FAULT DIAGNOSIS METHOD FOR WIND TURBINE PITCH SYSTEM BASED ON TimeGAN-Stacking

Pan Meiqi; He Xing

doi:10.19912/j.0254-0096.tynxb.2023-1452

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 192-200. DOI: 10.19912/j.0254-0096.tynxb.2023-1452

FAULT DIAGNOSIS METHOD FOR WIND TURBINE PITCH SYSTEM BASED ON TimeGAN-Stacking

Pan Meiqi¹, He Xing²

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Abstract

The small number of unbalanced fault samples in the variable pitch system of wind turbines makes it difficult to train data-driven fault diagnosis models, leading to frequent missed or false alarms in monitoring systems.. In response to the above issues, this article proposes a fault diagnosis method for wind turbine pitch system based on TimeGAN-Stacking. At the data level, due to the imbalance of the original sample categories, the dynamic change characteristics of the gradual probability distribution of the fan operation data are tracked based on the time-series Generative adversarial network （TimeGAN）, and the global and local distribution of the generated samples are optimized to effectively balance and expand the comprehensive sample set of multiple faults of the fan; At the model level, establish a Stacking integrated model to integrate the advantages of multiple fault diagnosis devices and further improve fault diagnosis capabilities. Finally, the proposed method was tested based on actual wind field data, and the results showed that the proposed TimeGAN -Stacking fault identification method can effectively diagnose four types of pitch faults.

Key words

wind turbines / data mining / fault analysis / deep learning / time-series generative adversarial network（TimeGAN） / sample enhancement

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Pan Meiqi, He Xing. FAULT DIAGNOSIS METHOD FOR WIND TURBINE PITCH SYSTEM BASED ON TimeGAN-Stacking[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 192-200 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1452

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