Abstract:

Analyze the hidden dangers existing in the sampling circuit of the secondary system, and in view of the defects that the existing methods are difficult to obtain large amount of data, based on the historical measurement data, the variational autoencoder is used to achieve data proliferation, and then the support vector machine model is trained on the proliferated data set, and the important parameters of the model are improved by using the grey wolf optimization algorithm, and the error range of the secondary system sampling is classified and checked. The experimental results show that the classification model trained by the proposed method has a good evaluation effect.

Key words: secondary system sampling loop, error evaluation, small sample size