Wind turbine abnormal status detection based on self-attention and domain adaptation

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (10) : 269-277.

FAULT DIAGNOSIS ANALYSIS

Wind turbine abnormal status detection based on self-attention and domain adaptation

WANG Xiaoxia*1,2, ZHENG Xiaojian1, LIU Pu1, WANG Rongkang1, WANG Tao3

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Abstract

To address the issue of insufficient historical data for newly installed wind turbines and the significant data distribution differences between various turbines, this paper proposes an abnormal status detection method for wind turbines that integrates a self-attention mechanism with domain adaptation networks. Firstly, an encoder-decoder structure is employed to perform feature reconstruction from both source and target domain turbines in order to capture latent wind power patterns and domain-specific information. Then, a self-attention module is designed to extract cross-domain shared features through adversarial learning with a domain discriminator, and domain-specific information is automatically weighted based on the matching degree of cross-domain shared features, enabling dynamic feature reconstruction and thereby improving the model's adaptability to changes in the data distribution across different units. Finally, the reconstruction error is calculated as the abnormal score for anomaly detection. Results from actual wind turbine operation data demonstrate that this method can efficiently identify abnormal data with limited historical data and significantly improve detection accuracy compared to other deep learning and deep transfer learning methods.

Key words

wind turbines / anomaly detection / domain adaptation / self-attention mechanism / adversarial training

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WANG Xiaoxia1, 2, ZHENG Xiaojian1, LIU Pu1, WANG Rongkang1, WANG Tao3. Wind turbine abnormal status detection based on self-attention and domain adaptation[J]. Journal of Vibration and Shock, 2025, 44(10): 269-277

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