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ARTICLE

Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network

Tao Guo, Gang Tian, Zhimin Ao*, Xi Fang, Lili Wei, Fei Li
Tongren Power Supply Bureau of Guizhou Power Grid Co., Ltd., Tongren, 554300, China
* Corresponding Author: Zhimin Ao. Email:
(This article belongs to this Special Issue: The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )

Energy Engineering 2022, 119(3), 985-996. https://doi.org/10.32604/ee.2022.018185

Received 06 July 2021; Accepted 15 September 2021; Issue published 31 March 2022

Abstract

Ground short circuit faults in current transmission lines are common in the power systems. In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines, a novel convolutional neural network (CNN) model is constructed to identify the short-circuit fault of the transmission line in the power system. The CNN model is mainly consisted of five convolutional layers, three max-pooling layers, one concatenate layer, one dropout layer, one fully connected layer, and a Softmax classifier. This method uses a fixed time window to intercept system short-circuit fault data, extracts the deep features of these data from the training samples, and then corresponds the extracted features to labels one-to-one. Finally, in PSCAD/EMTDC, the new England 10 machine 39 nodes are taken as an example to realize the simulation. The experimental results show that the CNN model can quickly and accurately identify the short-circuit fault types, and the optimal model accuracy rate reaches 99.95%. The results of this manuscript -have positive effect on enhancing the disaster prevention capability and the operation stability of transmission lines.

Keywords

Convolutional neural networks; transmission line; fault; classification

Cite This Article

Guo, T., Tian, G., Ao, Z., Fang, X., Wei, L. et al. (2022). Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network. Energy Engineering, 119(3), 985–996.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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