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.
引用
收藏
相关论文
共 50 条
  • [21] MODELING OF OVER VOLTAGES IN 10 kV CABLE DISTRIBUTION NETWORK AT SINGLE-PHASE FAULTS TO GROUND WITH ARC
    Boshnyaga, V. A.
    Postolatiy, V. M.
    Suslov, V. M.
    Clindukhov, A. N.
    PROBLEMELE ENERGETICII REGIONALE, 2013, (01): : 28 - 47
  • [22] Chukarich, an algorithm for calculating ground faults in single-phase power transformers
    Ilich, V.N.
    Dzhurich, M.B.
    Dzhurich, A.R.
    Elektrichestvo, 2004, (12): : 17 - 21
  • [23] A Fault Section Location Method Based on Energy Remainder of Generalized S-Transform for Single-phase Ground Fault of Distribution Networks
    Pang, Zhenjiang
    Du, Jun
    Jiang, Fan
    He, Lianjie
    Li, Yuling
    Qin, Lixiang
    Li, Yan
    PROCEEDINGS OF 2018 IEEE 3RD ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC 2018), 2018, : 1511 - 1515
  • [24] Semantic Segmentation-Based Intelligent Threshold-Free Feeder Detection Method for Single-Phase Ground Fault in Distribution Networks
    Hong, Cui
    Qiu, Heng-Yi
    Gao, Jian-Hong
    Lin, Shuyue
    Guo, Mou-Fa
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 9
  • [25] Detection of single-phase-to-ground faults in distribution networks based on Gramian Angular Field and Improved Convolutional Neural Networks
    Zhang, Qian
    Qi, Zhenxing
    Cui, Puyi
    Xie, Min
    Din, Jinjin
    ELECTRIC POWER SYSTEMS RESEARCH, 2023, 221
  • [26] Research on single-phase grounding fault location technology in distribution networks based on impedance method
    Wang, Sha
    Wei, Shengkai
    AIP ADVANCES, 2024, 14 (09)
  • [27] Optimized deep learning based single-phase broken fault type identification for active distribution networks
    Wu, Yan
    Meng, Xiaoli
    Guan, Shilei
    Wu, Yan
    Song, Xiaohui
    Gu, Lingyun
    Zhou, Feiyan
    Liu, Jinjie
    ENERGY REPORTS, 2023, 9 : 119 - 126
  • [28] Overvoltage Monitoring for Single-Phase Arc-To-Ground Failures in Distribution Cable Networks
    V. E. Kachesov
    V. N. Larionov
    A. G. Ovsyannikova
    Power Technology and Engineering, 2002, 36 (4) : 207 - 213
  • [29] Optimized deep learning based single-phase broken fault type identification for active distribution networks
    Wu, Yan
    Meng, Xiaoli
    Guan, Shilei
    Wu, Yan
    Song, Xiaohui
    Gu, Lingyun
    Zhou, Feiyan
    Liu, Jinjie
    ENERGY REPORTS, 2023, 9 : 119 - 126
  • [30] Identification Method for Single-line-to-ground Faults with Line Break Based on Phasor Measurement in Distribution Networks
    Liu, Yadong
    Li, Zichang
    Yan, Yingjie
    He, Guanghui
    Fang, Jian
    Li, Kejun
    Jiang, Xiuchen
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2023, 11 (03) : 907 - 916