Fault section location method of distribution network based on wavelet energy entropy and artificial neural network

It is of great significance to locate faults in distribution systems quickly. Traditional methods for locating fault sections in distribution networks are mostly based on rules or physical models. However, with the increasing complexity of the topological structure of distribution networks and the increasing number of distributed power sources, traditional methods are difficult to meet the current needs of locating fault sections in distribution networks. Based on this, this paper proposes a fault section location scheme for distribution networks based on wavelet energy entropy and artificial neural networks. First, the effective values of voltage and current data are extracted to train the model, and correlation analysis is used to optimize the features. An improved IEEE33 node data set containing distributed power sources was built, and different types of fault data at different section locations were simulated. The proposed method was used for training and verification, which demonstrated the rationality and applicability of the proposed method. It could reach 97.38%.