Fault Line Selection for Active Distribution Network Based on Domain Adaptive Transfer Learning

被引：1
作者：
Liu, Changyu [1 ]
Wang, Xiaojun [1 ]
Shang, Boyang [1 ]
Luo, Guoming [1 ]
Liu, Zhao [1 ]
机构：
[1] School of Electrical Engineering, Beijing Jiaotong University, Beijing
来源：
Gaodianya Jishu/High Voltage Engineering | 2024年 / 50卷 / 07期
基金：
中国国家自然科学基金;
关键词：
active distribution network; attention mechanism; convolutional neural network; domain adaptation; fault line selection; transfer learning;
D O I：
10.13336/j.1003-6520.hve.20230521
中图分类号：
学科分类号：
摘要：
Artificial intelligent model based on data driven, especially the convolutional neural network(CNN), has already achieved great performance in distribution network fault diagnosis. While CNN relies on massive data, and the performance of model will decrease severely due to the lack of data. We proposed an active distribution network fault line selection method based on domain adaptive transfer learning. Firstly, a CNN incorporated attention mechanism was built, and fault characteristics of transient zero sequence current in active distribution network were extracted. Then, domain adaptive transfer learning was adopted, and the distance between source domain and target domain was decrease by the maximum mean discrepancy function, to solve fault line selection with small samples. Finally, the proposed method was verified in active distribution network under different operating mode by Matlab/Simulink. Simulation results verify the proposed method can realize high accuracy and robust fault line selection in active distribution network with small samples. © 2024 Science Press. All rights reserved.
引用
收藏
页码：3050 / 3059
页数：9
相关论文
共 29 条
  • [1] XU Bingyin, LI Tianyou, XUE Yongduan, Relaying protection and automation of distribution networks, pp. 64-67, (2017)
  • [2] ZHUANG Shengbin, MIAO Xiren, JIANG Hao, Et al., A line selection method for single-phase high-impedance grounding fault in resonant grounding system of distribution network based on improved Euclidean-dynamic time warping distance, Power System Technology, 44, 1, pp. 273-281, (2020)
  • [3] LIANG Rui, XIN Jian, WANG Chonglin, Et al., Fault line selection in small current grounding system by improved active component method, High Voltage Engineering, 36, 2, pp. 375-379, (2010)
  • [4] ZHU Tao, Fault line selecting method in non-solidly-earthed network based on SCADA system, Power System Protection and Control, 47, 13, pp. 141-147, (2019)
  • [5] CHENG Qiming, GAO Jie, WANG Xiaowei, Et al., Fault line selection method based on optimized bistable denoising for non-solid-earthed network, High Voltage Engineering, 44, 11, pp. 3483-3492, (2018)
  • [6] ZHANG Yuxi, WANG Zengping, LI Zhenzhao, Et al., A new method of fault line selection in a distribution network based on characteristic frequency band transient reactive power, Power System Protection and Control, 51, 1, pp. 1-11, (2023)
  • [7] SHANG Liqun, LI Yao, Faulty line identification in distribution network based on combined modulus and S-transform energy entropy, Journal of Xi’an Jiaotong University, 56, 5, pp. 119-126, (2022)
  • [8] LIU Yugen, WANG Jiannan, MA Jinpei, Et al., Comprehensive fault line selection method for resonant grounded system combining wavelet packet transform with fifth harmonic method, High Voltage Engineering, 41, 5, pp. 1519-1525, (2015)
  • [9] JIN Tao, CHU Fuliang, A fault line-selection method in new distribution network with DG based on transient non-power frequency zero sequence current, Transactions of China Electrotechnical Society, 30, 17, pp. 96-105, (2015)
  • [10] SHU Hongchun, HUANG Haiyan, TIAN Xincui, Et al., Fault line selection in resonant earthed system based on morphological peak-valley detection, Automation of Electric Power Systems, 43, 1, pp. 228-233, (2019)
123