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Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends

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  • Igba, Joel
  • Alemzadeh, Kazem
  • Durugbo, Christopher
  • Henningsen, Keld

Abstract

The evolution of the wind industry in the last decade has not only seen growth in the installed capacity of turbines and innovation within the industry, but has also seen an increase in research activities in this domain. Gearbox field performance, characterised by reliability, availability and maintainability (RAM), has been a major driver in the research domain due to challenges the industry has faced in gearbox design and operations and maintenance. This paper presents a systematic literature review of the current approaches of performance assessment, such as reliability and maintainability analysis of wind turbine gearboxes with a focus on the use of in-service data. The state-of-the-art in literature are discussed and classified according to key research themes, whilst identifying possible gaps due to lack of literature in specific areas. Also, the future trends in gearbox field performance assessment research are explored. In an attempt to close the gaps in one of the areas not covered in literature, an approach for the estimation of gearbox maintainability was presented. Furthermore, a case study on how preventive maintenance of gearbox bearings which can be applied in practice was carried out to demonstrate the importance of the techniques discussed in this article towards meeting industry׳s needs.

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  • Igba, Joel & Alemzadeh, Kazem & Durugbo, Christopher & Henningsen, Keld, 2015. "Performance assessment of wind turbine gearboxes using in-service data: Current approaches and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 144-159.
    • Handle: RePEc:eee:rensus:v:50:y:2015:i:c:p:144-159
    DOI: 10.1016/j.rser.2015.04.139
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    6. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
    7. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    8. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    9. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Song, Chaosheng & Chen, Dingliang & Zheng, Jie, 2022. "Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion," Renewable Energy, Elsevier, vol. 200(C), pages 1023-1036.
    10. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    11. Romero, Antonio & Soua, Slim & Gan, Tat-Hean & Wang, Bin, 2018. "Condition monitoring of a wind turbine drive train based on its power dependant vibrations," Renewable Energy, Elsevier, vol. 123(C), pages 817-827.
    12. de Azevedo, Henrique Dias Machado & Araújo, Alex Maurício & Bouchonneau, Nadège, 2016. "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 368-379.

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