English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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, in the last layer of the network, the softmax activation function is used to classify the results, and the output of the network is transformed into a probability distribution. The selected wind turbine SCADA data was substituted into the pre-improved and improved stacked denoising autoencoding (SDA) networks for comparative training and verification. The results show that the stacked denoising autoencoding network based on group normalization is more accurate and effective for wind turbine condition monitoring and fault diagnosis, and also provides a reference for wind turbine fault identification.

Fault Diagnosis of Wind Turbine Generator with Stacked Noise Reduction Autoencoder Based on Group Normalization