Wind Turbine Fault Classification Method Based on Adversarial Training and Transformer
The complexity and diversity of wind turbine fault classification severely affect the efficiency of wind power generation.Conventional manual methods display low efficiency and accuracy.Existing deep learning models perform ineffectively in real environments owing to the data noise interference.To improve the classification performance and robustness of wind turbine fault classification models in real environments,this paper proposes a fault classification method based on adversarial training and Transformer.First,by introducing a one-dimensional convolution and Gated Linear Unit(GLU)enhanced attention mechanism for learning local features,the paper improves the sensitivity of the model to local features by retaining local information that is overlooked straightforwardly.Second,combining with constraint factor-constrained adversarial samples improves the accuracy of adversarial sample generation.Finally,while eliminating incorrect samples,the feedback generation process enhances its anti-interference capability.The experimental results reveal that compared with five commonly used classification models,the proposed model achieves an average improvement in classification performance of 7.76％and minimal error compared with actual results.The locally enhanced attention mechanism and proposed adversarial training method improve the average classification performance of the model by 4.51％and 4.95％,respectively.The proposed model still maintains good performance in a noise environment ranging from 10％to 20％.This enhances its stability in real environments.The method improves the accuracy and enhances the generalization capability of the model.This is significant for improving wind turbine fault classification performance and robustness.
wind turbineGated Linear Unit(GLU)Transformer modeladversarial trainingfault classification
万方数据
维普
