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Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review

Author

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  • Habibi, Hamed
  • Howard, Ian
  • Simani, Silvio

Abstract

Reliability improvement of wind turbine power generation is the key issue that can turn the wind power into one of the main power sources to respond to the world power demands. The likelihood of fault occurrence on wind turbine components is unavoidable, especially for large rotor modern wind turbines, operating in harsh offshore environments. Accordingly, the maintenance need increases due to unanticipated faults, which in turn, leads to higher energy cost and less reliable power generation. In this regard, model-based fault detection and fault tolerant control techniques have been extensively investigated in the last decade, for achieving good performance. In this way, the reliability, availability and safety features of the wind turbine power generation are also enhanced. Thus, in this paper a comprehensive review of the most-recent model-based fault detection and fault tolerant control schemes for wind turbine power generation is presented, focusing on the advantages, capabilities and limitations. Note that the so-called data-driven or signal-based methodologies, which rely on the analysis of the signals directly generated from the monitored system, are not reviewed in this paper. This review is organized in a tutorial manner, to be a suitable reference for further research for the wind turbine’s reliability improvement.

Suggested Citation

  • Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    • Handle: RePEc:eee:renene:v:135:y:2019:i:c:p:877-896
    DOI: 10.1016/j.renene.2018.12.066
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    References listed on IDEAS

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    1. Lin, Yonggang & Tu, Le & Liu, Hongwei & Li, Wei, 2016. "Fault analysis of wind turbines in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 482-490.
    2. Badihi, Hamed & Zhang, Youmin & Hong, Henry, 2017. "Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches," Applied Energy, Elsevier, vol. 201(C), pages 284-307.
    3. Feng, Zhipeng & Liang, Ming & Zhang, Yi & Hou, Shumin, 2012. "Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation," Renewable Energy, Elsevier, vol. 47(C), pages 112-126.
    4. Jena, Debashisha & Rajendran, Saravanakumar, 2015. "A review of estimation of effective wind speed based control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1046-1062.
    5. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    6. Knopf, Brigitte & Nahmmacher, Paul & Schmid, Eva, 2015. "The European renewable energy target for 2030 – An impact assessment of the electricity sector," Energy Policy, Elsevier, vol. 85(C), pages 50-60.
    7. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    8. Yolanda Vidal & Christian Tutivén & José Rodellar & Leonardo Acho, 2015. "Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator," Energies, MDPI, vol. 8(5), pages 1-17, May.
    9. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    10. Tang, Baoping & Song, Tao & Li, Feng & Deng, Lei, 2014. "Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine," Renewable Energy, Elsevier, vol. 62(C), pages 1-9.
    11. Mengnan Cao & Yingning Qiu & Yanhui Feng & Hao Wang & Dan Li, 2016. "Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data," Energies, MDPI, vol. 9(10), pages 1-18, October.
    12. Shi, Fengming & Patton, Ron, 2015. "An active fault tolerant control approach to an offshore wind turbine model," Renewable Energy, Elsevier, vol. 75(C), pages 788-798.
    13. Liu, W.Y. & Zhang, W.H. & Han, J.G. & Wang, G.F., 2012. "A new wind turbine fault diagnosis method based on the local mean decomposition," Renewable Energy, Elsevier, vol. 48(C), pages 411-415.
    14. Seyed Mojtaba Tabatabaeipour & Peter F. Odgaard & Thomas Bak & Jakob Stoustrup, 2012. "Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach," Energies, MDPI, vol. 5(7), pages 1-25, July.
    15. Silvio Simani, 2015. "Overview of Modelling and Advanced Control Strategies for Wind Turbine Systems," Energies, MDPI, vol. 8(12), pages 1-24, November.
    16. Njiri, Jackson G. & Söffker, Dirk, 2016. "State-of-the-art in wind turbine control: Trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 377-393.
    17. Abuaisha, Tareq Saber, 2014. "General study of the control principles and dynamic fault behaviour of variable-speed wind turbine and wind farm generic models," Renewable Energy, Elsevier, vol. 68(C), pages 245-254.
    18. Silvio Simani & Paolo Castaldi & Saverio Farsoni, 2017. "Data–Driven Fault Diagnosis of a Wind Farm Benchmark Model," Energies, MDPI, vol. 10(7), pages 1-26, June.
    19. Kamal, E. & Aitouche, A., 2013. "Robust fault tolerant control of DFIG wind energy systems with unknown inputs," Renewable Energy, Elsevier, vol. 56(C), pages 2-15.
    20. Li, Hui & Yang, Chao & Hu, Yaogang & Liao, Xinglin & Zeng, Zheng & Zhe, Chen, 2016. "An improved reduced-order model of an electric pitch drive system for wind turbine control system design and simulation," Renewable Energy, Elsevier, vol. 93(C), pages 188-200.
    21. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    22. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
    23. Hameed, Z. & Ahn, S.H. & Cho, Y.M., 2010. "Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation," Renewable Energy, Elsevier, vol. 35(5), pages 879-894.
    24. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    25. Entezami, M. & Hillmansen, S. & Weston, P. & Papaelias, M.Ph., 2012. "Fault detection and diagnosis within a wind turbine mechanical braking system using condition monitoring," Renewable Energy, Elsevier, vol. 47(C), pages 175-182.
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