A Novel Diagnosis Method Based on the Feature Selection Algorithm and Convolutional Neural Network for SF6 Gas-Insulated Equipment

Abstract

SF6-decomposition-based insulation degradation of gas-insulated equipment has attracted considerable research attention in the power industry. However, because limited feature mining has been performed for SF6 decomposition components, diagnostic accuracy is reduced. Therefore, in this study, a novel diagnosis strategy was proposed based on various feature selection algorithms and convolutional neural network (CNN). In all, 19 characteristic products were obtained by combining original characteristic products. Various feature selection algorithms were used to mine feature information. A novel CNN structure was designed. The 1-D input layer was reorganized into the 2-D layer to improve the operation efficiency of the CNN. The robustness and generalization ability of the network were improved by adjusting the training step size and convolution kernel size or number. The diagnosis accuracy of the SF6 decomposition component was improved to 99%. Thus, a set of accurate and effective diagnosis schemes was proposed for the power industry.