ResNest-SVM-based method for identifying single-phase ground faults in active distribution networks

被引:1
|
作者
Lian, Qingwen [1 ]
Luo, Xiang [2 ]
Lin, Dong [2 ]
Lin, Caihua [2 ]
Chen, Bingxi [3 ]
Guo, Ziyi [2 ]
机构
[1] State Grid Fujian Electric Power Corporation, Fujian, Gulou, China
[2] State Grid Fujian Electric Power Co., LTD., Electrical Power Research Institution, Fuzhou, China
[3] Fujian Zhongshi Institute Electric Power Adjustment Test Co., LTD., Fuzhou, China
关键词
Active distribution network - Active distributions - Distributed generators - Distributed line selection - Fault point - Line selections - Neural-networks - Resnest-SVM - Single phase grounding faults - Zero-mode transient;
D O I
10.3389/fenrg.2024.1501737
中图分类号
学科分类号
摘要
Single-phase grounding fault is the most common fault type in the distribution network. An accurate and effective single-phase grounding fault identification method is a prerequisite for maintaining the safe and stable operation of the power grid. Most neutral points of the active distribution network are grounded through arc suppression coils. In the active distribution network, the power supply in the network changes from one to multiple, which may change the direction of the fault current. In this paper, the superposition theorem is used to analyze the difference in the boosting effect of different types of distributed generators (DG) on line mode current in the sequence network diagram when DG is connected upstream or downstream of the fault point. Secondly, the composition of the zero-mode transient current of the fault line is analyzed. A judgment method based on the superposition diagram of transient zero-sequence voltage and current is proposed. Then, this paper improves the ResNest network and modifies the classifier of the last fully connected layer to SVM. Finally, the model in PSCAD is used to simulate single-phase grounding faults to obtain the training set and validation set. These datasets are used to train and test AlexNet, ResNet50, ResNeSt, and ResNeSt-SVM. The results show that under different fault points, transition resistances, DG access upstream and downstream of the fault point, and different fault initial phase angles, the ResNest-SVM model method can accurately identify the fault line and has better anti-noise ability than the other three network structures. Copyright © 2024 Lian, Luo, Lin, Lin, Chen and Guo.
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