Application of Convolutional Neural Network in Fault Line Selection of Distribution Network

• Published in: Journal of Applied Science and Engineering Vol. 25; no. 1; pp. 195 - 205
• Main Authors: Jingjing Tian, Fang Geng, Feng Zhao, Fengyang Gao, Xinqiang Niu
• Format: Journal Article
• Language: English
• Published: 淡江大學 23.07.2022; Tamkang University Press
• Subjects: convolutional neural network; fault line selection; shared weights; time-frequency energy matrix; time-frequency energy matrix; shared weights; convolutional neural network; Fault line selection
• ISSN: 2708-9967; 2708-9975
• DOI: 10.6180/jase.202202_25(1).0020

Aiming at the problem that the effect of the existing fault line selection methods is mainly determined by the fault features constructed by manual extraction, and the fault feature extraction process is complex and time-consuming, a new method based on convolutional neural network (CNN) is proposed. Firstly, the fault voltage and current signal data are collected, and the time-frequency energy matrix of fault signal is constructed by HHT band-pass filtering method, which is regarded as the two-dimensional matrix form of input data of CNN. Then the time-frequency energy matrix is input into the CNN, and the fault features are extracted autonomously through convolution layer and pooling layer of the network, which is used to train the network to realize fault line selection and fault phase judgment. Finally, the results show that the method not only has high accuracy of fault line selection, but also can complete the fault line selection and fault phase judgment at the same time without adjusting any parameters, which realize the shared weights of the two non-independent problems. Meanwhile, under the influence of noise interference, compensation degree, network structure change and other factors, the proposed method has good robustness. However, compared with the FCM, SVM, DNN and DBN, CNN can still identify the faulty line and keep the optimal accuracy in the case of two-point grounding fault, and other methods have errors in line selection or low accuracy. Therefore, the experimental results also provide a new idea for fault line selection of distribution network.