Deep‐Belief‐Networks Based Fault Classification in Power Distribution Networks

Abstract

Accurate fault classification is the premise of fault location and management study in a power distribution network. In most of the traditional fault classification methods used in power distribution network, the characteristic quantities are selected by experience, which will increase the uncertainty of fault classification results. A novel fault classification method based on deep belief networks (DBN) is proposed in this paper. Samples of fault current and voltage are preprocessed by min–max standardization and waveform splicing firstly, then they are used to train the DBN together with fault type label. Characteristic quantities of the current and voltage will be automatically extracted by the well‐trained DBN model, and the reliable fault type classification of distribution network can be realized. Simulation and experimental results show that the fault classification method is suitable for distribution network, and it has not only characteristics of obvious fault feature extraction and high fault classification accuracy, but also has good adaptability while the neutral grounding modes changing or used in power distribution network with distributed generator. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.