Frontiers of Mechanical Engineering

, Volume 12, Issue 3, pp 281–302 | Cite as

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

  • Shoudao Huang
  • Xuan Wu
  • Xiao Liu
  • Jian Gao
  • Yunze He
Open Access
Review Article
  • 465 Downloads

Abstract

Electric power conversion system (EPCS), which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine (DD-WT). However, this component accounts for the most failures (approximately 60% of the total number) in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring (CM) and fault diagnostics (FD). Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.

Keywords

direct-drive wind turbine electric power conversion system condition monitoring fault diagnosis operation control under faults fault tolerance 

Notes

Acknowledgements

This work was supported by the National Key R&D Program of China (Grant No. 2016YFF0203400). The program focuses on studies on service quality monitoring and maintenance quality control technology for large wind turbines. The project leader is Professor Shoudao Huang. The authors are also grateful to the National Natural Science Foundation of China (Grant No. 51377050) for the financial support.

References

  1. 1.
    Qiao W, Lu D. A survey on wind turbine condition monitoring and fault diagnosis—Part I: Components and subsystems. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6536–6545Google Scholar
  2. 2.
    Qiao W, Lu D. A survey on wind turbine condition monitoring and fault diagnosis—Part II: Signals and signal processing methods. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6546–6557Google Scholar
  3. 3.
    Liu W, Tang B, Han J, et al. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review. Renewable and Sustainable Energy Reviews, 2015, 44: 466–472Google Scholar
  4. 4.
    Mirafzal B. Survey of fault-tolerance techniques for three-phase voltage source inverters. IEEE Transactions on Industrial Electronics, 2014, 61(10): 5192–5202Google Scholar
  5. 5.
    Machado de Azevedo H D, Araújo A M, Bouchonneau N. A review of wind turbine bearing condition monitoring: State of the art and challenges. Renewable and Sustainable Energy Reviews, 2016, 56: 368–379Google Scholar
  6. 6.
    Feng Y, Zhou J, Qiu Y, et al. Fault tolerance for wind turbine power converter. In: Proceedings of 2nd IET Renewable Power Generation Conference (RPG 2013). IET, 2013Google Scholar
  7. 7.
    Qiu Y, Jiang H, Feng Y, et al. A new fault diagnosis algorithm for PMSG wind turbine power converters under variable wind speed conditions. Energies, 2016, 9(7): 548Google Scholar
  8. 8.
    Tian Z, Jin T, Wu B, et al. Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy, 2011, 36(5): 1502–1509Google Scholar
  9. 9.
    Yang D, Li H, Hu Y, et al. Vibration condition monitoring system for wind turbine bearings based on noise suppression with multipoint data fusion. Renewable Energy, 2016, 92: 104–116Google Scholar
  10. 10.
    Cheng M, Zhu Y, The state of the art of wind energy conversion systems and technologies: A review. Energy Conversion and Management, 2014, 88: 332–347Google Scholar
  11. 11.
    Nasiri M, Milimonfared J, Fathi S H. A review of low-voltage ridethrough enhancement methods for permanent magnet synchronous generator based wind turbines. Renewable and Sustainable Energy Reviews, 2015, 47: 399–415Google Scholar
  12. 12.
    Thomson W T. On-line MCSA to diagnose shorted turns in low voltage stator windings of 3-phase induction motors prior to failure. In: Proceedings of the IEEE International Electric Machines and Drives Conference. IEEE, 2001, 891–898Google Scholar
  13. 13.
    Tallam R M, Habetler T G, Harley R G. Stator winding turn-fault detection for closed-loop induction motor drives. IEEE Transactions on Industry Applications, 2003, 39(3): 720–724Google Scholar
  14. 14.
    Nandi S, Toliyat H. Novel frequency-domain-based technique to detect stator interturn faults in induction machines using statorinduced voltages after switch-off. IEEE Transactions on Industry Applications, 2002, 38(1): 101–109Google Scholar
  15. 15.
    Kliman G B, Premerlani W J, Koegl R A, et al. Sensitive on-line turn-to-turn fault detection in AC motors. Electric Machines and Power Systems, 2000, 28(10): 915–927Google Scholar
  16. 16.
    Li H, Sun L, Xu B. Research on transient behaviors and detection methods of stator winding inter-turn short circuit fault in induction motors based on multi-loop mathematical model. In: Proceedings of International Conference on Electrical Machines and Systems. IEEE, 2005, 1951–1955Google Scholar
  17. 17.
    Joksimovic G M, Penman J. The detection of inter-turn short circuits in the stator windings of operating motors. IEEE Transactions on Industrial Electronics, 2000, 47(5): 1078–1084Google Scholar
  18. 18.
    Cruz S M Z, Cardoso A J M. Stator winding fault diagnosis in three-phase synchronous and asynchronous motors, by the extended Park’s vector approach. IEEE Transactions on Industry Applications, 2001, 37(5): 395–401Google Scholar
  19. 19.
    Penman J, Sedding H G, Lloyd B A, et al. Detection and location of interturn short circuits in the stator windings of operating motors. IEEE Transactions on Energy Conversion, 1994, 9(4): 652–658Google Scholar
  20. 20.
    Melero M G, Cabanas M F. Study of an induction motor working under stator winding inter-turn short circuit condition. In: Proceedings of 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. Atlanta: IEEE, 2003, 423–429Google Scholar
  21. 21.
    Henao H, Demian C, Capolino G A. A frequency-domain detection of stator winding faults in induction machines using an external flux sensor. IEEE Transactions on Industry Applications, 2003, 39(5): 1272–1279Google Scholar
  22. 22.
    Guo C, Zhang L, Wang Z. Fault diagnosis of AC motor on the vibrating spectral analysis. Oil Field Machinery, 2005, 34(4): 21–23 (in Chinese)Google Scholar
  23. 23.
    Cao C. Real-time detecting signal of motor vibration based on wavelet packet decomposition. Electric Machines and Control Applications, 2005, 32(8): 58–61 (in Chinese)Google Scholar
  24. 24.
    Amaral T G, Pires V F, Martins J F, et al. Statistic moment based method for the detection and diagnosis of induction motor stator fault. In: Proceedings of International Conference on Power Engineering. IEEE, 2007, 106–110Google Scholar
  25. 25.
    Lee S B, Habetler T G, Harley R G, et al. An evaluation of modelbased stator resistance estimation for induction motor stator winding temperature monitoring. IEEE Transactions on Energy Conversion, 1998, 4, 17(1): 7–15Google Scholar
  26. 26.
    Lee S B, Habetler T G. An online stator winding resistance estimation technique for temperature monitoring of line-connected induction machines. IEEE Transactions on Industry Application, 2003, 4, 39(3): 685–694Google Scholar
  27. 27.
    Gao Z, Habetler T G, Harley R G, et al. A sensorless adaptive stator winding temperature estimator for mains-fed induction machines with continuous-operation periodic duty cycles. In: Proceedings of the IEEE Industry Applications Conference, 2006. 41st IAS Annual Meeting. IEEE, 2006, 448–455Google Scholar
  28. 28.
    Briz F, Degner M W, Guerrero J M, et al. Temperature estimation in inverter fed machines using high frequency carrier signal injection. IEEE Transactions on Industry Application, 2007, 799–808Google Scholar
  29. 29.
    Beguenane R, Benbouzid M E H. Induction motors thermal monitoring by means of rotor resistance identification. IEEE Transactions on Energy Conversion, 1999, 14(3): 566–570Google Scholar
  30. 30.
    Grubic S, Aller J M, Lu B, et al. A survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems. IEEE Transactions on Industrial Electronics, 2008, 55(12): 4127–4136Google Scholar
  31. 31.
    Stone G C. Advancements during the past quarter century in online monitoring of motor and generator winding insulation. IEEE Transactions on Dielectrics and Electrical Insulation, 2002, 9(5): 746–751Google Scholar
  32. 32.
    Stone G C, Boulter E A, Culbert I, et al. Electrical Insulation for Rotating Machines: Design, Evaluation, Aging, Testing, and Repair. New York: John Wiley & Sons, Inc., 2004Google Scholar
  33. 33.
    Tozzi M, Cavallini A, Montanari G C. Monitoring off-line and online PD under impulsive voltage on induction motors—Part 1: Standard procedure. IEEE Electrical Insulation Magazine, 2010, 26(4): 16–26Google Scholar
  34. 34.
    Wang C, Wang Z, Li F, et al. Anti-interference techniques used for on-line partial discharge monitoring. In: Proceedings of International Conference on Properties and Application. 1994, 2: 582–585Google Scholar
  35. 35.
    Li G, Yi K. Study on using thermal infrared imaging technology detecting the iron core faults of generator. Ningxia Electric Power, 2012, 12(6): 5–7Google Scholar
  36. 36.
    Posedel Z. Inspection of stator cores in large machines with a low yoke induction method-measurement and analysis of interlamination short-circuits. IEEE Transactions on Energy Conversion, 2001, 16(1): 81–86Google Scholar
  37. 37.
    Sarikhani A, Mirafzal B, Mohammed O. Inter-turn fault diagnosis of PM synchronous generator for variable speed wind applications using floating-space-vector. In: Proceedings of IECON 2010—36th Annual Conference on IEEE Industrial Electronics Society. IEEE, 2010, 2628–2633Google Scholar
  38. 38.
    Ding F, Trutt F C. Calculation of frequency spectra of electromagnetic vibration for wound-rotor induction machines with winding faults. Electric Machines and Power Systems, 1988, 14 (3–4): 137–150Google Scholar
  39. 39.
    Hameed Z, Hong Y S, Cho Y M, et al. Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews, 2009, 13(1): 1–39Google Scholar
  40. 40.
    Zhang R, Wang X, Yang Y, et al. Based on the method of equivalent residual magnetism of permanent magnet motor rotor eccentricity magnetic field analytic calculation. Transactions of China Electrotechnical Society, 2009, 24(5): 7–12 (in Chinese)Google Scholar
  41. 41.
    Qiu Z, Li C, Zhou X, et al. Analytical calculation of no-load air-gap magnetic field in surface-mounted permanent magnet motors with rotor eccentricity. Transactions of China Electrotechnical Society, 2013, 28(3): 114–121 (in Chinese)Google Scholar
  42. 42.
    Tang R. Modern Permanent Magnet Machines Theory and Design. Beijing: China Machine Press, 2008, 18–21 (in Chinese)Google Scholar
  43. 43.
    Hao H, Chai J, Jiang Z, et al. Excitation loss in a Nd-Fe-B magnetic materials with alternating magnetic fields. Journal of Tsinghua University (Science and Technology), 2004, 44(6): 721–724 (in Chinese)Google Scholar
  44. 44.
    Xiao X, Zhang M, Li Y. On-line estimation of permanent-magnet flux linkage ripple for PMSM. Proceedings of the CSEE, 2007, 27(24): 142–146 (in Chinese)Google Scholar
  45. 45.
    Qi F. Magnetic stability of permanent magnet materials. Journal of magnetic Materials and Devices, 1998, 29(5): 26–31 (in Chinese)Google Scholar
  46. 46.
    von Staa F, Hempel K A, Artz H. On the energy losses of hot worked Nd-Fe-B magnets and ferrites in a small alternating magnetic field perpendicular to a bias field. IEEE Transactions on Magnetics, 1995, 31(6): 3650–3652Google Scholar
  47. 47.
    Xiao X, Zhang M, Li Y. Permanent magnet synchronous motor permanent magnet condition on-line monitoring. Proceedings of the CSEE, 2007, 27(24): 43–47 (in Chinese)Google Scholar
  48. 48.
    Shinnaka S. New “D-State-Observer”-based vector control for sensorless drive of permanent-magnet synchronous motors. IEEE Transactions on Industry Applications, 2005, 41(3): 825–833Google Scholar
  49. 49.
    Chen Z, Tomita M, Doki S, et al. An extended electromotive force model for sensorless control of Interior permanent-magnet synchronous motors. IEEE Transactions on Industrial Electronics, 2003, 50(2): 288–295Google Scholar
  50. 50.
    Eskola M, Tuusa H. Comparison of MRAS and novel simple method for position estimation in PMSM drives. In: Proceedings of IEEE 34th Annual Power Electronics Specialist Conference. Acapulco: IEEE, 2003Google Scholar
  51. 51.
    Krishnan R, Vijayraghavan P. Fast estimation and compensation of rotor flux linkage in permanent magnet synchronous machines. In: Proceedings of the IEEE International Symposium on Industrial Electronics. IEEE, 1999Google Scholar
  52. 52.
    Tchakoua P, Wamkeue R, Ouhrouche M, et al. Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies, 2014, 7(4): 2595–2630Google Scholar
  53. 53.
    García Márquez F P, Tobias A M, Pinar Pérez J M, et al. Condition monitoring of wind turbines: Techniques and methods. Renewable Energy, 2012, 46(2): 169–178Google Scholar
  54. 54.
    Yang W, Tavner P J, Tian W. Wind turbine condition monitoring based on an improved spline-kernelled chirplet transform. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6565–6574Google Scholar
  55. 55.
    Astolfi D, Castellani F, Terzi L. Fault prevention and diagnosis through SCADA temperature data analysis of an onshore wind farm. Diagnostyka, 2014, 15(2): 71–78Google Scholar
  56. 56.
    Shahriar M R, Wang L, Kan M S, et al. Fault detection of wind turbine drivetrain utilizing power-speed characteristics. In: Amadi-Echendu J, Hoohlo C, Mathew J, eds. 9th WCEAM Research Papers. Lecture Notes in Mechanical Engineering. Cham: Springer, 2015, 143–155Google Scholar
  57. 57.
    Guo P, Infield D. Wind turbine tower vibration modeling and monitoring by the nonlinear state estimation technique (NSET). Energies, 2012, 5(12): 5279–5293Google Scholar
  58. 58.
    Yang H, Mathew J, Ma L. Vibration feature extraction techniques for fault diagnosis of rotating machinery: A literature survey. In: Proceedings of Asia-Pacific Vibration Conference. Gold Coast, 2003Google Scholar
  59. 59.
    Hameed Z, Ahn S, Cho Y. Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation. Renewable Energy, 2010, 35(5): 879–894Google Scholar
  60. 60.
    Costinas S, Diaconescu I, Fagarasanu J. Wind power plant condition monitoring. In: Proceedings of the 3rd WSEAS International Conference on Energy Planning, Energy Saving, Environmental Education. Tenerife, 2009, 71–76Google Scholar
  61. 61.
    Rogers A L, Manwell J F, Wright S. Wind Turbine Acoustic Noise. White paper. 2002/2006Google Scholar
  62. 62.
    Salon S, Salem S, Sivasubramaniam K. Monitoring and diagnostic solutions for wind generators. In: Proceedings of IEEE Power and Energy Society General Meeting. IEEE, 2011Google Scholar
  63. 63.
    Niknam S A, Thomas T, Hines J W, et al. Analysis of acoustic emission data for bearings subject to unbalance. International Journal Prognostics and Health Management, 2013, 21(Suppl2): 1–10Google Scholar
  64. 64.
    Ma Y, He C, Feng X. Institutions function and failure statistic and analysis of wind turbine. Physics Procedia, 2012, 24(Part A): 25–30Google Scholar
  65. 65.
    Yang W, Court R, Jiang J. Wind turbine condition monitoring by the approach of SCADA data. Renewable Energy. 2013, 53(9): 365–376Google Scholar
  66. 66.
    Patil N, Das D, Goebel K, et al. Identification of failure precursor parameters for insulated gate bipolar transistors (IGBTs). In: Proceedings of International Conference on Prognostics and Health Management. Denver: IEEE, 2008Google Scholar
  67. 67.
    Yang L, Agyakwa P A, Johnson C M. A time-domain physics-offailure model for the lifetime prediction of wire bond interconnects. Microelectronics and Reliability, 2011, 51(9–11): 1882–1886Google Scholar
  68. 68.
    Li H, Liu S, Ran L, et al. Overview of condition monitoring technologies of power converter for high power grid-connected wind turbine generator system. Transactions of China Electrotechnical Society, 2016, 31(8): 1–10 (in Chinese)Google Scholar
  69. 69.
    Jablonski A, Barszcz T, Bielecka M. Automatic validation of vibration signals in wind farm distributed monitoring systems. Measurement, 2011, 44(10): 1954–1967Google Scholar
  70. 70.
    Liang Y, Fang R. An online wind turbine condition assessment method based on SCADA and support vector regression. Automation of Electric Power Systems, 2013, 37(14): 7–12 (in Chinese)Google Scholar
  71. 71.
    Guo P, Xu M, Bai N, et al. Wind turbine tower vibration modeling and monitoring driven by SCADA data. Proceedings of the CSEE, 2013, 33(5): 138–135 (in Chinese)Google Scholar
  72. 72.
    Dai J, Yuan X, Liu D, et al. Vibration analysis of large direct drive wind turbine nacelle based on SCADA system. Acta Energize Solaris Sinica, 2015, 36(12): 2895–2905Google Scholar
  73. 73.
    Isermann R. Model-based fault detection and diagnosis-status and applications. Annual Reviews in Control, 2004, 29(1): 71–85Google Scholar
  74. 74.
    Mahyob P, Reghem P, Barakat G. Permeance network modeling of the stator winding faults in electrical machines. IEEE Transactions on Magnetics, 2009, 45(3): 1820–1823Google Scholar
  75. 75.
    Kim B W, Kim K T, Hur J. Simplified impedance modeling and analysis for inter-turn fault of IPM-type BLDC motor. Journal of Power Electronics, 2012, 12(1): 10–18Google Scholar
  76. 76.
    Yazidi A, Henao H, Capolino G. Double-fed three-phase induction machine model for simulation of inter-turn short circuit fault. In: Proceedings of IEEE International Electric Machines and Drives Conference. IEEE, 2009, 571–576Google Scholar
  77. 77.
    Zhu D, Tan K. Present situation and prospects of condition monitoring and fault diagnosis technology for electrical equipments. Electrical Equipment, 2003, 4(6): 1–8 (in Chinese)Google Scholar
  78. 78.
    Widodo A, Yang B S, Gu D S, et al. Intelligent fault diagnosis system of induction motor based on transient current signal. Mechatronics, 2009, 19(5): 680–689Google Scholar
  79. 79.
    Cusido J, Romeral L, Ortega J A, et al. Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Transactions on Industrial Electronics, 2008, 55(2): 633–643Google Scholar
  80. 80.
    Jung J H, Lee J J, Kwon B H. Online diagnosis of induction motors using MCSA. IEEE Transactions on Industrial Electronics, 2006, 53(6): 1842–1852Google Scholar
  81. 81.
    Cusido J, Rosero J A, Ortega J A, et al. Induction motor fault detection by using wavelet decomposition on dq0 components. In: Proceedings of IEEE International Symposiums on Industry Electronics. IEEE, 2006, 2406–2411Google Scholar
  82. 82.
    Chetwani S H, Shah M K, Ramamoorty M. Online condition monitoring of induction motors through signal processing. In: Proceedings of 8th International Conference on Electrical Machines and Systems. IEEE, 2005, 2175–2179Google Scholar
  83. 83.
    Wu G. Theory and Practice of the State Monitoring of Motor Equipment. Beijing: Tsinghua University Press, 2005 (in Chinese)Google Scholar
  84. 84.
    Liu M, Cui S, Guo B. A method of failure recognition based on fuzzy C-means support vector machines for permanent magnetic DC motor. Micromotors, 2011, 44(10): 78–80 (in Chinese)Google Scholar
  85. 85.
    Xu Y, Xu J, Guo X. Fuzzy diagnostic system for induction motor based on wavelet analysis and RBF neural network. Research and Exploration in Laboratory, 2012, 28(4): 282–301Google Scholar
  86. 86.
    Chen X. Fault diagnosis of electro-mechanical equipment based on noise signal processing. Machine Tool and Hydraulic, 2005, 65(12): 183–186 (in Chinese)Google Scholar
  87. 87.
    Tan Y, He Y, Cui C. A novel method for analog fault diagnosis based on neural networks and genetic algorithm. IEEE Transactions on Instrumentation and Measurement, 2008, 57(11): 2631–2639Google Scholar
  88. 88.
    Su H, Chong K T. Induction machine condition monitoring using neural network modeling. IEEE Transactions on Industrial Electronics, 2007, 54(1): 241–249Google Scholar
  89. 89.
    Valtierra-Rodriguez M, de Jesus Romero-Troncoso R, Osornio-Rios R A, et al. Detection and classification of single and combined power quality disturbances using neural networks. IEEE Transactions on Industrial Electronics, 2014, 61(5): 2473–2482Google Scholar
  90. 90.
    Wang X, Kruger U, Irwin GW, et al. Nonlinear PCA with the local approach for diesel engine fault detection and diagnosis. IEEE Transactions on Control System Technology, 2008, 16(1): 122–129Google Scholar
  91. 91.
    Huang X, Wang J. The network generation technique of crack tracking. Journal of Shanghai Jiaotong University, 2001, 35(4): 493–495 (in Chinese)Google Scholar
  92. 92.
    Wang C, Zheng C. Semi-analytical finite element method for plane crack stress intensity factor. Engineering Mechanics, 2005, 22(1): 33–37 (in Chinese)Google Scholar
  93. 93.
    Yang T, Ren Y, Liu X, et al. Research on the modeling and simulation of wind turbine rotor imbalance fault. Journal of Mechanical Engineering, 2012, 48(6): 130–135 (in Chinese)Google Scholar
  94. 94.
    Jiang D. Huang Q, Hong L. Theoretical and experimental study on wind wheel unbalance for a wind turbine. In: Proceedings ofWorld Non-Grid-Connected Wind Power and Energy Conference. IEEE, 2009Google Scholar
  95. 95.
    Yuji T, Bouno T, Hamada T. Suggestion of temporarily for forecast diagnosis on blade of small wind turbine. IEEJ Transactions on Power and Energy, 2006, 126(7): 710–711Google Scholar
  96. 96.
    Bouno T, Yuji T, Hamada T, et al. Failure forecast diagnosis of small wind turbine using acoustic emission sensor. KIEE International Transaction on Electrical Machinery and Energy Conversion Systems, 2005, 5-B(1): 78–83Google Scholar
  97. 97.
    Qian Y, Ma H. A survey of fault diagnosis method for doubly-fed induction motor. Large electric Machine and Hydraulic Turbine, 2011, (5): 5–8 (in Chinese)Google Scholar
  98. 98.
    Le Roux W, Harley R G, Habetler T G. Detecting rotor faults in permanent magnet synchronous machines. In: Proceedings of 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronic Sand Drives. IEEE, 2003, 198–203Google Scholar
  99. 99.
    Le Roux W, Harley R G, Habetler T G. Converter control effects on condition monitoring of rotor faults in permanent magnet synchronous machines. In: Proceedings of the Industry Applications Conference. 38th IAS Annual Meeting. IEEE, 2003, 1389–1396Google Scholar
  100. 100.
    Rosero J, Romeral L, Ortega J A, et al. Demagnetization fault detection by means of Hilbert Huang transform of the stator current decomposition in PMSM. In: Proceedings of IEEE International Symposium on Industrial Electronics. IEEE, 2008, 172–177Google Scholar
  101. 101.
    Ruiz J R R, Rosero J A, Espinosa A G, et al. Detection of demagnetization faults in permanent-magnet synchronous motors under nonstationary conditions. IEEE Transactions on Magnetics, 2009, 45(7): 2961–2969Google Scholar
  102. 102.
    Rosero J A, Cusido J, Garcia A, et al. Study on the permanent magnet demagnetization fault in permanent magnet synchronous machines. In: Proceedings of 32nd Annual Conference of the IEEE Industrial Electronics. IEEE, 2006, 879–884Google Scholar
  103. 103.
    Farooq J, Srairi S, Djerdir A, et al. Use of permeance network method in the demagnetization phenomenon modeling in a permanent magnet. IEEE Transactions on Magnetics, 2006, 42(4): 1295–1298Google Scholar
  104. 104.
    Wymore M L, Dam J E V, Ceylan H, et al. A survey of health monitoring systems for wind turbines. Renewable and Sustainable Energy Reviews, 2015, 52: 976–990Google Scholar
  105. 105.
    Jöckel S, Herrmann A, Rink J. High energy production plus builtin reliability—The VENSYS 70/77—New gearless wind turbines in the 1.5 MW class. Presentation in the Technical Track of the European Wind Energy Conference. 2006Google Scholar
  106. 106.
    Dubois M R, Polinder H, Ferreira J A. Generator topologies for direct-drive wind turbines, and adapted technology for turbines running in cold climate. In: Proceedings of Conference on Wind Energy in Cold Climates. Matane, 2001, 201–215Google Scholar
  107. 107.
    Dubois M R, Polinder H, Ferreira J A. Comparison of generator topologies for direct-drive wind turbines. In: Proceedings of Nordic Countries Power and Industrial Electronics Conference (NORPIE). Aalborg, 2000Google Scholar
  108. 108.
    Versteegh C J A. Design of the Zephyros Z72 wind turbine with emphasis on the direct drive PM generator. In: Proceedings of Nordic Countries Power and Industrial Electronics Conference (NORPIE). Trondheim, 2004Google Scholar
  109. 109.
    An X, Jiang D. Chaotic characteristics identification and trend prediction of running state for wind turbine. Electric Power Automation Equipment, 2010, 30(3): 15–19, 24 (in Chinese)Google Scholar
  110. 110.
    An X, Jiang D, Liu S, et al. Correlation analysis of oil temperature trend for wind turbine gearbox. In: Proceedings of the ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3. Montreal, 2010Google Scholar
  111. 111.
    Zhang Y, Wu W, Wu L. Motor mechanical fault diagnosis based on wavelet packet, Shannon entropy, SVM and GA. Electric Power Automation Equipment, 2010, 30(1): 87–91 (in Chinese)Google Scholar
  112. 112.
    Gu Y, Zhao W, Wu Z. Combustion optimization for utility boiler based on least square-support vector machine. Proceedings of the CSEE, 2010, 30(17): 91–97 (in Chinese)Google Scholar
  113. 113.
    Zhao M. Fault Feature Analysis and Experimental Investigation for Wind Turbine. Beijing: Tsinghua University Press, 2010 (in Chinese)Google Scholar
  114. 114.
    Barszcz T. Application of diagnostic algorithms for wind turbines. Diagnostyka, 2009, 50(2): 7–12Google Scholar
  115. 115.
    Wu Z, Huang N, Long S, et al. On the trend, trending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(38): 14889–14894Google Scholar
  116. 116.
    Pierre Tchakoua, René Wamkeue, Tommy Andy Tameghe, et al. A review of concepts and methods for wind turbines condition monitoring. In: Proceedings of 2013World Congress on Computer and Information Technology (WCCIT). 2013Google Scholar
  117. 117.
    Izelu C O, Oghenevwaire I S. A review on developments in the design and analysis wind turbine drive train. In: Proceedings of International Conference on Renewable Energy Research and Applications. IEEE, 2014, 589–594Google Scholar
  118. 118.
    Estima J O, Cardoso A J M. Fast fault detection, isolation and reconfiguration in fault-tolerant permanent magnet synchronous motor drives. In: Proceedings of IEEE Energy Convers. 2012, 3617–3624Google Scholar
  119. 119.
    Lu B, Sharma S K. A literature review of IGBT fault diagnostic and protection methods for power inverters. IEEE Transactions on Industry Applications, 2009, 45(5): 1770–1777Google Scholar
  120. 120.
    de Araujo Ribeiro R L, Jacobina C B, da Silva E R C, et al. Fault detection of open-switch damage in voltage-fed PWM motor drive systems. IEEE Transactions on Power Electronics, 2003, 18(2): 587–593Google Scholar
  121. 121.
    Khomfoi S, Tolbert L M. Fault diagnostic system for a multilevel inverter using a neural network. IEEE Transactions on Power Electronics, 2007, 22(3): 1062–1069Google Scholar
  122. 122.
    Tavnet P J, Van Bussel G J W, Spinato F. Machine and converter reliabilities in wind turbines. In: Proceedings of 3rd IET International Conference on Power electronics, Machines and Drives. Dublin: IET, 2006, 127–130Google Scholar
  123. 123.
    Jlassi I, Estima J O, Khojet El Khil S, et al. Multiple open-circuit faults diagnosis in back-to-back converters of PMSG drives for wind turbine systems. IEEE Transactions on Power Electronics, 2015, 30(5): 2689–2702Google Scholar
  124. 124.
    Choi U M, Jeong H G, Lee K B, et al. Method for detecting an open-switch fault in a grid-connected NPC inverter system. IEEE Transactions on Power Electronics, 2012, 27(6): 2726–2739Google Scholar
  125. 125.
    Freire NMA, Estima J O, Marques Cardoso A J. Open-circuit fault diagnosis in PMSG drives for wind turbine applications. IEEE Transactions on Industrial Electronics, 2013, 60(9): 3957–3967Google Scholar
  126. 126.
    Fang Z P. Z-source inverter. IEEE Transactions on Industry Applications, 2003, 39(2): 504–510Google Scholar
  127. 127.
    Faulstich S, Hahn B, Tavner P J. Wind turbine downtime and its importance for offshore deployment. Wind Energy (Chichester, England), 2011, 14(3): 327–337Google Scholar
  128. 128.
    Gao Z, Cecati C, Ding S X. A survey of fault diagnosis and faulttolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757–3767Google Scholar
  129. 129.
    Gao Z, Cecati C, Ding S X. A survey of fault diagnosis and faulttolerant techniques—Part II: Fault diagnosis with knowledgebased and hybrid/active approaches. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3768–3774Google Scholar
  130. 130.
    Schulte H, Gauterin E. Fault-tolerant control of wind turbines with hydrostatic trans-mission using Takagi-Sugeno and sliding mode techniques. Annual Reviews in Control, 2015, 40(17): 82–92Google Scholar
  131. 131.
    Corradini M L, Ippoliti G, Orlando G. Sensorless efficient faulttolerant control of wind turbines with geared generator. Automatica, 2015, 62(11): 161–167MathSciNetMATHGoogle Scholar
  132. 132.
    Guan H, Zhao H, Wang W, et al. LVRT capability of wind turbine generator and its application. Transactions of China Electrotechnical Society, 2007, 22(10): 173–177 (in Chinese)Google Scholar
  133. 133.
    Hu S, Li J, Xu H. Modeling on converters of direct-driven wind power system and its performance during voltage sags. High Voltage Engineering, 2008, 34(5): 949–954 (in Chinese)Google Scholar
  134. 134.
    Freitas W, Morelato A, Xu W. Improvement of induction generator stability using braking resistors. IEEE Transactions on Power Systems, 2004, 19(2): 1247–1249Google Scholar
  135. 135.
    Causebrook A, Atkinson D J, Jack A G. Fault ride-through of large wind farms using series dynamic braking resistors. IEEE Transactions on Power Systems, 2007, 22(3): 966–975Google Scholar
  136. 136.
    Fatu M, Lascu C, Andreescu G D, et al. Voltage sags ride-through of motion sensorless controlled PMSG for wind turbines. In: Proceedings of IEEE Industry Applications Conference. 42nd IAS Annual Meeting. IEEE, 2007, 171–178Google Scholar
  137. 137.
    Li J, Hu S, Kong D, et al. Studies on the low voltage ride through capability of fully converted wind turbine with PMSG. Automation of Electric Power Systems, 2008, 32(19): 92–95 (in chinese)Google Scholar
  138. 138.
    Li H, Dong S, Wang Y, et al. Coordinated control of active and reactive power of PMSG-based wind turbines for low voltage ride through. Transactions of China Electrotechnical Society, 2013, 28(5): 73–81 (in Chinese)Google Scholar
  139. 139.
    Schulte H, Gauterin E. Fault-tolerant control of wind turbines with hydrostatic transmission using Takagi-Sugeno and sliding mode techniques. Annual Reviews in Control, 2015, 40: 82–92Google Scholar
  140. 140.
    Zhang Z, Xu J, Liu X. Research on the high performance fluxweakening control strategy of permanent magnetic synchronous generator for wind turbine. High Power Converter Technology, 2013, 27(3): 62–65 (in Chinese)Google Scholar
  141. 141.
    Chai F, Bi Y. Research review of flux-weakening methods of axial flux permanent magnet synchronous machine. Micromotors, 2015, (2): 70–76 (in Chinese)Google Scholar
  142. 142.
    Li Z, Li Y, Li X. Flux-weakening control of consequent-pole permanent magnet machines. Proceedings of the CSEE, 2013, (21): 124–131 (in Chinese)Google Scholar
  143. 143.
    Parsa L, Toliyat H. Multi-phase permanent-magnet motor drives. IEEE Transactions on Industry Applications, 2005, 41(1): 30–37Google Scholar
  144. 144.
    Fu J R, Lipo T A. Disturbance-free operation of a multiphase current-regulated motor drive with an opened phase. IEEE Transactions on Industry Applications, 1994, 30(5): 1267–1274Google Scholar
  145. 145.
    Toliyat H A. Analysis and simulation of five-phase variable speed induction motor drives under asymmetrical connections. IEEE Transactions on Power Electronics, 1998, 13(4): 748–756Google Scholar
  146. 146.
    Dwari S, Parsa L. Fault-tolerant control of five-phase permanentmagnet motors with trapezoidal back EMF. IEEE Transactions on Industrial Electronics, 2011, 58(2): 476–485Google Scholar
  147. 147.
    Liu T H, Fu J R, Lipo T A. A strategy for improving reliability of field oriented controlled induction motor drives. IEEE Transactions on Industry Applications, 1993, 29(5): 910–918Google Scholar
  148. 148.
    Sinha G, Hochgraf C, Lasseter R H, et al. Fault protection in a multilevel inverter implementation of a static condenser. In: Proceedings of IEEE Industry Applications Conference. Thirtieth IAS Annual Meeting. IEEE, 1995, 2557–2564Google Scholar
  149. 149.
    Bianchi N, Bolognani S, Zigliotto M, et al. Innovative remedial strategies for inverter faults in IPM synchronous motor drives. IEEE Transactions on Energy Conversion, 2003, 18(2): 306–314Google Scholar
  150. 150.
    Athulya Justin, Reshma S. Fault tolerant control of wind energy conversion system—Fuzzy approach. In: Proceedings of the Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). Acapulco: IEEE, 2013Google Scholar
  151. 151.
    de Araujo Ribeiro R L, Jacobina C B, da Silva E R C, et al. Faulttolerant voltage-fed PWM inverter AC motor drive systems. IEEE Transactions on Industrial Electronics, 2004, 51(2): 439–446Google Scholar
  152. 152.
    Welchko B A, Lipo T A, Jahns T M, et al. Fault tolerant threephase AC motor drive topologies: A comparison of features, cost, limitations. IEEE Transactions on Power Electronics, 2004, 19(4): 1108–1116Google Scholar
  153. 153.
    Tiegna H, Amara Y, Barakat G, et al. Overview of high power wind turbine generators. In: Proceedings of International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2012Google Scholar
  154. 154.
    Chowdhury M M, Haque M E, Aktarujjaman M, et al. Grid integration impacts and energy storage systems for wind energy applications—A review. In: Proceedings of IEEE Power and Energy Society General Meeting. IEEE, 2011Google Scholar
  155. 155.
    Polinder H, Ferreira J A, Jensen B B, et al. Trends in wind turbine generator systems. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2013, 1(3): 174–185Google Scholar
  156. 156.
    Huang S, Gao J. The Design and Grid-Connected Control of Direct-Drive Permanent Magnet Wind Turbine. Beijing: Publishing House of Electronics Industry, 2015, 15–19 (in Chinese)Google Scholar
  157. 157.
    Alepuz S, Calle A, Busquets-Monge S, et al. Use of stored energy in PMSG rotor inertia for low-voltage ride-through in back-to-back NPC converter-based wind power systems. IEEE Transactions on Industrial Electronics, 2013, 60(5): 1787–1796Google Scholar
  158. 158.
    Scarcella G, Scelba G, Pulvirenti M, et al. A fault-tolerant power conversion topology for PMSG based wind power systems. In: Proceedings of International Conference on Electrical Machines (ICEM). IEEE, 2014Google Scholar
  159. 159.
    Yang Z, Chai Y. A survey of fault diagnosis for onshore gridconnected converter in wind energy conversion systems. Renewable and Sustainable Energy Reviews, 2016, 66: 345–359Google Scholar
  160. 160.
    Huang S, Wang H, Liao W, et al. The coordinated control strategy based on VSC-HVDC series-parallel topology in wind farm. Transactions of China Electrotechnical Society, 2015, 30(23): 155–162 (in Chinese)Google Scholar
  161. 161.
    Huang S, Wang H, Liao W, et al. Control strategy based on VSCHVDC series topology offshore wind farm for low voltage ride through. Transactions of China Electrotechnical Society, 2015, 30(14): 362–369 (in Chinese)Google Scholar
  162. 162.
    Arani MF M, Mohamed Y A R I. Assessment and enhancement of a full-scale PMSG-based wind power generator performance under faults. IEEE Transactions on Energy Conversion, 2016, 31(2): 728–739Google Scholar
  163. 163.
    Zmood D N, Holmes D G. Stationary frame current regulation of PWM inverters with zero steady-state error. IEEE Transactions on Power Electronics, 2003, 18(3): 814–822Google Scholar
  164. 164.
    Nian H, Cheng P. Resonant based direct power control strategy for PWM rectifier under unbalanced grid voltage condition. Transactions of China Electrotechnical Society, 2013, 28(11): 86–94 (in Chinese)Google Scholar
  165. 165.
    Huang S, Xiao L, Huang K, et al. DC voltage stability of directlydriven wind turbine with PM synchronous generator during the asymmetrical faults. Transactions of China Electrotechnical Society, 2010, 25(7): 123–129 (in Chinese)Google Scholar
  166. 166.
    Huang S, Xiao L, Huang K, et al. Operation and control on the grid-side converter of the directly-driven wind turbine with PM synchronous generator during asymmetrical faults. Transactions of China Electrotechnical Society, 2011, 26(2): 173–180 (in Chinese)MathSciNetGoogle Scholar
  167. 167.
    Xiao L, Huang S, Lu K. DC-bus voltage control of grid-connected voltage source converter by using space vector modulated direct power control under unbalanced network conditions. IET Power Electronics, 2013, 6(5): 925–934Google Scholar
  168. 168.
    Hasegawa N, Kumano T. Low voltage ride-through capability improvement of wind power generation using dynamic voltage restorer. In: Proceedings of the 5th IASME/WSEAS International Conference on Energy and Environment. 2010, 166–171Google Scholar
  169. 169.
    Wang L, Truong D N. Dynamic stability improvement of four parallel-operated PMSG-based off shore wind turbine generators fed to a power system using a STATCOM. IEEE Transactions on Power Delivery, 2013, 28(1): 111–119Google Scholar

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© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Shoudao Huang
    • 1
  • Xuan Wu
    • 1
  • Xiao Liu
    • 1
  • Jian Gao
    • 1
  • Yunze He
    • 1
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina

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