基于加权马氏距离型TOPSIS算法的10 kV配电网雷害风险评估

doi:10.11930/j.issn.1004-9649.202107085

中国电力 ›› 2022, Vol. 55 ›› Issue (4): 108-116.DOI: 10.11930/j.issn.1004-9649.202107085

基于加权马氏距离型TOPSIS算法的10 kV配电网雷害风险评估

杜岩¹, 谢从珍¹, 李彦丞¹, 谢心昊¹, 栾乐², 罗思敏²

1. 华南理工大学电力学院，广东广州 510641;
2. 广东电网有限责任公司广州供电局，广东广州 510013

收稿日期:2021-07-19 修回日期:2021-11-05 出版日期:2022-04-28 发布日期:2022-04-24
作者简介:杜岩（1997—），男，硕士研究生，从事大数据在配电网线路跳闸风险评估的应用等研究，E-mail：876190469@qq.com;谢从珍（1973—），女，通信作者，博士生导师，教授，从事新型功能电介质材料研发及测试，电力大数据及输配电线路防灾减灾技术研究，E-mail：xiecongzhen@scut.edu.cn
基金资助:
国家高技术研究发展计划(863 计划)资助项目(2015 AA050201); 广东电网广州供电局科技项目(GZHKJXM20180068)。

Lightning Risk Assessment of 10 kV Distribution Line Based on TOPSIS Algorithm Improved by Weighted Mahalanobis Distance

DU Yan¹, XIE Congzhen¹, LI Yancheng¹, XIE Xinhao¹, LUAN Le², LUO Simin²

1. School of Electric Power, South China University of Technology, Guangzhou 510641, China;
2. Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510013, China

Received:2021-07-19 Revised:2021-11-05 Online:2022-04-28 Published:2022-04-24
Supported by:
This work is supported by National High-tech Research and Development Program of China (863 Program) (No.2015 AA050201), Science and Technology Project of Guangdong Power Grid Co., Ltd., Guangzhou Power Supply Bureau (No.GZHKJXM20180068).

摘要/Abstract

摘要： 10 kV配电网架空线路绝缘水平较低，雷雨天气下雷击跳闸事故频发，由于大规模线路结构模型较为复杂等原因，基于雷击跳闸率等物理模型的风险评估方法在电网层面较难实现。同时，配电网缺少监测装置，人工记录的方式致使数据质量欠佳，严重影响模型计算的准确性。提出一种10 kV配电网雷害风险评估方法，基于线路运行、气象环境等多维数据，可在低质量数据条件下进行大规模配电网线路雷害风险评估。以线路所在地理网格为评估单元，从气象、线路走廊环境及线路本体特征参数3个维度提取10项配电网架空线路雷击跳闸风险指标，并量化风险指标以及雷击跳闸风险计算规则，使用加权马氏距离型TOPSIS算法构建10 kV配电网雷害风险评估体系，对风险进行分级。利用广州市10 kV配电网架空线路历史跳闸统计数据对风险评估结果进行验证，证明了评估方法的有效性。

关键词: 组合赋权, 加权马氏距离, TOPSIS, 风险评估, 雷击跳闸

Abstract: The insulation of overhead line in 10 kV distribution network is unsatisfied and lightning tripping accidents occur frequently in thunderstorm weather. Due to the complexity of large-scale line model construction, it is difficult to realize the risk assessment at the grid level based on physical model, such as lightning tripping rate. Besides, due to the lack of monitoring devices in the distribution network, the data quality is undesirable, which seriously affects the calculation accuracy of the model. In this paper, a lightning risk assessment method for 10 kV distribution network was proposed. Based on multi-dimensional data such as line operation, and meteorological environment information, the lightning assessment for large-scale distribution network lines was carried out under the condition of low quality data. Setting the geographical grid as evaluation unit, this paper extracted ten lightning-strike risk indexes from three dimensions, including meteorological, corridor environment, and characteristic parameters of line. Then TOPSIS algorithm improved by weighted Mahalanobis distance was used to build the lightning risk assessment system of 10 kV distribution network, and the risk was scaled. Finally, risk assessment results were verified by historical trip statistics in Guangzhou 10 kV distribution network, and the effectiveness of the assessment method was proved.

Key words: combination weight, weighted Mahalanobis distance, TOPSIS, risk assessment, lightning strike tripping

杜岩, 谢从珍, 李彦丞, 谢心昊, 栾乐, 罗思敏. 基于加权马氏距离型TOPSIS算法的10 kV配电网雷害风险评估[J]. 中国电力, 2022, 55(4): 108-116.

DU Yan, XIE Congzhen, LI Yancheng, XIE Xinhao, LUAN Le, LUO Simin. Lightning Risk Assessment of 10 kV Distribution Line Based on TOPSIS Algorithm Improved by Weighted Mahalanobis Distance[J]. Electric Power, 2022, 55(4): 108-116.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202107085

https://www.electricpower.com.cn/CN/Y2022/V55/I4/108

参考文献

[1] 黄伟坚. 雷电分布情况对10 kV架空配电线路防雷的影响分析[J]. 电瓷避雷器, 2013(3): 71–77
HUANG Weijian. Analysis of affects of lighting distribution on 10 kV overhead distribution lines[J]. Insulators and Surge Arresters, 2013(3): 71–77
[2] 刘育权, 郭媛君, 陆国俊, 等. 基于大数据的广州电网输电线路跳闸分析及相关因素关联性挖掘[J]. 南方电网技术, 2017, 11(4): 38–44
LIU Yuquan, GUO Yuanjun, LU Guojun, et al. Transmission line tripping analysis and correlative factor mining for Guangzhou power grid based on big data[J]. Southern Power System Technology, 2017, 11(4): 38–44
[3] 张义涛, 王泽忠, 刘丽平, 等. 基于灰色关联分析和改进神经网络的10 kV配电网线损预测[J]. 电网技术, 2019, 43(4): 1404–1410
ZHANG Yitao, WANG Zezhong, LIU Liping, et al. A 10 kV distribution network line loss prediction method based on grey correlation analysis and improved artificial neural network[J]. Power System Technology, 2019, 43(4): 1404–1410
[4] 谢桦, 陈俊星, 赵宇明, 等. 基于SMOTE和决策树算法的电力变压器状态评估知识获取方法[J]. 电力自动化设备, 2020, 40(2): 137–142,1
XIE Hua, CHEN Junxing, ZHAO Yuming, et al. Knowledge acquisition method of power transformer condition assessment based on SMOTE and decision tree algorithm[J]. Electric Power Automation Equipment, 2020, 40(2): 137–142,1
[5] 刘浩, 韩永霞, 陈长富, 等. 配电线路雷击跳闸率计算及差异化防雷方法研究[J]. 电瓷避雷器, 2020(4): 7–13,20
LIU Hao, HAN Yongxia, CHEN Changfu, et al. Research on lightning trip rate calculation and differentiated lightning protection of distribution line[J]. Insulators and Surge Arresters, 2020(4): 7–13,20
[6] 谭俊源, 唐军, 刘晓庭, 等. 珠江三角洲某地区雷电特征对10 kV配电网用避雷器故障的影响分析[J]. 高压电器, 2014, 50(4): 113–119
TAN Junyuan, TANG Jun, LIU Xiaoting, et al. Effects of lightning features on arrester faults in 10 kV distribution network in the Pearl River Delta[J]. High Voltage Apparatus, 2014, 50(4): 113–119
[7] 赵伟, 李哲, 史海锋, 等. 基于层次分析法的浙江电网雷击跳闸孕灾环境敏感性评估[J]. 高电压技术, 2017, 43(2): 619–626
ZHAO Wei, LI Zhe, SHI Haifeng, et al. Sensitivity assessment on hazard-pregnant environment of lightning trip-out in Zhejiang power grid based on analytic hierarchy process model[J]. High Voltage Engineering, 2017, 43(2): 619–626
[8] 赵淳, 陈家宏, 王剑, 等. 电网雷害风险评估技术研究[J]. 高电压技术, 2011, 37(12): 3012–3021
ZHAO Chun, CHEN Jiahong, WANG Jian, et al. Research on technology of lightning disaster risk assessment for power system[J]. High Voltage Engineering, 2011, 37(12): 3012–3021
[9] 谢从珍, 白剑锋, 王红斌, 等. 基于多维关联信息融合的架空输电线路雷害风险评估方法[J]. 中国电机工程学报, 2018, 38(21): 6233–6244,6485
XIE Congzhen, BAI Jianfeng, WANG Hongbin, et al. Lightning risk assessment of transmission lines based on multidimensional related information fusion[J]. Proceedings of the CSEE, 2018, 38(21): 6233–6244,6485
[10] 李彦斌, 于心怡, 王致杰. 采用灰色关联度与TOPSIS法的光伏发电项目风险评价研究[J]. 电网技术, 2013, 37(6): 1514–1519
LI Yanbin, YU Xinyi, WANG Zhijie. Risk assessment on photovoltaic power generation project by grey correlation analysis and TOPSIS method[J]. Power System Technology, 2013, 37(6): 1514–1519
[11] 王先甲, 汪磊. 基于马氏距离的改进型TOPSIS在供应商选择中的应用[J]. 控制与决策, 2012, 27(10): 1566–1570
WANG Xianjia, WANG Lei. Applications of TOPSIS improved based on Mahalanobis distance in supplier selection[J]. Control and Decision, 2012, 27(10): 1566–1570
[12] 梁海平, 田圣双, 李秋燕, 等. 基于改进TOPSIS灰色关联投影法的主网网架结构评价[J]. 电力自动化设备, 2019, 39(4): 63–69
LIANG Haiping, TIAN Shengshuang, LI Qiuyan, et al. Main grid structure evaluation based on improved TOPSIS grey relation projection method[J]. Electric Power Automation Equipment, 2019, 39(4): 63–69
[13] 关宏艳, 李宗坤, 葛巍, 等. 基于加权广义马氏距离的TOPSIS方法在水库防洪调度决策中的应用[J]. 天津大学学报(自然科学与工程技术版), 2016, 49(12): 1276–1281
GUAN Hongyan, LI Zongkun, GE Wei, et al. TOPSIS method based on weighted generalized Mahalanobis distance: an application to reservoir flood control operation[J]. Journal of Tianjin University (Science and Technology), 2016, 49(12): 1276–1281
[14] 张弦. 10 kV配电网线路雷击跳闸因素分析及其防雷性能评估[D]. 广州: 华南理工大学, 2015.
ZHANG Xian. The mechanism research on lightning tripping and assessment of lightning performance in 10 kV distribution lines[D]. Guangzhou: South China University of Technology, 2015.
[15] 何金良, 曾嵘. 配电线路雷电防护[M]. 北京: 清华大学出版社, 2013.
[16] 于建立, 樊亚东, 王建国, 等. 大地电导率和线路长度对10 kV架空线路雷电感应电压的影响[J]. 中国电机工程学报, 2020, 40(2): 672–684
YU Jianli, FAN Yadong, WANG Jianguo, et al. Effect of earth conductivity and length of line on lightning induced voltage of 10 kV overhead distribution lines[J]. Proceedings of the CSEE, 2020, 40(2): 672–684
[17] 徐志闻, 刘亚斌, 胡夏嵩, 等. 基于水分和原位电导率的西宁盆地盐渍土含盐量估算模型[J]. 农业工程学报, 2019, 35(5): 148–154
XU Zhiwen, LIU Yabin, HU Xiasong, et al. Salt content estimation model of saline soil in Xining Basin based on water content and in situ electrical conductivity[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(5): 148–154
[18] TANG Z Y, ZHOU W J, ZHAO J K, et al. Comparison of the weibull and the crow-AMSAA model in prediction of early cable joint failures[J]. IEEE Transactions on Power Delivery, 2015, 30(6): 2410–2418.
[19] 彭永晶, 陆佳政, 方针, 等. 10 kV配电线路杆塔接地电阻对耐雷水平影响研究[J]. 电瓷避雷器, 2019(4): 176–181
PENG Yongjing, LU Jiazheng, FANG Zhen, et al. Study on the effects of grounding resistance of the tower for 10 kV distribution lines on lightning withstand level[J]. Insulators and Surge Arresters, 2019(4): 176–181
[20] 吴易雯, 李莹杰, 张列宇, 等. 基于主客观赋权模糊综合评价法的湖泊水生态系统健康评价[J]. 湖泊科学, 2017, 29(5): 1091–1102
WU Yiwen, LI Yingjie, ZHANG Lieyu, et al. Assessment of lakes ecosystem health based on objective and subjective weighting combined with fuzzy comprehensive evaluation[J]. Journal of Lake Sciences, 2017, 29(5): 1091–1102
[21] 邓红雷, 李述文, 戴栋. 基于层次化、差异化的架空输电线路雷击风险评估[J]. 电力系统保护与控制, 2016, 44(4): 69–75
DENG Honglei, LI Shuwen, DAI Dong. Lightning risk assessment of overhead transmission line based on hierarchical and differentiation[J]. Power System Protection and Control, 2016, 44(4): 69–75
[22] 李文璟, 李梦, 邢宁哲, 等. 基于熵权-灰色模型的电力数据网风险预测[J]. 北京邮电大学学报, 2018, 41(3): 39–45
LI Wenjing, LI Meng, XING Ningzhe, et al. Risk prediction of power data network based on entropy weight-gray model[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(3): 39–45
[23] 徐长宝, 王玉磊, 赵立进, 等. 基于信息趋势预测和组合赋权的智能变电站继电保护系统状态模糊综合评价[J]. 电力自动化设备, 2018, 38(1): 162–168
XU Changbao, WANG Yulei, ZHAO Lijin, et al. Fuzzy comprehensive evaluation of intelligent substation relay protection system state based on information trend prediction and combination weighting[J]. Electric Power Automation Equipment, 2018, 38(1): 162–168
[24] 张厚, 刘长良, 王梓齐. 基于PCA和改进TOPSIS法的电厂混煤配比方案综合评价[J]. 热力发电, 2019, 48(11): 73–78
ZHANG Hou, LIU Changliang, WANG Ziqi. Comprehensive evaluation of coal-blending scheme in power plants based on PCA and improved TOPSIS method[J]. Thermal Power Generation, 2019, 48(11): 73–78
[25] 陈欢, 黄德才. 基于广义马氏距离的缺损数据补值算法[J]. 计算机科学, 2011, 38(5): 149–153
CHEN Huan, HUANG Decai. Missing data imputation based on generalized Mahalanobis distance[J]. Computer Science, 2011, 38(5): 149–153

基于加权马氏距离型TOPSIS算法的10 kV配电网雷害风险评估

Lightning Risk Assessment of 10 kV Distribution Line Based on TOPSIS Algorithm Improved by Weighted Mahalanobis Distance

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]	汪林光, 李旭涛, 任勇, 谢小荣. 基于元启发式算法的新能源电力系统振荡稳定性最差工况搜索方法[J]. 中国电力, 2025, 58(3): 65-72.
[2]	杨珂, 王栋, 李达, 张王俊, 向尕, 李军. 虚拟电厂网络安全风险评估指标体系构建及量化计算[J]. 中国电力, 2024, 57(8): 130-137.
[3]	赵会茹, 姚满宇, 李兵抗, 谢光龙, 丁智华, 胡臻达. 考虑乡村振兴贡献度的农网项目投资决策模型[J]. 中国电力, 2024, 57(6): 181-192.
[4]	许竞, 赵铁军, 高小刚, 叶鞠, 孙玲玲. 高比例新能源电力系统调节资源灵活性不足风险分析[J]. 中国电力, 2024, 57(11): 129-138.
[5]	钟成, 翟迪, 陆阳, 范静怡, 刘晓波, 张馨月, 赵雄文. 城市电网本地通信网络数字化感知业务与通信技术适配研究[J]. 中国电力, 2023, 56(8): 86-98.
[6]	罗超月岭, 李智威, 徐祯雨, 唐学军, 李黎, 方钊. 配电网设备资产运检成本影响因素评价分析[J]. 中国电力, 2023, 56(7): 216-227.
[7]	吴亚宁, 罗毅, 雷成, 黄豫, 梁宇, 周生存, 聂金峰. 基于改进型PEM和L指标的含风电场电力系统静态电压稳定评估[J]. 中国电力, 2022, 55(9): 192-203.
[8]	李存斌, 董佳. 中国风力发电绩效的区域差异及空间计量分析[J]. 中国电力, 2022, 55(3): 167-176.
[9]	赵洪山, 李静璇. 基于PSR和改进灰色TOPSIS的园区客户能效评估模型[J]. 中国电力, 2022, 55(3): 203-212.
[10]	沈培锋, 王徐延, 张昊亮, 卫志农, 徐广开, 孙国强. 考虑规模化快充负荷的低压互联配电台区风险评估[J]. 中国电力, 2021, 54(5): 56-64.
[11]	赵璞, 周满, 高建宇, 潘乐真, 刘自发, 谢浩铠. 基于电能替代的园区综合能源规划评价方法[J]. 中国电力, 2021, 54(4): 130-140.
[12]	李雪松, 蒋宇, 刘胥雯, 王阳, 丁羽. 基于改进Critic-G1算法的发电商市场力综合评价方法[J]. 中国电力, 2021, 54(11): 59-67.
[13]	张博, 唐钰政, 代双寅, 陈韵竹, 王盼盼, 肖先勇. 供用电双方满意的电压暂降治理增值服务策略[J]. 中国电力, 2020, 53(11): 50-59.
[14]	李晓, 李满礼, 倪明. 配电信息物理系统分析与控制研究综述[J]. 中国电力, 2020, 53(1): 11-21.
[15]	丁茂生, 孙维佳, 蔡星浦, 王琦. 电力系统极端事件的风险评估与防范[J]. 中国电力, 2020, 53(1): 32-39,65.

模态框（Modal）标题

基于加权马氏距离型TOPSIS算法的10 kV配电网雷害风险评估

Lightning Risk Assessment of 10 kV Distribution Line Based on TOPSIS Algorithm Improved by Weighted Mahalanobis Distance

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