CROSS-DOMAIN ABNORMAL STATE ASSESSMENT FOR WIND TURBINE BLADES VIA INTEGRATION OF TRANSFER LEARNING AND DYNAMIC DOMAIN ADAPTATION

Lin Zhichao; Wang Jinming; Li Zhonghu; Zhang Fei

doi:10.19912/j.0254-0096.tynxb.2025-0679

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 726-737. DOI: 10.19912/j.0254-0096.tynxb.2025-0679

CROSS-DOMAIN ABNORMAL STATE ASSESSMENT FOR WIND TURBINE BLADES VIA INTEGRATION OF TRANSFER LEARNING AND DYNAMIC DOMAIN ADAPTATION

Lin Zhichao, Wang Jinming, Li Zhonghu, Zhang Fei

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Abstract

To address the insufficient generalization ability of abnormal state assessment caused by cross-domain data distribution shifts in wind turbine blades under complex operating conditions, this paper proposes a Dynamic Domain Adaptive Residual Attention Network （DDA-RAN） based cross-domain anomaly assessment method. This approach integrates transfer learning with a dynamic attention mechanism, achieving collaborative optimization of domain shift through a bidirectional dynamic calibration strategy. Specifically, a Dynamic Domain Adaptive Attention Residual Block （DDAAR-Block） is designed. It utilizes a dynamic domain adaptive attention mechanism combined with switchable domain identifiers to adaptively activate either the source or target domain branch, balancing the representation of domain-invariant and domain-specific features. A cross-domain alignment module is constructed, implementing phased dynamic training via gradient truncation and a gating mechanism to promote cross-domain feature interaction and distribution alignment. An adaptive multi-scale residual block is adopted to extract deep and shallow temporal features, enhanced by global adaptive pooling for feature robustness. Furthermore, a regularized Multi-Layer Perceptron （MLP） classifier is introduced to improve model generalization. Experimental results demonstrate that in cross-domain assessment tasks for blade cracking and icing, the proposed method significantly outperforms mainstream comparison models （e.g., ADDA, DANN） in terms of target domain accuracy （up to 99.36%）, F1-score （99.35%）, and MCC （98.71%）. The accuracy shows an average improvement of 2.1%-3.8%. This provides a valuable reference for cross-domain anomaly assessment in industrial scenarios.

Key words

wind turbine blades / transfer learning / residual neural networks / state assessment / dynamic domain adaptation / cross-domain alignment

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Lin Zhichao, Wang Jinming, Li Zhonghu, Zhang Fei. CROSS-DOMAIN ABNORMAL STATE ASSESSMENT FOR WIND TURBINE BLADES VIA INTEGRATION OF TRANSFER LEARNING AND DYNAMIC DOMAIN ADAPTATION[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 726-737 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0679

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