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

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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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    18. Brooks, Sam & Mahmood, Minhal & Roy, Rajkumar & Manolesos, Marinos & Salonitis, Konstantinos, 2023. "Self-reconfiguration simulations of turbines to reduce uneven farm degradation," Renewable Energy, Elsevier, vol. 206(C), pages 1301-1314.
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    20. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    21. Jordi Cusidó & Arnau López & Mattia Beretta, 2021. "Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning," Energies, MDPI, vol. 14(16), pages 1-20, August.
    22. Afef Fekih & Hamed Habibi & Silvio Simani, 2022. "Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview," Energies, MDPI, vol. 15(19), pages 1-21, September.
    23. Zhang, Chen & Hu, Di & Yang, Tao, 2022. "Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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