Sihua Wang1,2, Wenhui Zhang1,2,*, Gaofei Zheng1,2, Xujie Li1,2, Yougeng Zhao1,2
Energy Engineering, Vol.119, No.6, pp. 2431-2445, 2022, DOI:10.32604/ee.2022.020779
- 14 September 2022
Abstract In order to improve the condition monitoring and fault diagnosis of wind turbines, a stacked noise reduction autoencoding network based on group normalization is proposed in this paper. The network is based on SCADA data of wind turbine operation, firstly, the group normalization (GN) algorithm is added to solve the problems of stack noise reduction autoencoding network training and slow convergence speed, and the RMSProp algorithm is used to update the weight and the bias of the autoenccoder, which further optimizes the problem that the loss function swings too much during the update process. Finally, More >
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