HEALTH STATUS ASSESSMENT OF WIND TURBINE BASED ON CNN-BIGRU

Liu Jun; Ge Lei; Zhao Xuanbo; Chen Zhengliang; An Bairen

doi:10.19912/j.0254-0096.tynxb.2024-1914

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 253-260. DOI: 10.19912/j.0254-0096.tynxb.2024-1914

HEALTH STATUS ASSESSMENT OF WIND TURBINE BASED ON CNN-BIGRU

Liu Jun, Ge Lei, Zhao Xuanbo, Chen Zhengliang, An Bairen

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Abstract

To evaluate the health status of wind turbines, this paper proposes a CNN-BiGRU-based health evaluation method. Firstly, the quartile method is used to eliminate the abnormal data in the wind turbine supervisory control and data acquisition （SCADA） system, and the maximum relevance minimum redundancy （mRMR） algorithm is applied to select power-related features. Then,a CNN-BiGRU-based power prediction model is constructed to predict the wind turbine's output power.The convolutional neural network （CNN） extracts high-dimensional input features effectively, while the sparrow search algorithm （SSA） optimizes the parameters of the bidirectional gated recurrent unit （BiGRU） network. A benchmark distribution model is established based on the power prediction residuals of wind turbines in a healthy state, calculate the Mahalanobis distance （MD） from the residuals of real-time prediction results to the benchmark distribution model, and construct wind turbine health indicators based on this MD to evaluate the health status of wind turbine.

Key words

wind turbines / supervisory control and data acquisition,（SCADA） / power prediction / health status assessment / convolutional neural network / BIGRU

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Liu Jun, Ge Lei, Zhao Xuanbo, Chen Zhengliang, An Bairen. HEALTH STATUS ASSESSMENT OF WIND TURBINE BASED ON CNN-BIGRU[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 253-260 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1914

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