Identification Technology of High Loss Components in Power Grid Based on Neural Network Data Analysis Method

In view of the problems that most of the research on power grid loss is not comprehensive and the identification of loss types is not accurate, a high loss component identification technology based on neural network data analysis method is proposed in this paper. Based on the comprehensive analysis of line loss and transformer loss, the station loss types are divided into six categories, and the station meter data are input into the fully connected neural network model for learning and analysis, so as to complete the identification of high loss components. Based on the experimental analysis of 109 electricity meter data samples in a certain station area, the results show that the recognition accuracy of the proposed technology for electricity meters without abnormal loss is as high as 98.5%, the overall recognition accuracy is 93.4%, and the recognition time is 0.35 s, which is better than other comparison technologies.