Hazard Identification Method for Distribution Networks Based on Generative Adversarial Networks

Abstract

Addressing the issues of untimely discovery and low identification accuracy of hazards in China’s distribution networks, this paper introduces a hazard identification method for distribution networks based on generative adversarial networks (GANs). The method employs a modular design comprising three parts: hazard traveling wave acquisition, hazard location, and hazard type identification. The hazard traveling wave acquisition part uses wavelet transform to extract the waveform of the initial hazard wave and records the arrival time at the device. The hazard location part improves the dual-end traveling wave location method to determine the hazard location, enhancing location accuracy. The hazard type identification part uses affinity propagation to cluster the hazard traveling waves, obtaining classification features of the hazard traveling waves, and then identifies distribution network hazards through GANs. Finally, the method was applied to a distribution network in a certain region, achieving a hazard identification accuracy of 81.3%. The identification results validate the effectiveness of the proposed method.