Yaw Noise Fault Detection for Wind Turbines Based on ResNet-Transformer Model - CHEN Yanan; HU Kaikai; LI Ziyuan; WANG Lipeng (Kongzhi Yu Xinxi Jishu)
Wind turbines are among the core components of the new power system, operating in harsh environments and subjected to various uncertainties that make them prone to faults. The yaw system, as a critical component of wind turbines, is particularly susceptible to faults. To enhance the accuracy of diagnosing yaw noise faults in wind turbines, this paper develops a deep residual network (ResNet)-Transformer model based on acoustic signals. Firstly, the model employs the residual structure of ResNet to extract local features from the acoustic signals, enhancing sensitivity to subtle signal variations. Secondly, it utilizes the multi-head attention mechanism of the Transformer to capture global features, facilitating the extraction of global information from original signals across different time steps and thereby improving the model's flexibility and robustness in capturing long-term features. Finally, by integrating both local and global information, the model effectively balances micro-level details with macro-level dependencies, enabling accurate identification and classification of critical features in complex acoustic signals. Experimental results demonstrated that the proposed model achieved an accuracy of up to 96.88% in diagnosing yaw faults based on acoustic signals, providing a novel technical approach for future rapid and targeted diagnostics to ensure maintenance and operational safety in wind turbines.