基于综合特征矩阵的配电网故障判别方法

doi:10.11930/j.issn.1004-9649.202003220

中国电力 ›› 2021, Vol. 54 ›› Issue (11): 125-132.DOI: 10.11930/j.issn.1004-9649.202003220

基于综合特征矩阵的配电网故障判别方法

符金伟¹, 史常凯¹, 尹惠¹, 关石磊¹, 王安琪², 王越²

1. 中国电力科学研究院有限公司,北京 100192;
2. 中国农业大学信息与电气工程学院,北京 100083

收稿日期:2020-03-30 修回日期:2020-12-14 出版日期:2021-11-05 发布日期:2021-11-16
作者简介:符金伟(1988-),女,工程师,硕士,从事配电自动化系统及配网状态检修研究,E-mail:fujinwei@epri.sgcc.com.cn;王越(1982-),男,通信作者,副教授,从事电力系统可靠性与风险决策研究,E-mail:wyue@cau.edu.cn
基金资助:
国家电网有限公司科技项目(配电网故障模拟与试验检测平台关键技术研究与开发，PD71-17-009)

Distribution Network Fault Type Identification Method Based on Feature-summarizing Matrix

FU Jinwei¹, SHI Changkai¹, YIN Hui¹, GUAN Shilei¹, WANG Anqi², WANG Yue²

1. China Electric Power Research Institute, Beijing 100192, China;
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China

Received:2020-03-30 Revised:2020-12-14 Online:2021-11-05 Published:2021-11-16
Supported by:
This work is supported by Science and Technology Project of SGCC (Research and Development of Key Technologies for Distribution Network Fault Simulation and Test Detection Platform, No.PD71-17-009)

摘要/Abstract

摘要： 配电网短路故障类型的快速识别是保证故障精准可靠切除的基础。提出了一种基于综合特征矩阵的配电网故障判别方法，该方法综合考虑长距离输电、非线性负载投切、数据量残缺、噪声和高阻抗接地等情况下的配电网短路故障类型识别的难点，基于Hilbert变换得到的电压均方根（RMS）值、离散傅立叶变换得到的电压谐波信息及Hilbert-Huang变换得到的电压突变信息构造特征值进行故障类型判别。通过搭建含3条馈线的10 kV辐射配电网络Simulink仿真测试系统，对所提的综合特征矩阵方法进行了验证，并与经典故障检测方法进行了对比分析，论证了所提方法的精度优势。

关键词: 配电网, 故障检测, 综合特征矩阵, Hilbert变换, 离散傅立叶变换, Hilbert-Huang变换

Abstract: Fast identification of short-circuit fault types of distribution network is the basis for accurate fault removal. A method for detecting short-circuit fault types in distribution networks is proposed based on comprehensive feature-summarizing matrix. This method comprehensively considers the difficulties in short-circuit fault-type identification of distribution networks in the case of long-distance transmission, non-linear load switching, incomplete data volume, noise and high-impedance grounding, and the fault types are identified based on the voltage RMS value obtained by Hilbert transform, the voltage harmonics obtained from the Discrete Fourier transform and the converted voltage information by the Hilbert-Huang transform. The proposed feature-summarizing matrix method was verified through a Simulink simulation test system of a 10kV radial network consisting of three feeders, and compared with the traditional methods, which has proved the accuracy advantages of the proposed method.

Key words: distribution network, fault detection, feature-summarizing matrix method, Hilbert transform, discrete Fourier transform, Hilbert-Huang transform

符金伟, 史常凯, 尹惠, 关石磊, 王安琪, 王越. 基于综合特征矩阵的配电网故障判别方法[J]. 中国电力, 2021, 54(11): 125-132.

FU Jinwei, SHI Changkai, YIN Hui, GUAN Shilei, WANG Anqi, WANG Yue. Distribution Network Fault Type Identification Method Based on Feature-summarizing Matrix[J]. Electric Power, 2021, 54(11): 125-132.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202003220

https://www.electricpower.com.cn/CN/Y2021/V54/I11/125

参考文献

[1] 胡亚娟, 曹志, 朱绍杰, 等. 基于Petri网的故障区段定位与故障模式辨识方法[J]. 智慧电力, 2019, 47(8): 63–68, 75
HU Yajuan, CAO Zhi, ZHU Shaojie, et al. Fault section location and fault mode identification method based on petri nets[J]. Smart Power, 2019, 47(8): 63–68, 75
[2] 舒凡娣, 谢嘉晟, 廖晓娇, 等. 结合粒子群算法和穷举法的配电网故障诊断方法[J]. 智慧电力, 2019, 47(1): 94–99
SHU Fandi, XIE Jiasheng, LIAO Xiaojiao, et al. Distribution network fault diagnosis method combining particle swarm optimization and exhaustive method[J]. Smart Power, 2019, 47(1): 94–99
[3] 万信书, 方连航, 梁钰, 等. 基于三相电流幅值分析的小电流单相接地故障区段定位方法[J]. 电力科学与技术学报, 2020, 35(4): 49–57
WAN Xinshu, FANG Lianhang, LIANG Yu, et al. Method of fault section location for the small current single-phase-to-ground fault based on the amplitude analysis of three-phase current[J]. Journal of Electric Power Science and Technology, 2020, 35(4): 49–57
[4] 张磐, 张愉, 丁一, 等. 基于微扰法的低压有源配电网故障定位方法[J]. 中国电力, 2018, 51(12): 63–71
ZHANG Pan, ZHANG Yu, DING Yi, et al. Fault location for low voltage active distribution network based on perturbation method[J]. Electric Power, 2018, 51(12): 63–71
[5] GUO M F, YANG N C, CHEN W F. Deep-learning-based fault classification using Hilbert–Huang transform and convolutional neural network in power distribution systems[J]. IEEE Sensors Journal, 2019, 19(16): 6905–6913.
[6] 邵庆祝, 杨畅, 谢民, 等. 基于RTDS的光伏接入配电网故障特征研究[J]. 电力系统保护与控制, 2019, 47(22): 1–8
SHAO Qingzhu, YANG Chang, XIE Min, et al. Research on fault characteristics of distributed photovoltaic system in distributed network based on RTDS[J]. Power System Protection and Control, 2019, 47(22): 1–8
[7] 马如伟. 含有分布式电源的配电网故障检测与识别研究[D]. 西安: 西安理工大学, 2017.
MA Ruwei. The research for fault detection and identification of distribution network with distributed generator supply[D]. Xi'an: Xi'an University of Technology, 2017.
[8] 胡伟. 基于测控终端的配电网故障区域及故障类型辨识方法研究[D]. 长沙: 湖南大学, 2017.
HU Wei. Identification method of fault area and fault type of distribution network based on feeder terminal unit[D]. Changsha: Hunan University, 2017.
[9] ABDELGAYED T S, MORSI W G, SIDHU T S. Fault detection and classification based on Co-training of Semisupervised Machine learning[J]. IEEE Transactions on Industrial Electronics, 2018, 65(2): 1595–1605.
[10] 高晓策. 含分布式电源的配电网故障辨识及定位的研究[D]. 秦皇岛: 燕山大学, 2018.
GAO Xiaoce. Research on fault identification and location of distribution network with distributed generation[D]. Qinhuangdao: Yanshan University, 2018.
[11] 马军, 余英. 配电网在线故障检测技术的研究[J]. 电力与能源, 2016, 37(6): 695–698
MA Jun, YU Ying. Research on distribution network on-line fault detection technology[J]. Power & Energy, 2016, 37(6): 695–698
[12] 李艳. 电动汽车充电对配电网的影响及电压扰动检测研究[D]. 长沙: 长沙理工大学, 2016.
LI Yan. Research on the influence of electric vehicle charging to distribution network and voltage disturbance detection[D]. Changsha: Changsha University of Science and Technology, 2016.
[13] 洪翠, 付宇泽, 郭谋发, 等. 改进多分类支持向量机的配电网故障识别方法[J]. 电子测量与仪器学报, 2019, 33(1): 7–15
HONG Cui, FU Yuze, GUO Moufa, et al. Identification method of distribution network faults based on improved multi-classification support vector machine[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(1): 7–15
[14] 许波, 王振宇. 离散傅里叶变换误差分析与参数设置[J]. 电气电子教学学报, 2020, 42(4): 52–55
XU Bo, WANG Zhenyu. Error analysis and parameters setting of discrete Fourier transform[J]. Journal of Electrical & Electronic Education, 2020, 42(4): 52–55
[15] 张国军, 任荣, 韩静静, 等. 希尔伯特变换在配电网故障选线中的应用[J]. 电力系统保护与控制, 2014, 42(10): 23–28
ZHANG Guojun, REN Rong, HAN Jingjing, et al. Application of Hilbert transform in faulty line selection of distribution network[J]. Power System Protection and Control, 2014, 42(10): 23–28
[16] 郭铁梁, 李志军, 张文祥. 基于希尔伯特变换的带通信号包络频谱分析及Matlab仿真[J]. 高师理科学刊, 2020, 40(8): 72–77
GUO Tieliang, LI Zhijun, ZHANG Wenxiang. Envelope spectrum analysis and Matlab simulation of band signal based on Hilbert transform[J]. Journal of Science of Teachers' College and University, 2020, 40(8): 72–77
[17] KOLEY E, SHUKLA S K, GHOSH S, et al. Protection scheme for power transmission lines based on SVM and ANN considering the presence of non-linear loads[J]. IET Generation, Transmission & Distribution, 2017, 11(9): 2333–2341.
[18] 边辉, 孙屹岱. 基于HHT的风力发电系统电压暂降检测研究[J]. 计算技术与自动化, 2020, 39(2): 46–49
BIAN Hui, SUN Yidai. Research on voltage sag detection of wind power system based on HHT[J]. Computing Technology and Automation, 2020, 39(2): 46–49
[19] 刘洋, 王鹏, 孙旺, 等. 基于经验模态分解的配电网故障选线方法研究[J]. 供用电, 2018, 35(8): 67–72
LIU Yang, WANG Peng, SUN Wang, et al. Research on fault line selection method of distribution network based on EMD[J]. Distribution & Utilization, 2018, 35(8): 67–72
[20] 符金伟, 史常凯, 范闻博, 等. 配电线路接地故障波形特征分析与研究[J]. 电力设备管理, 2019(4): 31–35
FU Jinwei, SHI Changkai, FAN Wenbo, et al. Analysis and research on waveform characteristics of grounding fault of distribution line[J]. Electric Power Equipment Management, 2019(4): 31–35

基于综合特征矩阵的配电网故障判别方法

Distribution Network Fault Type Identification Method Based on Feature-summarizing Matrix

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	贺春光, 王林峰, 曹媛, 安佳坤, 雷子健, 宋关羽, 冀浩然. 考虑综合收益的多电压等级配电网柔性互联装置协同规划方法[J]. 中国电力, 2025, 58(1): 78-84.
[2]	王安宁, 范荣奇, 张旸, 刘嘉超, 胡薇, 钟世民, 贾科. 基于多特征量判据的新型配电系统早期故障检测[J]. 中国电力, 2024, 57(9): 181-193.
[3]	祝士焱, 许寅, 和敬涵, 王颖. 基于多微电网投影的配电系统协调恢复方法[J]. 中国电力, 2024, 57(9): 224-230.
[4]	张亚健, 陈茨, 薛飞, 马丽, 郑敏. 电制氢协同的含高比例光伏配电网两阶段电压随机优化控制[J]. 中国电力, 2024, 57(8): 23-35.
[5]	孙东磊, 孙毅, 刘蕊, 孙鹏凯, 张玉敏. 计及多层级配电网的分布式新能源最大消纳空间分解测算[J]. 中国电力, 2024, 57(8): 108-116.
[6]	王阳, 马伟东, 刘洎溟, 王博石, 姚凯, 韩伟, 余娟. 基于关联规则与重构误差的二次系统故障检测方法[J]. 中国电力, 2024, 57(8): 159-167.
[7]	凡鹏飞, 李宝琴, 侯江伟, 李嵘, 宋崇明, 林凯骏. 配电网分布式电源经济可承载力评估[J]. 中国电力, 2024, 57(7): 196-202.
[8]	王家武, 赵佃云, 刘长锋, 陈康, 张玉敏. 基于目标级联法的多主体主动配电网自治协同优化[J]. 中国电力, 2024, 57(7): 214-226.
[9]	韩璟琳, 胡平, 侯若松, 陈志永, 李洪涛, 柴园园. 计及多光储一体机的配电网电压优化控制策略[J]. 中国电力, 2024, 57(6): 69-77, 152.
[10]	徐峰亮, 王克谦, 王文豪, 王鹏, 王文烨, 张帅, 赵凤展. 计及激励型需求响应的低压配电网混合储能优化配置[J]. 中国电力, 2024, 57(6): 90-101.
[11]	乔俊峰, 周爱华, 彭林, 王一清, 沈晓峰, 潘森, 杨佩, 黄晨宏. 基于多源数据深度融合的配电网运行评价方法[J]. 中国电力, 2024, 57(6): 193-203.
[12]	张宇, 魏新迟, 冯凯辉, 时珊珊, 孟子涵, 梁媛. 计及分布式光伏承载力的配电网侧独立储能充放电策略[J]. 中国电力, 2024, 57(4): 111-117.
[13]	魏明江, 李鹏, 于浩, 冀浩然, 宋关羽, 习伟. 数字配电网边缘计算模拟实验平台[J]. 中国电力, 2024, 57(3): 12-19.
[14]	焦昊, 殷岩岩, 吴晨, 刘建, 徐春雷, 徐贤, 孙国强. 基于安全强化学习的主动配电网有功-无功协调优化调度[J]. 中国电力, 2024, 57(3): 43-50.
[15]	刘国强, 李国春, 郝亚楠, 李贵海, 吴中杰. 低压直流配电网强弱电弧特征及检测算法[J]. 中国电力, 2024, 57(2): 94-102.

模态框（Modal）标题

基于综合特征矩阵的配电网故障判别方法

Distribution Network Fault Type Identification Method Based on Feature-summarizing Matrix

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics