Design of Fault Feature Extraction Algorithm Model for Distribution Network Based on Wavelet Entropy

In this paper, a fault feature extraction algorithm model of distribution network based on wavelet entropy is designed, and combined with a large number of simulation data and field data for test and analysis, to improve the accuracy of line fault diagnosis and troubleshooting. The case verification results show that the feature extraction algorithm and feature gene library based on wavelet entropy can effectively identify the normal state, three-phase short circuit fault, two-phase short-circuit fault, two-phase grounding fault and single-phase grounding fault of distribution network transient signals, which meets the requirements of sustainable development of distribution network, and has good economic and social benefits.