Research on Comprehensive Analysis Method for Transmission Line Faults Based on Multi-Source Data

Abstract

Transmission lines are subject to various faults due to extreme weather conditions. Monitoring can provide multiple sources of data on the transmission lines. By leveraging the advantages of Bayesian estimation and neural networks, a comprehensive analysis method for transmission line faults based on multi-source data fusion is proposed. This method uses Bayesian estimation to analyze and obtain data strongly related to fault risks. and applies neural networks to construct a fault analysis model. Taking wind-induced faults as an example, the method analyzes a case study of typical transmission line data. The results demonstrate that the proposed method can effectively analyze the occurrence of wind-induced faults on the lines, providing technical support for intelligent operation and maintenance of the transmission lines.