Application of small sample virtual expansion and spherical mapping model in wind turbine fault diagnosis.

Due to the actual operation of the wind turbine, the collected fault data sets are limited, and it is difficult to realize fault diagnosis through the correlation between variables. To solve this problem, a fault diagnosis method based on virtual expansion and spherical mapping model is proposed in this paper. Firstly, Hermite interpolation is applied to discrete wind power data samples to obtain an interpolation curve about the characteristics of the sample, and a synchronous sampling method is adopted for the interpolation curve to construct a virtual sample. Then, the features of the virtual sample are mapped to a three-dimensional space. Define the spherical data model and perform spherical fitting in a three-dimensional coordinate system. Finally, feature extraction is performed on the fitted spherical surface for training and testing extreme learning machine (ELM). The distribution law of fault data in the spherical model is summarized. Using the data generated based on the Bootstrap method as a control group, comparative experiments were carried out in back-propagation neural network (BP), Probabilistic Neural Network (PNN), General Regression Neural Network (GRNN), and Support Vector Machine (SVM), which verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]