Research on an Improved Convolutional Neural Network Fault Diagnosis Method for Exciter System

Abstract

ABSTRACT At present, China’s power grid has entered a new stage of ultra-high voltage, long-distance, large-capacity, large-unit, AC and DC hybrid interconnection grid, and the generator excitation system has an important impact on the stability of the power system. As the core component of the excitation system, the exciter plays a vital role in the safe operation of the excitation system. In view of the above problems, this paper proposes an improved CNN exciter fault diagnosis research method. To be able to learn more abundant fault features, this article first builds a dilated convolutions which is types of deep convolutional neural network (DCNN) to expand the receptive field of the convolution kernel. Then build the Pythagorean Spatial Pyramid Pooling Layer (PTSPP) to further enhance the feature information of the extracted samples. Finally, this article will generate two-dimensional matrix samples from the collected excitation voltage and excitation current signals for model training. The experimental results show that the proposed PTSPP-DCNN method has high classification accuracy in the fault diagnosis of the exciter system. The comparison results show that the fault classification accuracy of the proposed method is higher than other deep learning methods.