to understand human thinking</p><p>in order to develop intelligent and efficient systems for solving complicated problems, reducing</p><p>issues with manual computation. However, while imitating the human brain is a complicated</p><p>task, some areas of AI perform better than that due to the computer's ability to perform</p><p>mathematical calculations millions of times faster than our capacity. Since the beginning of the</p><p>21st century, AI has been applied in various fields such as product design, financial accounting,</p><p>economics, medicine, and is based on several theories of machine learning such as statistical</p><p>learning, neural learning and evolutionary learning, being neural learning the most used in</p><p>several applications (JHA et al., 2017).</p><p>Previously aimed at academic studies, machine learning applications have been</p><p>growing in the industry with the use of artificial intelligence combined with the internet of</p><p>things (IoT), which are driving significant advances in the sector (VESELY, 2017). Thus, when</p><p>it comes to wind energy, the principles of artificial intelligence have been used in several</p><p>applications. Jha et al. (2017), consider that the research of AI applications in wind energy uses</p><p>three main approaches: (i) neural networks, (ii) statistical analysis, (iii) evolutionary learning</p><p>and their combinations as hybrid applications of AI. They also point out that most research</p><p>relating to AI and wind energy focuses on forecasting wind speed and power generation,</p><p>however, areas such as power generation system design, risk optimization, and failure diagnosis</p><p>are also part of the research.</p><p>3.3.1 Artificial intelligence methods and concepts</p><p>In the scope of data science, there is a diversity of methods that are used according</p><p>to the final objective of the study. This section presents the main families of AI methods found</p><p>in the articles selected in the systematic literature review.</p><p>3.3.1 Artificial Neural Networks (ANN)</p><p>In general, Artificial Neural Networks are machines designed for modeling</p><p>particular tasks performed by the human brain. More specifically, they are processors made up</p><p>of simple processing units with a propensity of learning after going through a training process,</p><p>where the connections between neurons, known as synaptic weights, store the knowledge</p><p>acquired from the environment (HAYKIN, 2008).</p><p>33</p><p>These models mimic the structure of neurons for modelling real problems having,</p><p>as similarity with the brain, the ability to acquire knowledge through the learning process</p><p>(BANGALORE, 2014). According to Cabral (2017), artificial neural networks are artificial</p><p>intelligence systems robust to failure, which can learn and present results in real time, and</p><p>therefore have applicability in several branches of research that require data analysis,</p><p>combining statistics with computation. in the optimization of dynamic processes with high</p><p>reliability.</p><p>The main component of artificial neural networks (ANN) is the neuron, which has</p><p>the function of generating an output based on a series of inputs. These inputs are associated</p><p>with weights and the scalar product of this weights and inputs plus the bias is passed through</p><p>an activation function, which determines the output of the neuron, as shown in Figure 3.4</p><p>(HAYKIN, 2008).</p><p>Figure 3.4: Typical neuron model.</p><p>Source: Adapted from Haykin (2008).</p><p>Normally, neural networks are composed of input layer, hidden layer and output</p><p>layer, and the gradient descent method is used to adjust the weights according to training to</p><p>simulate a relationship between inputs and output (WU; SONG, 2016). After that, the neuron</p><p>output is compared with the real truth values in order to calculate the error between them, the</p><p>process can be repeated several times in order to minimize it as much as possible. These</p><p>iterations are called epochs (STRDCZKIEWICZ; BARSZCZ, 2016). There are some types of</p><p>neural networks that vary their input/output ratio according to the network configuration, which</p><p>consists of the number of neurons in their layers and their connections (BANGALORE, 2014).</p><p>When it comes to the process of learning neural network methods, the relationship</p><p>between inputs and outputs is stored in the weights (synapitc weights) and bias (bias), which</p><p>determine the output of each neuron. These weights are determined after the algorithm is</p><p>34</p><p>submitted to a training and test datasets, characterizing the learning process, which can be</p><p>supervised or unsupervised. The supervised learning process requires that the training dataset</p><p>has ground truth data, having an output associated with the inputs, thus establishing a</p><p>relationship between input and output at the end of the learning process. Therefore, supervised</p><p>learning is a process applied for modeling input/output relationship. On the other hand, the</p><p>unsupervised learning process can be carried out without labelled data, being frequently applied</p><p>in data clustering (BANGALORE, 2014).</p><p>According to Haykin (2008), the properties of neural networks have some</p><p>characteristics and advantages:</p><p>• Non-linearity: Neural networks can be linear and non-linear.</p><p>• Input-output mapping: Neural networks are able to map classified datasets</p><p>through supervised learning.</p><p>• Adaptability: Neural networks have the ability to adapt their synaptic weights</p><p>to changes in the environment in which they are exposed to, characterizing</p><p>learning.</p><p>• Evident answer: In the pattern classification, a neural network can be built to</p><p>select between patterns with reliability;</p><p>• Contextual information: knowledge is represented by the entire structure of the</p><p>neural network, with the participation of all neurons globally, offering</p><p>information in the context of the environment that is inserted.</p><p>• Fault tolerance: Neural networks present a moderate degradation in</p><p>performance instead of catastrophic failures;</p><p>• Uniform analysis and design: The same notation is used in all applications</p><p>involving neural networks because neurons are the common component in all</p><p>neural networks. This allows the sharing of theories and learning algorithms</p><p>about neural networks of different configurations. In addition, modular neural</p><p>networks can be built.</p><p>• Analogy with neurobiology: The design of a neural network is inspired by the</p><p>brain behavior.</p><p>Regarding the maintenance of wind turbines, an advantage of applying neural</p><p>networks is that it is required less specific technical information about them. In other</p><p>words,</p><p>neural networks-based methods do not require deep technical knowledge about the component</p><p>to be monitored (BANGALORE et al., 2017; ELHOR et al., 1999). In addition, the methods</p><p>35</p><p>can be applied to a variety of equipment, since there is a large amount of data available, and</p><p>can be applied to several sets of wind turbines (BANGALORE, et al., 2017).</p><p>In this research, neural networks appear in lots of different types such as auto-</p><p>associative neural networks (KREMER, 1992), self-organizing maps neural networks</p><p>(KOHONEN; SOMERVUO, 2002), ANFIS neural networks (CHEN; MATHEWS; TAVNER,</p><p>2013), Recurrent neural networks (SUN; SUN, 2018), Hopfield neural networks (REN; QU,</p><p>2014), Backpropagation neural networks (HAYKIN, 1999), Extreme learning machine neural</p><p>networks (HUANG; DING; ZHOU, 2010), convolutional neural networks (GUO; FU, YANG,</p><p>2018), Auto-Encoder Neural Networks (ZHAO et al., 2018).</p><p>3.3.2 Support Vector Machines (SVM)</p><p>According to Vapnik (1999), Support vector machines are methods that map</p><p>databases in a space of various dimensions and then separate them through optimized plans.</p><p>Thus, they allow an optimal classification of the data, as shown in Figure 3.5. In other words,</p><p>they are machine learning algorithms that use supervised learning to separate data into classes.</p><p>These algorithms are popular in data classification because they are multi-variables robust and</p><p>work well with small datasets, which is a benefit for small amounts of supervised (labelled)</p><p>data. As the complexity of the model can be controlled, these methods generalize well to simple</p><p>and complex problems (REGAN; BEALE; INALPOLAT, 2017).</p><p>Support vector machines work by considering input data as points in a space. After</p><p>that, an optimal plan is defined for separating the data into classes, and a function known</p><p>through the Kernel method is used to transform the input data into a space of many dimensions</p><p>where a decision boundary can be stipulated. Thus, the training process is performed in order</p><p>to find the weights that maximize the margin between the classes and the boundary (JIMÉNEZ</p><p>et al., 2019). Therefore, the objective of SVM is to build a plan or series of plans in a space of</p><p>various dimensions that can classify a dataset, separating it into labels, obtaining better results</p><p>as the distance of the plane to the nearest point the dataset increases (KUSIAK; VERMA,</p><p>2012a).</p><p>36</p><p>Figure 3.5: A plane separating data using SVM</p><p>. Source: (SONG, [S.d.]).</p><p>3.3.3 Decision Trees</p><p>In the scope of Artificial Intelligence, decision trees are supervised learning</p><p>techniques applied in predicting an output based on a series of inputs. In this method, the</p><p>decision tree is built by creating decisions divided into nodes in order to minimize the forecast</p><p>error as much as possible. Thus, at each node, the input variable that most minimizes the error</p><p>is the correct candidate to pass to the subsequent node. Therefore, the forecast happens when</p><p>walking from the root node to the leaf node that delivers the forecast value (MAZIDI;</p><p>TJERNBERG; BOBI, 2017).</p><p>According to Loh and Shih (1997), decision trees are used in classification because</p><p>they facilitate complex decision processes by segregating them into simple decision processes.</p><p>In this method, the classification tree is represented by nodes and branches, where each node</p><p>represents a decision between characteristics, leading to two or more branches. The model is</p><p>trained through input functions and each branch corresponds to a result of the process, forming</p><p>a path from the roots to the leaves of the decision tree, and thus performing the classification</p><p>(JIMÉNEZ et al., 2019).</p><p>An advantage of decision tree methods is the possibility of applying it in non-linear</p><p>relationships between characteristics and classes, thus being a very flexible method (JIMÉNEZ</p><p>et al., 2019; PAL; MATHER, 2003). In the articles analyzed in this work, some authors used</p><p>variations of these algorithms, mainly for modeling and parameter selection.</p><p>37</p><p>3.3.4 Ensemble Learning</p><p>Ensemble learning methods consist in using a series of algorithms together in order</p><p>to build one good single classifier (OPITZ; MACLIN, 1999). It works by training the next</p><p>classifier of the sequence with a resampled training dataset, containing more prediction errors</p><p>made by the previous classifier using bagging or boosting strategies. Some ensemble learning</p><p>algorithms appear in this research such as the Random Forrest Algorithm (KUSIAK, VERMA,</p><p>2011) and Boosting Algorithms (VERMA, KUSIAK, 2012).</p><p>3.3.5 K-nearest neighbours</p><p>The Nearest Neighbour Classifier is one of the simplest classifying techniques in</p><p>the context of machine learning, being a supervised learning approach. It performs the</p><p>classification task by identifying the nearest neighbours to the point example in the space, using</p><p>those neighbours to determine the class of the point. One needs to set the number of neighbours</p><p>that are going to be used for classification. For example, if the number of neighbours is set to</p><p>7, the 7th nearest neighbours are used for classifying the point, which is going to be classified</p><p>as the majority of points with them 7 nearest neighbours Cunningham and Delany (2007).</p><p>3.3.5 Fuzzy Logic</p><p>In artificial intelligence, fuzzy logic tries to imitate human thinking by exploring</p><p>approximate rather than exact models of reasoning, working with variables ranging from 0</p><p>(totally false) to 1 (totally true) instead of binary variables (AZADEGAN et al., 2011). This</p><p>logic is often applied in building Expert Systems. As these types of systems are based on rules</p><p>of human experts, it can contain vague expressions that are often modeled better by fuzzy logic</p><p>than by Boolean logic (ZADEH, 1983).</p><p>3.4 AI Applications for wind turbine maintenance</p><p>According to Hossain, Abu-Siada and Muyeen (2018), regarding the operation and</p><p>maintenance of wind turbines, artificial intelligence can be used to monitor the condition of</p><p>equipment, including methods such as Neural Networks (ANN), expert systems, space vector</p><p>modulations and fuzzy logic systems. They also say that ANN are used to diagnose failure in</p><p>different components of the turbine such as generator, gearbox, bearings and electronics,</p><p>however, they are time-consuming methods and require a large database to cover all possible</p><p>conditions, making them impractical.</p><p>38</p><p>However, the studies found in the SLR of the present work have different</p><p>conclusions. Four literature review articles were selected within the studies and were analysed</p><p>taking the into consideration the insights about the research theme. Marugán et al. (2018)</p><p>presented a literature review about the use of Artificial Neural Networks in the wind energy, in</p><p>order to demonstrate that the method can be a useful alternative for a variety of cases. They</p><p>analysed 190 articles published over a 5-year period and separate them into four main</p><p>categories: Forecasting and prediction, design optimization, fault detection and diagnosis and</p><p>optimal control. Helbin and Ritter (2018) focused on the applications of deep learning for fault</p><p>detection in wind turbines. They separate articles between supervised and unsupervised learning</p><p>approaches, concluding that normal behaviour models using SCADA data can detect faults in</p><p>initial stages in a variety of components. They also infer that deep learning with SCADA data</p><p>has been applied successfully in tests, however, it is still not much used. Bermejo et al. (2019)</p><p>highlighted articles that used Artificial Neural Networks for energy and reliability prediction in</p><p>solar, hydraulic and wind energy. They state that recent research about wind turbine condition</p><p>monitoring aim more at individual components than the whole wind turbine, they also explain</p><p>the urge to determine the criticality of components</p>