Refined Diagnosis Method for Disconnected High-Resistance Grounding Faults in Medium-Voltage Distribution Lines

doi:10.11930/j.issn.1004-9649.202306046

Abstract

Abstract:

Multiple voltage drops or abnormal currents may occur in the power system, resulting in significant deformation and fluctuation of fault signal characteristics related to the medium voltage DC distribution network, exceeding the normal fluctuation range, leading to a decrease in the refinement of fault diagnosis. A refined diagnosis method for high resistance grounding faults in medium voltage distribution lines has been proposed. On the basis of constructing a high resistance grounding resistance model, the wavelet energy moment algorithm is used to obtain the characteristics of high resistance grounding faults in medium voltage distribution lines. The extracted fault features are input into the least squares multi-level support vector machine to achieve precise diagnosis of high resistance grounding faults in medium voltage distribution lines. The simulation results indicate that the difference in fault phase voltage waveform obtained by the proposed method is less than 2.3%; The similarity of fault phase current waveform is higher than 98%; The diagnosis time is relatively short, and the highest recognition rate during fault diagnosis can reach 98%, with an average recognition accuracy of 95%; The convergence value reaches 0.97. From this, it can be seen that the proposed method has strong anti-interference performance and can accurately identify high resistance grounding faults when photovoltaic energy is connected to medium voltage distribution lines, ensuring stable operation after photovoltaic energy is connected to medium voltage distribution lines.

Key words: medium voltage distribution line, high resistance ground fault, wavelet energy distance algorithm, feature extraction, least squares multi-level support vector machine

Peng ZHENG, Pengcheng HAN, Guodong WANG, Ying LOU. Refined Diagnosis Method for Disconnected High-Resistance Grounding Faults in Medium-Voltage Distribution Lines[J]. Electric Power, 2024, 57(4): 220-228.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202306046

https://www.electricpower.com.cn/EN/Y2024/V57/I4/220

Figures/Tables 14

References 21

1	倪萌, 王蓓蓓, 朱红, 等. 能源互联背景下面向高弹性的多元融合配电网双层分布式优化调度方法研究[J]. 电工技术学报, 2022, 37 (1): 208- 219.
	NI Meng, WANG Beibei, ZHU Hong, et al. Study of two-layer distributed optimal scheduling strategy for highly elastic multi-resource fusion distribution network in energy interconnection environment[J]. Transactions of China Electrotechnical Society, 2022, 37 (1): 208- 219.
2	韦明杰, 石访, 张恒旭, 等. 基于零序电流波形区间斜率曲线的配电网高阻接地故障检测[J]. 电力系统自动化, 2020, 44 (14): 164- 171.
	WEI Mingjie, SHI Fang, ZHANG Hengxu, et al. Detection of high impedance grounding fault in distribution network based on interval slope curves of zero-sequence current[J]. Automation of Electric Power Systems, 2020, 44 (14): 164- 171.
3	翁月莹, 陈翔宇, 肖新华, 等. 基于PSO和贝叶斯分类器的配电网高阻接地故障识别技术[J]. 电测与仪表, 2020, 57 (2): 52- 56.
	WENG Yueying, CHEN Xiangyu, XIAO Xinhua, et al. High impedance ground fault identification technology based on PSO and Bayes classifier[J]. Electrical Measurement & Instrumentation, 2020, 57 (2): 52- 56.
4	王宾, 崔鑫. 基于伏安特性动态轨迹的谐振接地系统弧光高阻接地故障检测方法[J]. 中国电机工程学报, 2021, 41 (20): 6959- 6968.
	WANG Bin, CUI Xin. Detection method of arc high resistance grounding fault in resonant grounding system based on dynamic trajectory of volt-ampere characteristic[J]. Proceedings of the CSEE, 2021, 41 (20): 6959- 6968.
5	刘战磊, 曾祥君, 喻锟, 等. 基于不平衡过电压动态抑制的谐振接地配电网单相接地故障保护新方法[J]. 电力系统保护与控制, 2021, 49 (8): 41- 49.
	LIU Zhanlei, ZENG Xiangjun, YU Kun, et al. A new method of single-phase grounding fault protection for a resonant grounding distribution network based on unbalanced overvoltage dynamic suppression[J]. Power System Protection and Control, 2021, 49 (8): 41- 49.
6	曹文思, 吴擎, 徐铭铭, 等. 基于分形理论与聚类分析的小电阻接地系统高阻接地保护方法[J]. 电力自动化设备, 2020, 40 (4): 204- 209, 224.
	CAO Wensi, WU Qing, XU Mingming, et al. Protection method based on fractal theory and cluster analysis for grounding fault with high resistance of low resistance grounding system[J]. Electric Power Automation Equipment, 2020, 40 (4): 204- 209, 224.
7	叶鑫杰, 兰生, 肖思捷, 等. 基于小波包能量熵和DBN的MMC-HVDC输电线路单极接地故障定位方法[J]. 南方电网技术, 2021, 15 (2): 82- 91.
	YE Xinjie, LAN Sheng, XIAO Sijie, et al. Single pole grounding fault location method of MMC-HVDC transmission line based on wavelet packet energy entropy and DBN[J]. Southern Power System Technology, 2021, 15 (2): 82- 91.
8	刘远龙, 刘伟生, 王鹏玮, 等. 基于频带有功功率系数的接地故障方向保护方法[J]. 山东大学学报(工学版), 2021, 51 (1): 76- 86.
	LIU Yuanlong, LIU Weisheng, WANG Pengwei, et al. Earth fault protection method based on frequency band power factor[J]. Journal of Shandong University (Engineering Science), 2021, 51 (1): 76- 86.
9	牛原, 秦文萍, 夏福良, 等. 基于零序电流投影分量比值的小电阻接地系统故障保护[J]. 太原理工大学学报, 2021, 52 (6): 928- 935.
	NIU Yuan, QIN Wenping, XIA Fuliang, et al. Fault protection based on the ratio of zero-sequence current projection components for low-resistance grounding system[J]. Journal of Taiyuan University of Technology, 2021, 52 (6): 928- 935.
10	洪梅, 刘科峰, 张栋, 等. 基于交叉小波分析方法的西太平洋副热带高压年际变率与热带海温及大气环流异常的相关性研究[J]. 热带气象学报, 2020, 36 (2): 166- 179.
	HONG Mei, LIU Kefeng, ZHANG Dong, et al. The correlation of the Western Pacific subtropical high interannual variability with tropical sst and atmospheric circulation anomalies based on the cross-wavelet analysis[J]. Journal of Tropical Meteorology, 2020, 36 (2): 166- 179.
11	张中旭, 李智蓉. 小波分析方法在定点形变日常跟踪中的应用研究[J]. 云南大学学报(自然科学版), 2020, 42 (6): 1121- 1128.
	ZHANG Zhongxu, LI Zhirong. Application of wavelet analysis method in daily tracking of fixed-point deformation[J]. Journal of Yunnan University (Natural Sciences Edition), 2020, 42 (6): 1121- 1128.
12	陈是扦, 彭志科, 周鹏. 信号分解及其在机械故障诊断中的应用研究综述[J]. 机械工程学报, 2020, 56 (17): 91- 107. DOI
	CHEN Shiqian, PENG Zhike, ZHOU Peng. Review of signal decomposition theory and its applications in machine fault diagnosis[J]. Journal of Mechanical Engineering, 2020, 56 (17): 91- 107. DOI
13	肖凌俊, 吕勇, 袁锐. MED与GMCP稀疏增强信号分解在滚动轴承故障诊断中的应用[J]. 机械科学与技术, 2020, 39 (2): 165- 173.
	XIAO Lingjun, LÜ Yong, YUAN Rui. Application of MED and GMCP sparse enhanced signal decomposition in rolling bearing fault diagnosis[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39 (2): 165- 173.
14	翟文鹏, 张小内, 侯惠让, 等. 基于小波能量矩的嗅觉脑电信号识别[J]. 生物医学工程学杂志, 2020, 37 (3): 399- 404.
	ZHAI Wenpeng, ZHANG Xiaonei, HOU Huirang, et al. Olfactory electroencephalogram signal recognition based on wavelet energy moment[J]. Journal of Biomedical Engineering, 2020, 37 (3): 399- 404.
15	王耀庆, 孙建平, 李冰, 等. 基于小波变换和LSTM的短期风速预测研究[J]. 计算机仿真, 2021, 38 (2): 438- 443.
	WANG Yaoqing, SUN Jianping, LI Bing, et al. Short-term wind speed forecasting study based on wavelet transform and lstm[J]. Computer Simulation, 2021, 38 (2): 438- 443.
16	常春, 梅检民, 赵慧敏, 等. 基于局部切空间排列和最小二乘支持向量机的气缸压力识别[J]. 振动与冲击, 2020, 39 (13): 16- 21, 63.
	CHANG Chun, MEI Jianmin, ZHAO Huimin, et al. Recognition of cylinder pressure based on LTSA-LSSVM[J]. Journal of Vibration and Shock, 2020, 39 (13): 16- 21, 63.
17	吴兆立. 基于蜻蜓算法和最小二乘支持向量机的矿井突水水源判别[J]. 中国矿业, 2021, 30 (2): 91- 94.
	WU Zhaoli. Identification of mine water inrush source based on dragonfly algorithm and least squares support vector machine[J]. China Mining Magazine, 2021, 30 (2): 91- 94.
18	李茂青, 刘建强, 高锋阳, 等. 基于受控拉格朗日函数的永磁同步电动机控制器设计[J]. 控制理论与应用, 2020, 37 (6): 1406- 1412.
	LI Maoqing, LIU Jianqiang, GAO Fengyang, et al. Stabilization controller design of permanent magnet synchronous motor based on controlled Lagrangians[J]. Control Theory & Applications, 2020, 37 (6): 1406- 1412.
19	闫广峰, 岑敏仪. 引入拉格朗日算子的最佳线性回归模型选择[J]. 大地测量与地球动力学, 2021, 41 (11): 1111- 1117.
	YAN Guangfeng, CEN Minyi. Optimum linear regression model selection algorithm with Lagrange multipliers[J]. Journal of Geodesy and Geodynamics, 2021, 41 (11): 1111- 1117.
20	李琳, 王培培, 谷鹏, 等. 基于LU分解和交替最小二乘法的分布式奇异值分解推荐算法[J]. 模式识别与人工智能, 2020, 33 (1): 32- 40.
	LI Lin, WANG Peipei, GU Peng, et al. Distributed singular value decomposition recommendation algorithm based on LU decomposition and alternating least square[J]. Pattern Recognition and Artificial Intelligence, 2020, 33 (1): 32- 40.
21	伍毅, 盛丽, 潘海洋, 等. 基于迁移最小二乘支持矩阵机的滚动轴承故障诊断方法[J]. 振动与冲击, 2022, 41 (21): 53- 59.
	WU Yi, SHENG Li, PAN Haiyang, et al. Fault diagnosis method of rolling bearing based on transferleast squares support matrix machine[J]. Journal of Vibration and Shock, 2022, 41 (21): 53- 59.

线路	正序电阻/Ω	负序电阻/Ω	正序电感/mH	零序电感/mH	正序电容/μF	负序电容/μF
架空线路	0.20	1.25	1.32	0.54	0.07	0.03
缆线混合线路	0.32	2.36	2.58	0.96	0.33	0.27
电缆线路	0.27	3.51	5.25	1.62	0.43	0.35

线路	正序电阻/Ω	负序电阻/Ω	正序电感/mH	零序电感/mH	正序电容/μF	负序电容/μF
架空线路	0.20	1.25	1.32	0.54	0.07	0.03
缆线混合线路	0.32	2.36	2.58	0.96	0.33	0.27
电缆线路	0.27	3.51	5.25	1.62	0.43	0.35

时间/s	电压/kV
时间/s	实际电压	所提方法	文献[4] 方法	文献[5] 方法	文献[6] 方法	文献[7] 方法
0.0	–0.1	0.10	–0.1	–0.3	–0.2	–0.1
0.2	0.1	0.10	0.1	–0.3	–0.2	–0.1
0.4	–0.1	0.10	–0.1	–0.3	–0.2	–0.2
0.6	0.1	–0.05	0.1	–0.2	–0.2	–0.1
0.8	–0.7	–0.80	0.7	–1.0	–1.0	0.5
1.0	0.2	0.20	0.4	–0.4	–0.3	–0.1
1.2	0.2	0.20	0.4	–0.2	–0.1	–0.1
1.4	0.2	0.20	–0.2	–0.2	–0.1	–0.1

时间/s	电压/kV
时间/s	实际电压	所提方法	文献[4] 方法	文献[5] 方法	文献[6] 方法	文献[7] 方法
0.0	–0.1	0.10	–0.1	–0.3	–0.2	–0.1
0.2	0.1	0.10	0.1	–0.3	–0.2	–0.1
0.4	–0.1	0.10	–0.1	–0.3	–0.2	–0.2
0.6	0.1	–0.05	0.1	–0.2	–0.2	–0.1
0.8	–0.7	–0.80	0.7	–1.0	–1.0	0.5
1.0	0.2	0.20	0.4	–0.4	–0.3	–0.1
1.2	0.2	0.20	0.4	–0.2	–0.1	–0.1
1.4	0.2	0.20	–0.2	–0.2	–0.1	–0.1

时间/s	电压/kV
时间/s	实际电压	所提方法	文献[4] 方法	文献[5] 方法	文献[6] 方法	文献[7] 方法
0.0	0.1	0.1	0.2	0.1	0.1	0.1
0.2	–0.1	0.0	–0.1	–0.1	0.1	–0.1
0.4	0.1	–0.1	0.1	0.1	0.2	0.2
0.6	–0.5	–0.6	0.1	0.0	–0.1	0.1
0.8	0.0	0.1	–0.4	–0.3	–0.5	–0.4
1.0	–0.1	–0.1	–0.2	–0.1	0.3	–0.1
1.2	0.1	0.1	0.1	0.1	0.1	0.1
1.4	0.1	0.1	0.1	0.1	0.1	–0.1