A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation

Aimed at the problem that the complicated working condition of wind turbine and the lack of sufficient target samples, which makes it difficult to conduct effective health condition monitoring (HCM), a novel method based on composite variational mode entropy (CVME) and weighted distribution adaptation (WDA) is proposed in this paper. A series of mode components are first obtained by performing variational mode decomposition (VMD) on the signals under various working conditions. The mode components are analyzed on multi-scale, and then the fuzzy entropy is extracted at different scales. The extracted CVME is input as a feature vector into WDA. The WDA method can effectively reduce the discrepancy of data distribution between the source and target domains by adjusting the weight of the marginal distribution and the conditional distribution, and solve the problem of class imbalance in domains by a weight matrix. The transferability evaluation is used to select the feature sets under auxiliary working conditions with high similarity to the target feature set as the source samples. Finally, the source and target samples are input into the classifier for training and testing. Compared with traditional fault diagnosis methods, experiment shows that the proposed method has higher accuracy in wind turbine fault diagnosis under variable working conditions. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

A novel method based on CVME and WDA is proposed in this paper for wind turbine fault diagnosis. By extracting fuzzy entropy from mode components obtained through VMD, and adjusting data distribution weight using WDA, the method shows higher accuracy in wind turbine fault diagnosis under variable working conditions compared to traditional methods.