Prognostic_condition_monitoring_for_wind_turbine_drivetrains_via_generator_current_analysis

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<p>Chinese Journal of Electrical Engineering, Vol.4, No.3, September 2018</p><p>Prognostic Condition Monitoring for Wind Turbine</p><p>Drivetrains via Generator Current Analysis</p><p>Wei Qiao*, and Liyan Qu</p><p>(Power and Energy Systems Laboratory, Department of Electrical and Computer Engineering,</p><p>University of Nebraska-Lincoln, Lincoln, NE 68588-0511, USA)</p><p>Abstract: Maintenance costs account for a significant portion of the total cost of electricity</p><p>generated by wind turbines. Currently in the wind power industry, maintenance is mainly performed on</p><p>regular schedules or when significant damage occurs in a wind turbine making it inoperable, instead of</p><p>being determined by the actual condition of the wind turbine. Among the total maintenance costs,</p><p>approximately 25%~35% is related to regularly scheduled preventive maintenance and 65%~75% to</p><p>unscheduled corrective maintenance. To reduce the failure rate and level and maintenance costs and</p><p>improve the availability, reliability, safety, and lifespans of wind turbines, it is desirable to perform</p><p>condition-based predictive maintenance for wind turbines, which will require a high-fidelity online</p><p>prognostic condition monitoring system(CMS) for fault diagnosis and prognosis and remaining useful</p><p>life(RUL) prediction of wind turbines. Most of the existing wind turbine CMSs are based on vibration</p><p>monitoring and have no or limited capability in fault prognosis and RUL prediction. Compared to</p><p>vibration monitoring, the prognostic condition monitoring techniques based on generator current signal</p><p>analysis proposed recently have significant advantages in terms of cost, hardware complexity,</p><p>implementation, and reliability. This paper discusses the principles and challenges of using generator</p><p>current signals for prognostic condition monitoring of wind turbine drivetrains and presents an</p><p>overview of recent advancements in this area.</p><p>Keywords: Current signal, drivetrain, fault diagnosis, fault prognosis, prediction, prognostic</p><p>condition monitoring, remaining useful life(RUL), wind turbine.</p><p>1 Introduction</p><p>Compared to the turbine generator systems spinning</p><p>in the traditional thermal and hydro power plants,</p><p>wind turbines have relatively higher failure rates due to</p><p>the highly varying operating conditions and harsher</p><p>environments, and therefore, require more frequent</p><p>inspection and maintenance. Currently, in the wind</p><p>power industry, maintenance is primarily performed</p><p>based on predetermined schedules(i.e., preventive</p><p>maintenance) or to repair or replace failed parts(i.e.,</p><p>corrective maintenance). In practice, many preventive</p><p>maintenance actions are not necessary because the wind</p><p>turbines are still in healthy condition. On the other hand,</p><p>to reduce maintenance costs, it is desirable to perform</p><p>maintenance in a timely manner before a small failure</p><p>turns into a large or catastrophic failure. For instance,</p><p>the failure of a $1,500 bearing, if not repaired or</p><p>replaced in time, could result in a $100,000 gearbox</p><p>replacement, a $50,000 generator rewind, and $70,000</p><p>in expenses to replace other failed parts[1]. According to</p><p>a General Electric(GE) report[2], a $5,000 bearing</p><p>replacement can turn into a $250,000 project that</p><p>involves cranes, a service crew, gearbox replacement,</p><p>and generator rewind, not including the loss of</p><p>electricity generation during downtime.</p><p>Since wind turbines are usually situated on high</p><p>towers, installed in remote areas, and distributed over</p><p>large geographic regions, the attendance for maintenance</p><p>is restricted by factors such as the availability of</p><p>manpower, parts, equipment, and other required resources</p><p>as well as weather conditions. For example, no</p><p>maintenance actions can be performed for wind turbines</p><p>during bad weather conditions such as storms, snows,</p><p>high tides, etc. Possible inaccessibility in certain periods</p><p>of the year can prevent any maintenance actions for a</p><p>long time, e.g., several weeks, which can cause a</p><p>significant loss of electricity production during downtime.</p><p>Therefore, it is expensive to perform maintenance for</p><p>wind turbines. It was reported by [3-5] that the operation</p><p>and maintenance(O&M) costs for onshore and offshore</p><p>wind turbines are on the order of 10%~15% and</p><p>20%~35%, respectively, of the total cost of the</p><p>generated electricity and increase with the age of the</p><p>wind turbine. Out of the total maintenance costs,</p><p>approximately 25%~35% is related to preventive</p><p>maintenance and 65%~75% to corrective maintenance.</p><p>To make wind energy more cost effective, it is critical to</p><p>reduce the maintenance costs and downtime of wind</p><p>turbines.</p><p>An effective means to reduce the maintenance costs</p><p>and downtime of wind turbines is to perform</p><p>condition-based predictive maintenance before a failure</p><p>occurs or starts to affect the operating performance or</p><p>safety of the wind turbine. To achieve condition-based</p><p>predictive maintenance, the health conditions of wind</p><p>turbines need to be monitored and prognosed online</p><p>continuously by a condition monitoring system(CMS).</p><p>* Corresponding Author, E-mail: wqiao3@unl.edu.</p><p>This work was supported in part by the Office of Energy Efficiency</p><p>and Renewable Energy (EERE), U.S. Department of Energy under</p><p>Awards Number DE-EE0006802 and DE-EE0001366, and in part by</p><p>the U.S. National Science Foundation under Grant ECCS-1308045.</p><p>W. Qiao et al.: Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis 81</p><p>The existing CMSs are typically equipped with dedicated</p><p>sensors and data acquisition devices to acquire the data</p><p>that contains the information on the health condition of</p><p>the wind turbines and dedicated signal processing</p><p>algorithms to extract the information (features) that are</p><p>related to possible failures of the wind turbines from the</p><p>acquired data. The information extracted is then used to</p><p>perform fault diagnosis to detect, locate, and identify</p><p>occurring faults and monitor the development of the</p><p>faults from defects (i.e., incipient faults) into failures.</p><p>Alarms will be triggered when failures occur and</p><p>corrective maintenance actions are needed[6-7]. Future</p><p>CMSs are also expected to perform fault prognosis to</p><p>predict the development of a defect into a failure, when</p><p>the failure occurs, and the remaining useful life (RUL)</p><p>of the wind turbine component with the defect[6].</p><p>In the last decade, significant research has been</p><p>conducted to develop technologies for condition</p><p>monitoring of wind turbines, with the focus on blades</p><p>and drivetrain components, including bearings, shafts,</p><p>gearbox, and generator, because the majority of the</p><p>maintenance costs and downtime are caused by failures</p><p>of drivetrain components and blades[6-7]. A variety of</p><p>signals, such as vibration, acoustic, strain, torque,</p><p>temperature, lubrication oil parameter, electrical, and</p><p>supervisory control and data acquisition system (SCADA)</p><p>signals have been studied to monitor wind turbine</p><p>health conditions[7]. Since the faults in wind turbine</p><p>drivetrains usually lead to vibrations of the shafts,</p><p>vibration signals are most commonly used for condition</p><p>monitoring of wind turbine drivetrain components[7]. For</p><p>example, the majority of the commercially available</p><p>CMSs mainly use vibration signals measured from</p><p>key drivetrain components, such as gearbox, main</p><p>bearing, and generator[8]. The vibration-based condition</p><p>monitoring technologies have matured and been</p><p>standardized by ISO 10816, which provides guidance for</p><p>evaluating vibration severity in machines operating in</p><p>the 10~200Hz(600~12,000r/min) frequency range[9-10].</p><p>However, vibration-based condition monitoring requires</p><p>installation of additional sensors and data acquisition</p><p>devices, which can be expensive and intrusive to the</p><p>wind turbines. In addition, the installed vibration sensors</p><p>can cause additional issues to the reliability of the</p><p>wind turbines when the sensors fail. Moreover, the</p><p>effectiveness and accuracy of vibration-based condition</p><p>monitoring techniques are affected by sensor locations</p><p>and are easily contaminated by environmental noise[11].</p><p>Compared to vibration signals, the use of electrical</p><p>(voltage/current) signals measured from generator</p><p>terminals for condition monitoring of wind turbine</p><p>drivetrains has following benefits: First, electrical</p><p>signals are already used in wind turbine generator</p><p>control systems, and therefore, no additional hardware</p><p>cost for sensors or data acquisition devices is needed.</p><p>Moreover, electrical-signal-based methods are reliable,</p><p>easily accessible, and nonintrusive to the wind turbines</p><p>because electrical signals can be collected at a</p><p>convenient location, such as the bottom of the tower.</p><p>Electrical signals have been used to detect generator</p><p>electrical and mechanical faults in wind turbines[12-19]. In</p><p>addition, the output electric power of wind turbines has</p><p>been used for detecting blade faults as well as</p><p>mechanical faults in the drivetrain components other</p><p>than generators[20-23]. The use of electric power needs the</p><p>measurements of both currents and voltages of the</p><p>generator. Other than electric power, the work [24-26]</p><p>used only generator voltage or current signals for wind</p><p>turbine drivetrain condition monitoring.</p><p>However, there are challenges of using electrical</p><p>signals for condition monitoring of wind turbine</p><p>drivetrains. First, the fault signatures in electrical signals</p><p>have low signal-to-noise ratios (SNRs), where signal is</p><p>referred to as the fault signatures contained in the</p><p>electrical measurements and noise is referred to as the</p><p>dominant components of the electrical measurements</p><p>that are irrelevant to faults. This is due to modulation</p><p>with the fundamental frequencies and other harmonic</p><p>components of the electrical signals which are usually</p><p>nonstationary and noisy. Moreover, online condition</p><p>monitoring requires continuous online processing of</p><p>large amounts of low-SNR nonstationary data, which</p><p>poses high requirements on the computational efficiency</p><p>and resolution of the signal processing algorithms used</p><p>for condition monitoring. None of the previous work has</p><p>sufficiently addressed these challenges. Finally, there</p><p>was no previous work on fault prognosis and RUL</p><p>prediction of wind turbines using electrical signals.</p><p>Recently, significant advancements have been made</p><p>in using generator current signals for prognostic condition</p><p>monitoring, including fault diagnosis and prognosis</p><p>and RUL prediction, of wind turbine drivetrains under</p><p>nonstationary operating conditions[27-43]. This paper</p><p>provides an overview of these advancements. The rest of</p><p>the paper is organized as follows: Section 2 presents</p><p>the principles of using generator current signals for</p><p>prognostic condition monitoring of wind turbine</p><p>drivetrains. Then, the challenges of using current signals</p><p>for prognostic condition monitoring of wind turbines are</p><p>discussed in Section 4. Section 5 presents a prognostic</p><p>CMS that uses generator current signals for fault</p><p>diagnosis and prognosis and RUL prediction of wind</p><p>turbine drivetrains and the major signal processing</p><p>algorithms used in the CMS. Section 5 provides the</p><p>validation results for the current-based prognostic</p><p>condition monitoring algorithms. Finally, Section 6</p><p>presents concluding remarks and recommendations for</p><p>current-based prognostic condition monitoring methods.</p><p>2 Principles of using generator current</p><p>signals for prognostic condition monitoring</p><p>of wind turbine drivetrains</p><p>A fault in a wind turbine drivetrain component</p><p>usually induces vibrations of the shafts at certain</p><p>frequencies fF,1, , fF,M, which are called the fault</p><p>characteristic frequencies in vibration and are usually</p><p>proportional to the rotating frequencies of the shafts.</p><p>Due to mechanical couplings between generator and</p><p>failed drivetrain component(s) as well as electromagnetic</p><p>coupling between generator rotor and stator, the shaft</p><p>vibrations at the fault characteristic frequencies</p><p>modulate the frequency and amplitude of generator</p><p>stator/rotor current signals. Thus, a wind turbine</p><p>82 Chinese Journal of Electrical Engineering, Vol.4, No.3, September 2018</p><p>generator current signal i(t) can generally be expressed</p><p>as follows.</p><p>( ) ( )sin 2 ( ) ( )k k k</p><p>k</p><p>i t I t f t t t</p><p>   </p><p></p><p></p><p>, , ,</p><p>1</p><p>( )sin 2 ( ) ( )</p><p>M</p><p>k j F j k j</p><p>j</p><p>I t f t t t</p><p></p><p>    </p><p></p><p> (1)</p><p>where k is the harmonic number; Ik(t), fk(t), and φk(t) are</p><p>the amplitude, frequency, and initial phase of the kth</p><p>harmonic component, respectively; Ik, j(t), fF, j(t), and</p><p>φk, j(t) are the amplitude, frequency, and initial phase</p><p>of the jth fault characteristic frequency in vibration</p><p>that modulates the frequency of the current signal,</p><p>respectively. The values of Ik(t), fk(t), Ik, j(t), and fF, j(t)</p><p>can be time varying due to the nonstationary shaft</p><p>rotating speed of the wind turbine.</p><p>Due to the amplitude modulation of the current</p><p>signal by the fault-induced vibrations, Ik(t) can be</p><p>expressed as follows.</p><p>, , ,</p><p>1</p><p>( ) ( ) ( )sin 2 ( )d</p><p>M</p><p>k w k k j F j</p><p>j</p><p>I t I t I t f t t</p><p></p><p>     (2)</p><p>where Iw,k(t) is the amplitude of the kth harmonic of the</p><p>original current signal without modulation; and Ik, j(t)</p><p>and fF, j(t) are the amplitude and frequency of the jth fault</p><p>characteristic frequency component in vibration that</p><p>modulates the current signal.</p><p>As the result of the frequency modulation, each</p><p>fault characteristic frequency in vibration fF, j(t) in (1)</p><p>will become an infinite number of sidebands around the</p><p>harmonic frequency fk(t) in the current signal. Therefore,</p><p>(1) can be rewritten as follows.</p><p>,</p><p>1</p><p>( ) ( )sin 2 ( ) ( ) ( )</p><p>M</p><p>k k F j k</p><p>k j m</p><p>i t I t f t mf t t t</p><p></p><p>        </p><p>    </p><p> </p><p>(3)</p><p>where m is an integer, indicating that the sidebands</p><p>occur at multiples of the fault characteristic frequency in</p><p>vibration fF, j(t) ( j=1, , M) away from the harmonic</p><p>frequency fk(t).</p><p>Wind turbines are mainly equipped with two types</p><p>of generators: permanent magnet synchronous generators</p><p>(PMSGs) and doubly-fed induction generators(DFIGs).</p><p>PMSGs only have stator current signals, for which the</p><p>harmonic frequency fk(t) in (3) is proportional to the</p><p>mechanical shaft rotating frequency fr(t) of the wind</p><p>turbine. Since the fault characteristic frequencies in</p><p>vibration fF, j(t) ( j = 1, , M ) are also proportional to the</p><p>shaft rotating frequency, (3) can be rewritten as follows</p><p>for the wind turbines equipped with PMSGs.</p><p>( ) ( )sin 2 ( ) ( ) ( )k k k k</p><p>k n</p><p>i t I t f t nf t t t</p><p>        </p><p>    </p><p>  (4)</p><p>where n is a real number. Equation (4) indicates that the</p><p>distances between each harmonic frequency and the fault</p><p>characteristic frequencies, which appear as sidebands</p><p>around the harmonic frequency, are proportional to the</p><p>harmonic frequency.</p><p>DFIGs have both stator and rotor current signals.</p><p>For a DFIG stator current signal, the kth harmonic</p><p>frequency fk(t) in (3) is nearly a fixed value because the</p><p>stator of the DIFG is connected to the power grid. For a</p><p>DFIG rotor current signal, the kth harmonic frequency</p><p>fk(t) in (3) can be expressed as follows.</p><p>fk(t) = k [ fs  pfr(t)] (5)</p><p>where fs is the grid frequency, which is a fixed value;</p><p>fr(t) is the mechanical shaft rotating frequency of the</p><p>DFIG, which is usually time-varying; and p is the</p><p>number of pole pairs of the DFIG.</p><p>Equations (1)~(5) indicate that both the amplitude</p><p>and frequency of the generator current signals contain</p><p>the information related to faults (i.e., the fault</p><p>characteristic frequencies in vibration) in wind turbine</p><p>drivetrains. Therefore, according to the locations</p><p>and amplitudes of the fault characteristic frequency</p><p>components extracted from current signals, fault</p><p>diagnosis (i.e., fault detection, fault type identification,</p><p>and fault level evaluation) can be performed. In practice,</p><p>since amplitudes of the fault-induced sidebands around</p><p>the fundamental frequency are much</p><p>large than those</p><p>around the harmonics, only k = 1 in (1)~(5) is</p><p>considered for prognostic condition monitoring.</p><p>3 Challenges of using generator current</p><p>signals for prognostic condition monitoring</p><p>of wind turbine drivetrains</p><p>Although the vibration(s) induced by a fault in a</p><p>wind turbine drivetrain component are transmitted to the</p><p>generator and therefore produce signatures in generator</p><p>current signals, the energy of the fault signatures in a</p><p>current signal is weaker than that in a vibration signal</p><p>measured from a vibration sensor near the fault location.</p><p>This is caused by the energy loss when transmitting</p><p>the vibration through the shaft(s) of the drivetrain.</p><p>Therefore, the SNR using generator current signals for</p><p>condition monitoring is lower than that of using</p><p>vibration signals.</p><p>Additionally, as shown in (1)~(4), a single fault</p><p>characteristic frequency in vibration becomes multiple/</p><p>many or even an infinite number of fault characteristic</p><p>frequencies in current due to frequency and amplitude</p><p>modulations. As a result, the total energy of a certain</p><p>fault characteristic frequency component in vibration</p><p>will be dispersed into multiple/many or even an infinite</p><p>number of fault characteristic frequencies in current,</p><p>which further reduces the SNR significantly for fault</p><p>diagnosis.</p><p>Moreover, the fault characteristic frequencies in</p><p>current signals are time-varying due to nonstationary</p><p>shaft rotating speeds of the wind turbine drivetrains.</p><p>Therefore, when the samples of a current signal acquired</p><p>with the equal time interval within a time window are</p><p>analyzed in the frequency domain via frequency</p><p>spectrum analysis, such as the fast Fourier transform</p><p>(FFT), the fault characteristic frequencies will overlap</p><p>with each other and with the dominant frequencies of the</p><p>current signal that are irrelevant to faults or even spread</p><p>W. Qiao et al.: Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis 83</p><p>over a wide range of the frequency spectrum. As a result,</p><p>it will not be able to extract the nonstationary fault</p><p>signatures(i.e., fault characteristic frequencies) directly</p><p>from the frequency spectrum of the current signal.</p><p>Due to the low SNR and nonstationary fault</p><p>signatures caused by nonstationary operations of wind</p><p>turbines, it is very challenging to use generator current</p><p>signals for prognostic condition monitoring of wind</p><p>turbine drivetrains. To solve these challenges, advanced</p><p>signal processing algorithms are needed.</p><p>4 Current-based prognostic condition</p><p>monitoring</p><p>This section presents a prognostic CMS that uses</p><p>generator current signals for fault diagnosis and</p><p>prognosis and RUL prediction of wind turbine</p><p>drivetrains under all operating conditions. Fig.1</p><p>illustrates the schematic of the prognostic CMS, which</p><p>consists of several functional modules, including signal</p><p>conditioning, fault feature extraction, fault diagnosis,</p><p>fault prognosis, RUL prediction, alarm management, and</p><p>equipment management. The conditioned current signal</p><p>can also be used to assist other signals, such as vibration</p><p>signals, for condition monitoring of the wind turbine</p><p>drivetrains under nonstationary operating conditions.</p><p>The current-based prognostic CMS has great potential to</p><p>enable condition-based predictive maintenance for wind</p><p>turbine drivetrains. The reminder of this section will</p><p>present the major signal processing algorithms used for</p><p>signal conditioning, fault feature extraction, fault</p><p>diagnosis, fault prognosis, and RUL prediction in the</p><p>prognostic CMS.</p><p>4.1 Signal conditioning: demodulation</p><p>As discussed in Section 2, shaft vibrations induced</p><p>by faults in wind turbine drivetrain components</p><p>modulate amplitude and frequency of generator current</p><p>signals. Although the modulated current signals contain</p><p>fault-related information, it is hard to extract fault</p><p>signatures from the signals directly for fault diagnosis</p><p>due to the modulation. To solve this problem, amplitude/</p><p>frequency demodulation is usually performed as part</p><p>of signal conditioning to facilitate the fault feature</p><p>extraction[27-31].</p><p>The objective of amplitude demodulation is to</p><p>extract the instantaneous amplitude or envelope of a</p><p>signal, which can be achieved by using the square law[27],</p><p>Hilbert transform[28-29], etc. The Hilbert transform of a</p><p>current signal i(t), denoted by H[i(t)], can be expressed</p><p>as:</p><p>1 ( )[ ( )] diH i t</p><p>t</p><p> </p><p></p><p></p><p></p><p></p><p>  (6)</p><p>Then, the envelope of the current signal, which is</p><p>denoted as I(t), is defined as:</p><p>2 2( ) [ ( )] { [ ( )]}I t i t H i t  (7)</p><p>The objective of frequency demodulation is to</p><p>demodulate the fault characteristic frequencies from the</p><p>frequency of the current signal. A simple method for</p><p>frequency demodulation is the phase locked loop(PLL)</p><p>algorithm[27]. Another method is based on the detection</p><p>of the envelope of the instantaneous frequency of the</p><p>current signal. The instantaneous phase (t) of a current</p><p>signal i(t) can be estimated as follows[30-31].</p><p>( )( ) arctan</p><p>( )</p><p>i tt</p><p>I t</p><p></p><p> </p><p>  </p><p> </p><p>(8)</p><p>where I(t) is the instantaneous amplitude of the current</p><p>signal and can be estimated by (7) using the Hilbert</p><p>transform. Then, the instantaneous frequency of the</p><p>current signal, f(t), can be calculated by the time</p><p>derivative of the phase.</p><p>1 d[ ( )]( )</p><p>2 d</p><p>tf t</p><p>t</p><p></p><p></p><p></p><p>(9)</p><p>Finally, the envelope of f(t), which contains the fault</p><p>characteristic frequencies, is extracted by using an</p><p>envelope detection method.</p><p>4.2 Signal conditioning: angular/synchronous</p><p>resampling</p><p>As discussed in Sections 2 and 3, when the wind</p><p>turbines operate in varying-speed conditions, and as</p><p>such the fault characteristic frequencies in current</p><p>signals are time- varying. Since the current signals used</p><p>in wind turbine generator control systems are usually</p><p>sampled at equal time intervals, the fault characteristic</p><p>frequencies contained in the frequency spectra of the</p><p>Fig.1 Schematic of the current-based prognostic CMS for wind turbine drivetrains</p><p>84 Chinese Journal of Electrical Engineering, Vol.4, No.3, September 2018</p><p>current signals and their amplitudes and frequencies</p><p>overlap with each other and with the dominant</p><p>frequencies that are irrelevant to faults. Therefore, it is</p><p>difficult to extract the nonstationary fault characteristic</p><p>frequencies directly from the frequency spectra of the</p><p>current signals or their amplitude or frequency signals.</p><p>The traditional way to extract nonstationary fault</p><p>signatures from nonstationary signals is using time-</p><p>frequency analysis techniques, such as short-term</p><p>Fourier transform(STFT), wavelet transform, Hilbert-</p><p>Huang transform[32], etc. However, the frequency</p><p>resolution of these methods may not be sufficient for</p><p>wind turbine drivetrain fault diagnosis using generator</p><p>current signals. Moreover, some of these methods have</p><p>high computational costs[32] and, therefore, may not be</p><p>suitable for online condition monitoring.</p><p>To solve this problem, several angular or</p><p>synchronous resampling methods have been proposed to</p><p>resample a signal with a constant time increment to</p><p>become a signal with a constant phase increment with</p><p>respect to a shaft rotating frequency of the wind</p><p>turbine[30-31,33-34]. As a consequence, the frequency</p><p>components that are proportional to the shaft rotating</p><p>frequency, such as the characteristic frequencies of the</p><p>faults in wind turbine drivetrains, will become constant</p><p>values in the frequency spectra of the resampled current</p><p>signals and their amplitude and frequency signals</p><p>obtained via demodulation. In this way, the energy of</p><p>fault signatures that distributes over a wide frequency</p><p>range in the spectrum of the original current signal will</p><p>concentrate at single locations in the frequency spectrum</p><p>of the resampled current signal. This will significantly</p><p>increase the SNR to facilitate fault diagnosis. Therefore,</p><p>the FFT can be applied directly to generate the frequency</p><p>spectrum for the resampled current signal or its amplitude</p><p>or frequency signal, from which the fault characteristic</p><p>frequencies can be easily identified and extracted.</p><p>4.3 Signal conditioning: multiscale filtering</p><p>In some circumstances, generator current signals</p><p>may contain frequencies that are irrelevant to shaft</p><p>rotating frequencies. For example, the fundamental</p><p>frequency of the stator current signals of a DFIG is the</p><p>same as the grid frequency, which is independent of the</p><p>shaft rotating frequencies of the wind turbine drivetrain.</p><p>Another example is that generator current signals may</p><p>contain frequency components caused by tower</p><p>vibrations, which are non-proportional to the shaft</p><p>rotating frequencies of wind turbine drivetrains. In these</p><p>cases, if angular resampling is applied to the current</p><p>signals, the nonstationary fault characteristic frequencies</p><p>will become constant values; however, the frequencies</p><p>that are irrelevant to shaft rotating frequencies will</p><p>become nonstationary and, therefore, may mask the</p><p>constant fault characteristic frequencies in the spectra of</p><p>resampled current signals, leading to a spectrum</p><p>smearing problem. Therefore, angular/ synchronous</p><p>resampling is not an effective signal conditioning</p><p>method in these cases.</p><p>To solve the spectrum smearing problem of the</p><p>current signals containing frequencies that are irrelevant</p><p>to shaft rotating frequencies, a method based on</p><p>multiscale filtering spectrum(MFS)[35] was developed. In</p><p>the MFS method, the instantaneous shaft rotating</p><p>frequency of the wind turbine is first estimated from a</p><p>generator current signal. Then, a multiscale filter bank</p><p>based on a Vold-Kalman filter is designed according to</p><p>the center frequencies corresponding to the shaft rotating</p><p>frequency at different scales. The mono-component</p><p>signals whose frequencies are continuous multipliers of</p><p>the shaft rotating frequency are subsequently extracted</p><p>from the envelope of the measured current signal.</p><p>Finally, a weighted energy spectrum is constructed</p><p>within the selected frequency range, from which possible</p><p>fault characteristic frequencies can be identified.</p><p>4.4 Fault feature extraction and fault detection</p><p>After signal conditioning, the fault signatures are</p><p>usually extracted from a frequency spectrum, such as the</p><p>power spectral densit(PSD), of the conditioned current</p><p>signal. In the PSD, the magnitude at a certain frequency</p><p>represents the energy of the time-domain signal at the</p><p>frequency. If the signal has high energy at a certain</p><p>frequency, it will generate an impulse at that frequency</p><p>in the PSD spectrum of the signal. An impulse detection</p><p>algorithm has been proposed to extract the impulses in</p><p>the PSD spectra of conditioned current signals[30]. If</p><p>one or multiple extracted impulses are located at fault</p><p>characteristic frequencies, it indicates the occurrence of</p><p>faults.</p><p>However, some wind turbine drivetrain faults, such</p><p>as gear defects in gearboxes, cannot be simply</p><p>detected by investigation of the impulses at the fault</p><p>characteristic frequencies, because these impulses</p><p>also occur when the gears have no fault[33]. To solve</p><p>this problem, statistical features of these impulses[33]</p><p>and other fault features extracted from original or</p><p>conditioned current signals, such as noise-to-signal</p><p>ratio(NSR), root-mean-square(RMS) values, Kurtosis,</p><p>maximum values, etc. are also used; and fault diagnosis</p><p>may need to be performed via pattern classification</p><p>using computational intelligence and machine learning</p><p>methods[28,36].</p><p>4.5 Fault prognosis and RUL prediction</p><p>When a defect or fault occurs, it is important to</p><p>predict the future condition(fault prognosis) and the</p><p>RUL of the failing component in order to achieve</p><p>condition-based predictive maintenance. Recently,</p><p>statistical particle-filtering-based fault prognostic and</p><p>RUL prediction methods for wind turbine drivetrains</p><p>have been developed[42-43]. Fig.2 illustrates the schematic</p><p>of the method proposed in [42]. It consists of</p><p>four functional modules: signal conditioning, feature</p><p>extraction, fault prognosis, and RUL prediction. The</p><p>signal processing methods used in the signal conditioning</p><p>module have been discussed in Sections 4.1~4.3. The</p><p>signal conditioning module solves the problems of low</p><p>SNRs of and nonstationary fault signatures in generator</p><p>current signals. Then, the PSD of the conditioned current</p><p>signal is calculated and the fault-related feature is</p><p>extracted from the PSD spectrum. In the fault prognosis</p><p>module, an adaptive neuro-fuzzy inference system</p><p>W. Qiao et al.: Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis 85</p><p>(ANFIS) model is designed to learn the state transition</p><p>function of the extracted fault feature. After that, a</p><p>particle filtering algorithm is designed to predict the</p><p>fault feature based on the state transition function learned</p><p>by the ANFIS and the weights of particles. When new</p><p>data of the fault feature is available, the weights of the</p><p>particles are updated. Finally, the RUL of failing</p><p>component is predicted at different time instants, where</p><p>RUL is the time between now and the moment when the</p><p>predicted fault feature reaches a threshold, which can be</p><p>determined using the existing failure database.</p><p>4.6 Current-aided vibration monitoring</p><p>Generator current signals have also been used to aid</p><p>other signals, such as vibration signals, for prognostic</p><p>condition monitoring of wind turbine drivetrains. A key</p><p>issue in the vibration monitoring for variable-speed wind</p><p>turbine drivetrains is the elimination of the effect of the</p><p>shaft speed variations in the vibration signals measured</p><p>under varying rotating speed conditions. The work [31]</p><p>proposed a current-aided vibration order tracking</p><p>method for bearing fault diagnosis of variable-speed</p><p>wind turbines, as shown in Fig.3. The method eliminated</p><p>the need for a rotor position/speed sensor to measure</p><p>Fig.2 Block diagram of the fault prognostic and</p><p>RUL prediction method[42]</p><p>Fig.3 Flowchart of the current-aided vibration order</p><p>tracking method[31]</p><p>the reference signal for vibration order tracking. A</p><p>simple and effective algorithm was developed in that</p><p>work to acquire the reference signal from a generator</p><p>stator current signal. First, the generator shaft rotating</p><p>frequency is calculated from the instantaneous</p><p>fundamental frequency of the current signal, which is</p><p>estimated by using a time-frequency distribution method.</p><p>Then, the shaft phase-time relationship is established.</p><p>With this information, the envelope of the vibration</p><p>signal that was recorded synchronously with the current</p><p>signal is resampled with equal phase intervals. Finally,</p><p>bearing fault diagnosis is performed by observing the</p><p>impulses at bearing characteristic frequencies in the</p><p>power spectrum of the resampled vibration envelope</p><p>signal.</p><p>5 Validation results</p><p>The generator-current-based prognostic condition</p><p>monitoring methods have been validated on a variety of</p><p>laboratory wind turbine drivetrain test rigs, laboratory</p><p>wind turbines, and field wind turbines of different types</p><p>and sizes from hundreds of Watts to 1.6MW. Some</p><p>typical results are presented in the rest of this section to</p><p>demonstrate the effectiveness and superiority of some of</p><p>these methods.</p><p>5.1 Signal conditioning: demodulation</p><p>An Air Breeze wind turbine with a failed bearing</p><p>was tested. The turbine rotor is connected to the rotor of</p><p>the generator(a PMSG) directly without a gearbox. The</p><p>failed bearing was located between the rotors of the</p><p>turbine and the PMSG. The PMSG has six pole pairs.</p><p>The wind turbine operated with a varying shaft rotating</p><p>speed during the test due to the varying wind speed. One</p><p>phase stator current of the generator was recorded via a</p><p>Fluke 80i-110s AC/DC current clamp. The measured</p><p>current signal was collected by a National Instrument(NI)</p><p>data acquisition system with a sampling frequency of</p><p>10kHz. The failed bearing</p><p>has a broken cage, as shown</p><p>in Fig.4 in comparison with the new bearing[27].</p><p>Fig.5 shows the power spectrum of the original</p><p>current signal for the wind turbine with the failed bearing.</p><p>According to (3), the bearing cage fault characteristic</p><p>frequencies around the fundamental frequency f1 of the</p><p>current signal are f1 ± mfF (m=1,2, ∙∙∙.), where fF is the</p><p>bearing cage fault characteristic frequency in vibration.</p><p>Both f1 and fF vary in proportion with the shaft rotating</p><p>frequency of the wind turbine. Due to the high</p><p>magnitude of the stator current fundamental frequency</p><p>(a) New bearing (b)Failed bearing with a broken cage[27]</p><p>Fig.4 Test bearing</p><p>86 Chinese Journal of Electrical Engineering, Vol.4, No.3, September 2018</p><p>Fig.5 Power spectrum of the original generator stator</p><p>current signal</p><p>component and the varying shaft rotating frequency, the</p><p>impulses generated by the bearing fault are totally</p><p>masked by the stator current fundamental frequency</p><p>component in the power spectrum of the original current</p><p>signal and, therefore, cannot be identified for fault</p><p>diagnosis.</p><p>To solve this problem, demodulation is performed</p><p>for the current signal. Fig.6 shows the power spectrum</p><p>of the current frequency demodulated signal. The</p><p>characteristic frequencies of the bearing cage fault</p><p>distribute over a wide frequency range due to the</p><p>varying shaft rotating frequency and can barely be</p><p>identified in the power spectrum. Thus, it is difficult to</p><p>detect the broken bearing cage fault because the</p><p>magnitudes of the impulses at the fault characteristic</p><p>frequencies are too small.</p><p>5.2 Signal conditioning: angular/synchronous resam-</p><p>pling</p><p>To further solve the problem of nonstationary</p><p>fault characteristic frequencies, angular/synchronous</p><p>resampling is applied to the current frequency and</p><p>amplitude demodulated signals. In the resampling</p><p>process, the varying shaft rotating frequency of the wind</p><p>turbine was converted to a constant value of 10Hz.</p><p>Figs.7 and 8 compare the power spectra of the resampled</p><p>current frequency and amplitude demodulated signals,</p><p>respectively, for the healthy bearing case and the bearing</p><p>cage fault case[27]. In the healthy bearing case, no</p><p>impulse appears in the range from 2Hz to 9Hz in Fig.7(a)</p><p>or 8(a). However, an impulse appears at 3.95Hz in the</p><p>power spectra of the demodulated signals in the bearing</p><p>cage fault case, as shown in Figs.7(b) and 8(b). This</p><p>impulse frequency is almost the same as the calculated</p><p>characteristic frequency of the bearing cage fault with</p><p>the shaft rotating frequency fr at 10Hz. In addition, the</p><p>3.95Hz bearing fault characteristic frequency and the</p><p>Fig.6 Power spectrum of the current frequency</p><p>demodulated signal</p><p>(a) A healthy bearing</p><p>(b) A broken bearing cage fault</p><p>Fig.7 Comparison of the power spectra of the current</p><p>frequency demodulated signals for the wind turbine with</p><p>different bearing</p><p>(a) A healthy bearing</p><p>(b) A broken bearing cage fault</p><p>Fig.8 Comparison of the power spectra of the current</p><p>amplitude demodulated signals for the wind turbine with</p><p>different bearing</p><p>shaft rotating frequency modulate with each other and</p><p>generate an impulse at 6.05Hz(=10Hz–3.95Hz), as</p><p>shown in Figs.7(b) and 8(b). The impulses at 3.95Hz and</p><p>6.05Hz clearly indicate the occurrence of the bearing</p><p>cage fault.</p><p>5.3 Signal conditioning: multiscale f iltering</p><p>To validate the MFS method, a Skystream 3.7 wind</p><p>turbine in the field was tested[35]. One phase stator</p><p>current signal of the PMSG of the wind turbine was</p><p>measured and sampled at 1000Hz. The number of pole</p><p>pairs of the PMSG is 21. The type of shaft bearings is</p><p>W. Qiao et al.: Prognostic Condition Monitoring for Wind Turbine Drivetrains via Generator Current Analysis 87</p><p>6209. Both the MFS method and angular resampling</p><p>method are applied to the envelope of the current signal.</p><p>Fig.9(a) and (b) show the order spectra of the current</p><p>envelope signal obtained by using the angular</p><p>resampling method and the MFS method, respectively.</p><p>The cage fault characteristic order of the shaft bearings,</p><p>OFTFI = 0.598, appears clearly on both plots, indicating</p><p>that one of the shaft bearings installed in the wind</p><p>turbine has a cage fault. However, the order spectrum</p><p>obtained from the angular resampling method contains</p><p>some interfering peaks around order 15 (marked by an</p><p>ellipse). These components are those with the frequencies</p><p>unrelated to the shaft rotating frequency contained in the</p><p>original current signal. Since they may smear the</p><p>harmonics of other possible characteristic orders, it is</p><p>not clear whether or not the bearing has other faults. In</p><p>Fig.9(b), the interfering peaks disappear by using the</p><p>MFS method. There are no other components except for</p><p>the component related to the bearing cage fault relative</p><p>to the inner ring in Fig.9(b), indicating that only a cage</p><p>fault has occurred in the bearing. The results prove that</p><p>the MFS method is superior to the angular resampling</p><p>method for signal conditioning when the current signal</p><p>contains frequencies that are irrelevant to the shaft</p><p>rotating frequency.</p><p>5.4 Fault prognosis and RUL prediction</p><p>The fault prognosis and RUL prediction method</p><p>was validated by a gearbox run-to-failure test on a wind</p><p>turbine drivetrain test rig[42]. The test rig used a</p><p>two-stage helical gearbox connected with a generator to</p><p>emulate the drivetrain of a wind turbine. The gearbox</p><p>was driven by an adjustable-speed induction motor drive</p><p>through a speed reducer, which were used to emulate the</p><p>aerodynamics of a wind turbine rotor. One phase stator</p><p>current of the PMSG was measured by a FLUKE</p><p>80I-11s current probe. The current signal was acquired</p><p>using a NI data acquisition system with an interval of 6</p><p>minutes during the entire test. In each 6-minute interval,</p><p>the data was recorded for 120 seconds.</p><p>The NSR defined as follows is used as the fault</p><p>feature for the fault prognosis and RUL prediction.</p><p>Fig.9 Order spectra of the current envelope signal of the</p><p>field wind turbine with a bearing cage fault obtained by</p><p>different methods</p><p>noise</p><p>signal</p><p>NSR P</p><p>P</p><p> (10)</p><p>where Psignal( 21</p><p>02 sTi ) is the power of the fundamental</p><p>frequency component (i.e., the amplitude at the</p><p>fundamental frequency of the PSD spectrum) of the</p><p>current signal; Pnoise=PtotalPsignal; and Ptotal is the total</p><p>power of the current signal defined as:</p><p>2</p><p>total</p><p>s 1</p><p>1 | ( ) |</p><p>sN</p><p>n</p><p>P i n</p><p>N </p><p>  (11)</p><p>where Ns is the total number of data samples in the</p><p>recorded stator current signal i(n).</p><p>When a fault is developing in the gearbox, the</p><p>vibration and sound of the test rig will increase. The</p><p>increased vibration will lead to an increase of the value</p><p>of the fault feature NSR. At the end of the test, multiple</p><p>failures have occurred in the gearbox; both the vibration</p><p>and sound of the test rig reached high levels; and the</p><p>NSR has exceeded the threshold. The system was shut</p><p>down due to the safety concerns. A total of 285 120-</p><p>second data records were obtained in the test. Thus, the</p><p>lifetime of the gearbox was 285×6 = 1710 minutes.</p><p>The values of the fault feature predicted by the</p><p>particle filtering algorithm are compared with the actual</p><p>values in Fig.10. The uncertainty envelope is defined to</p><p>be the curves of the maximum and minimum values of</p><p>the NSR predicted by the particles in the particle</p><p>filtering algorithm. In the algorithm implementation, the</p><p>data before 1200 min are used for training the ANFIS to</p><p>learn the state transition function and the fault prognosis</p><p>starts at 1200 minutes. The particle filtering algorithm</p><p>accurately predicts the trend of the gearbox degradation</p><p>(i.e., the change of the fault feature NSR).</p><p>Fig.11 compares the probability density functions</p><p>(PDFs) of the RUL predicted by the particle filtering</p><p>algorithm at 900minutes, 1200minutes, and 1500minutes,</p><p>where the peaks of the PDF curves indicate the highest</p><p>probabilities of the predicted</p><p>failure time[42]. At</p><p>900minutes, it is difficult to find the peak value of the</p><p>PDF. As time goes on, more particles predict the RUL</p><p>close to the peak value of the PDF, indicating that the</p><p>RUL prediction becomes more accurate. The peak of the</p><p>PDF predicted at 1500minutes is close to 1710minutes,</p><p>which is the actual failure time of the gearbox. This</p><p>Fig.10 NSR prediction result of the test gearbox</p><p>88 Chinese Journal of Electrical Engineering, Vol.4, No.3, September 2018</p><p>Fig.11 PDFs of the RUL predicted by the particle filtering</p><p>algorithm at three different time instants[42]</p><p>indicates that the particle filtering algorithm has the</p><p>ability to use new values of the NSR to improve the</p><p>accuracy of the gearbox RUL prediction as time goes on.</p><p>Moreover, the RUL can be predicted accurately ahead of</p><p>time such that maintenance can be scheduled in advance</p><p>with the optimal logistics to minimize the cost.</p><p>6 Conclusion and recommendations</p><p>This paper discussed the principles and challenges</p><p>of using generator current signals for prognostic</p><p>condition monitoring of wind turbine drivetrains. A</p><p>prognostic CMS that incorporated recent advancements</p><p>in current-based wind turbine fault diagnosis and</p><p>prognosis and RUL prediction methods was presented.</p><p>These current-based prognostic condition monitoring</p><p>methods have been validated on a variety of laboratory</p><p>wind turbine drivetrain test rigs, laboratory wind</p><p>turbines, and field wind turbines of different types and</p><p>sizes from hundreds of Watts to 1.6MW. Some typical</p><p>results were presented in the paper to demonstrate the</p><p>effectiveness and superiority of some of these methods</p><p>for fault diagnosis and prognosis and RUL prediction of</p><p>wind turbines drivetrains under nonstationary operating</p><p>conditions.</p><p>The current-based prognostic condition monitoring</p><p>techniques provide a low-cost, reliable solution for the</p><p>wind industry to reduce the failure rate and level,</p><p>maintenance costs, and downtime; improve reliability;</p><p>and extend the life of wind turbines. With further</p><p>development and field test validations, the current-based</p><p>prognostic condition monitoring techniques can be</p><p>implemented in the CMSs of various types of wind</p><p>turbines.</p><p>In addition to wind turbine drivetrains, other wind</p><p>turbine subsystems, such as power electronic converters,</p><p>are also subject to significant failures and cause</p><p>significant downtime of wind turbines. 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Papaelias,</p><p>“Fault detection and diagnosis within a wind turbine mechanical</p><p>braking system using condition monitoring,” Renewable Energy,</p><p>vol. 47, pp. 175-182, Nov. 2012.</p><p>[25] Y. Amirat, V. Choqueuse, and M. Benhouzid, “EEMD-based</p><p>wind turbine bearing failure detection using the generator stator</p><p>current homopolar component,” Mechanical Systems and Signal</p><p>Processing, vol. 41, no. 1-2, pp. 667-678, Dec. 2013.</p><p>[26] P. Neti, M. R. Shah, K. Younsi, M. Tiwari, and P. Zhang, “Fault</p><p>detection based on current signature analysis for a generator,”</p><p>United States Patent No. US 8994359 B2, Mar. 31, 2015.</p><p>[27] X. Gong, and W. Qiao, “Bearing fault diagnosis for direct-drive</p><p>wind turbines via current demodulated signals,” IEEE Trans.</p><p>Industrial Electronics, vol. 60, no. 8, pp. 3419-3428, Aug. 2013.</p><p>[28] F. Cheng, J. Wang, L. Qu, and W. Qiao, “Rotor current-based</p><p>fault diagnosis for DFIG wind turbine drivetrain gearboxes using</p><p>frequency analysis and a deep classifier,” IEEE Trans. Industry</p><p>Applications, vol. 54, no. 2, pp 1062-1071, Mar.Apr. 2018.</p><p>[29] F. Cheng, L. Qu, W. Qiao, C. Wei, and L. Hao, “Fault diagnosis</p><p>of wind turbine gearboxes based on DFIG stator current</p><p>envelope analysis,” IEEE Trans. Sustainable Energy, accepted</p><p>for publication.</p><p>[30] X. Gong, and W. Qiao, “Current-based mechanical fault detection</p><p>for direct-drive wind turbines via synchronous sampling and</p><p>impulse detection,” IEEE Trans. Industrial Electronics, vol. 62,</p><p>no. 3, pp. 1693-1720, Mar. 2015.</p><p>[31] J. Wang, Y. Peng, and W. Qiao, “Current-aided order tracking of</p><p>vibration signals for bearing fault diagnosis of direct-drive wind</p><p>turbines,” IEEE Trans. Industrial Electronics, vol. 63, no. 10,</p><p>pp. 6336-6346, Oct. 2016.</p><p>[32] D. Lu, W. Qiao, X. Gong, and L. Qu, “Current-based fault</p><p>detection for wind turbine systems via Hilbert-Hung transform,”</p><p>Proc. IEEE PES General Meeting, July 2013, 5 pages.</p><p>[33] D. Lu, W. Qiao, and X. Gong, “Current-based gear fault</p><p>detection for wind turbine gearboxes,” IEEE Trans. Sustainable</p><p>Energy, vol. 8, no. 4, pp. 1453-1462, Oct. 2017.</p><p>[34] X. Gong, and W. Qiao, “Imbalance fault detection of direct-drive</p><p>wind turbines using generator current signals,” IEEE Trans.</p><p>Energy Conversion, vol. 27, no. 2, pp. 468-476, June 2012.</p><p>[35] J. Wang, Y. Peng, W. Qiao, and J. L. Hudgins, “Bearing fault</p><p>diagnosis of direct-drive wind turbines using multiscale filtering</p><p>spectrum,” IEEE Trans. Industry Applications, vol. 53, no. 3, pp.</p><p>3029-3038, May-Jun. 2017.</p><p>[36] F. Cheng, Y. Peng, L. Qu, and W. Qiao, “Current-based fault</p><p>detection and identification for wind turbine drivetrain gearboxes,”</p><p>IEEE Trans. Industry Applications, vol.53, no.2, pp. 878-887,</p><p>Mar.-Apr. 2017.</p><p>[37] J. Wang, F. Cheng, W. Qiao, and L. Qu, “Multiscale filtering</p><p>reconstruction for wind turbine gearbox fault diagnosis under</p><p>varying-speed and noisy conditions,” IEEE Trans. Industrial</p><p>Electronics, vol. 65, no. 5, pp 4268-4278, May 2018.</p><p>[38] X. Jin, F. Cheng, Y. Peng, W. Qiao, and L. Qu, “A comparative</p><p>study on vibration- and current-based approaches for drivetrain</p><p>gearbox fault diagnosis,” IEEE Industry Applications Magazine,</p><p>vol. 24, no. 6, Nov.-Dec. 2018, in press.</p><p>[39] X. Jin, W. Qiao, Y. Peng, F. Cheng, and L. Qu, “Quantitative</p><p>evaluation of wind turbine faults under variable operational</p><p>conditions,” IEEE Trans. Industry Applications, vol. 52, no. 3,</p><p>pp. 2061-2069, May/Jun. 2016.</p><p>[40] W. Qiao, and X. Gong, “Detecting faults in wind turbines,” U.S.</p><p>Patent Pub. No. US 2013/0325373 A1, Dec. 5, 2013.</p><p>[41] W. Qiao, and X. Gong, “Detecting faults in turbine generators,”</p><p>U.S. Patent Pub. No. US 2016/0033580 A1, Feb. 4, 2016.</p><p>[42] F. Cheng, L. Qu, and W. Qiao, “Fault prognosis and remaining</p><p>useful life prediction of wind turbine drivetrain gearboxes using</p><p>current signal analysis,” IEEE Trans. Sustainable Energy, vol. 9,</p><p>no. 1, pp 157-167, Jan. 2018.</p><p>[43] F. Cheng, L. Qu, W. Qiao, and L. Hao, “Enhanced particle</p><p>filtering for remaining useful life prediction of a wind turbine</p><p>drivetrain gearbox,” IEEE Trans. Industrial Electronics, accepted</p><p>for publication.</p><p>Wei Qiao (S’05 – M’08 – SM’12) received a</p><p>B.Eng. and an M.Eng. degrees in electrical</p><p>engineering from Zhejiang University, Hangzhou,</p><p>China, in 1997 and 2002, respectively, an M.S.</p><p>degree in high-performance computation</p><p>for engineered systems from Singapore-MIT</p><p>Alliance, Singapore, in 2003, and a Ph.D.</p><p>degree in electrical engineering from Georgia</p><p>Institute of Technology, Atlanta, GA, USA, in</p><p>2008.</p><p>Since August 2008, he has been with the University of Nebraska–</p><p>Lincoln, Lincoln, NE, USA, where he is currently a Professor with the</p><p>Department of Electrical and Computer Engineering. His research</p><p>interests include renewable energy, smart grids, condition monitoring,</p><p>power electronics, electric machines and drives, and new electrical</p><p>energy conversion devices. He is the author or coauthor of more than</p><p>220 papers in refereed journals and conference proceedings.</p><p>Dr. Qiao is an Editor of the IEEE Transactions on Energy</p><p>Conversion and the IEEE Power Engineering Letters, and an</p><p>Associate Editor of the IEEE Transactions on Power Electronics and</p><p>the IEEE Journal of Emerging and Selected Topics in Power</p><p>Electronics. He was the recipient of a 2010 U.S. National Science</p><p>Foundation CAREER Award and the 2010 IEEE Industry Applications</p><p>Society Andrew W. Smith Outstanding Young Member Award.</p><p>Liyan Qu (S’05–M’08–SM’17) received a</p><p>B.Eng. (with the highest distinction) and an</p><p>M.Eng. degrees in electrical engineering from</p><p>Zhejiang University, Hangzhou, China, in 1999</p><p>and 2002, respectively, and a Ph.D. degree in</p><p>electrical engineering from the University of</p><p>Illinois at Urbana–Champaign, Champaign, IL,</p><p>USA, in 2007.</p><p>From 2007 to 2009, she was an Application</p><p>Engineer with Ansoft Corporation, Irvine, CA,</p><p>USA. Since January 2010, she has been with the University of</p><p>Nebraska−Lincoln, Lincoln, NE, USA, where she is currently an</p><p>Associate Professor with the Department of Electrical and Computer</p><p>Engineering. Her research interests include energy efficiency,</p><p>renewable energy, numerical analysis and computer aided design of</p><p>electric machinery and power electronic devices, dynamics and</p><p>control of electric machinery, and magnetic devices.</p><p>Dr. Qu was a recipient of the 2016 U.S. National Science</p><p>Foundation CAREER Award.</p>