Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2024 , Vol. 58 >Issue 12: 1857 - 1867

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.145

New Type Power System and the Integrated Energy

Distribution Network Fault Risk Assessment Method Considering Difference in Entropy Value of Rare Factors

  • HUANG Junxian ,
  • CHEN Chun ,
  • CAO Yijia ,
  • QUAN Shaoli ,
  • WANG Yi
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  • 1. School of Electrical and Information Engineering, Changsha University of Science andTechnology, Changsha 410000, China
    2. Economics and Technology Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450052, China
    3. State Grid Hubei Electric Power Co., Ltd., Xianning 437000, Hubei, China

Received date: 2023-04-20

  Revised date: 2023-06-16

  Accepted date: 2023-06-17

  Online published: 2023-06-25

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Abstract

External factors such as bad weather and external failure seriously affect the reliability of a distribution network. To comprehensively and accurately assess the risk of faults in a distribution network, this paper proposes a fault risk assessment method that considers the difference in entropy value of rare factors. This method uses K-means clustering algorithm to classify failures based on their consequences and an improved association rule mining algorithm to analyze rare environmental factors and evaluate high-risk and low-probability factors, so as to realize the quantitative analysis of the association between rare factors and risk levels. By combining the Pearson correlation coefficient, the correlation of each environmental feature is analyzed and redundant features at different risk levels are eliminated. Then, the component criticality analysis method is used to adjust the risk weight of rare elements, which can quantitatively measure the degree of correlation between the occurrence of individual elements and the fluctuation of the overall risk of the system. According to the fluctuation difference of different factors, the risk weight optimization matrix considering the difference of rare factors is obtained, and the fault risk assessment model of the distribution network is established, in combination with the information theory. Finally, a distribution network fault risk assessment model is established, of which the accuracy and effectiveness is verified by a case analysis of an actual distribution network in a city.

Key words： distribution network risk assessment; rare risk factors; difference in risk entropy; correlation analysis; weight adjustment optimization

Cite this article

HUANG Junxian , CHEN Chun , CAO Yijia , QUAN Shaoli , WANG Yi . Distribution Network Fault Risk Assessment Method Considering Difference in Entropy Value of Rare Factors[J]. Journal of Shanghai Jiaotong University, 2024 , 58(12) : 1857 -1867 . DOI: 10.16183/j.cnki.jsjtu.2023.145

References

[1] 郑凌铭, 舒胜文, 陈彬, 等. 强台风环境下基于格点化和支持向量机的10 kV杆塔受损量预测方法[J]. 高电压技术, 2020, 46(1): 42-51.
  ZHENG Lingming, SHU Shengwen, CHEN Bin, et al. Prediction method for amount of 10 kV damaged poles under severe typhoon environment based on meshing and support vector machine[J]. High Voltage Engineering, 2020, 46(1): 42-51.
[2] 郑国鑫, 雷霞, 王湘, 等. 地震灾害模拟及配电网的风险评估[J]. 电工技术学报, 2020, 35(24): 5218-5226.
  ZHENG Guoxin, LEI Xia, WANG Xiang, et al. Earthquake simulation and risk assessment of distribution network[J]. Transactions of China Electrotechnical Society, 2020, 35(24): 5218-5226.
[3] 李吉侗, 王洲, 达紫祺, 等. 计及恶劣天气时空相关性的弹性配电网储能电站多层规划方法[J]. 电力系统保护与控制, 2023, 51(9): 128-137.
  LI Jitong, WANG Zhou, DA Ziqi, et al. Multi-level planning method of energy storage stations for resilient distribution networks considering spatio-temporal correlation of severe weather[J]. Power System Protection & Control, 2023, 51(9): 128-137.
[4] 李振坤, 王法顺, 郭维一, 等. 极端天气下智能配电网的弹性评估[J]. 电力系统自动化, 2020, 44(9): 60-68.
  LI Zhenkun, WANG Fashun, GUO Weiyi, et al. Resilience evaluation of smart distribution network in extreme weather[J]. Automation of Electric Power Systems, 2020, 44(9): 60-68.
[5] 周晓敏, 葛少云, 李腾, 等. 极端天气条件下的配电网韧性分析方法及提升措施研究[J]. 中国电机工程学报, 2018, 38(2): 505-513.
  ZHOU Xiaomin, GE Shaoyun, LI Teng, et al. Assessing and boosting resilience of distribution system under extreme weather[J]. Proceedings of the CSEE, 2018, 38(2): 505-513.
[6] 王守相, 黄仁山, 潘志新, 等. 极端冰雪天气下配电网弹性恢复力指标的构建及评估方法[J]. 高电压技术, 2020, 46(1): 123-132.
  WANG Shouxiang, HUANG Renshan, PAN Zhixin, et al. Construction and evaluation of resilience restoration capability indices for distribution network under extreme ice and snow weather[J]. High Voltage Engineering, 2020, 46(1): 123-132.
[7] 颜文婷, 杨隆, 李长城, 等. 考虑地震攻击交通网影响的配电网韧性评估及提升策略[J]. 上海交通大学学报, 2023, 57(9): 1165-1175.
  YAN Wenting, YANG Long, LI Changcheng, et al. Resilience evaluation and enhancement strategy of distribution network considering impact of seismic attack on transportation networks[J]. Journal of Shanghai Jiao Tong University, 2023, 57(9): 1165-1175.
[8] 杨丽君, 高鹏, 王伟浩, 等. 考虑时间尺度的配电网故障恢复方法研究[J]. 太阳能学报, 2021, 42(1): 453-459.
  YANG Lijun, GAO Peng, WANG Weihao, et al. Research on fault recovery method considering time scale for distribution network[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 453-459.
[9] 费思源. 大数据技术在配电网中的应用综述[J]. 中国电机工程学报, 2018, 38(1): 85-96.
  FEI Siyuan. Overview of application of big data technology in power distribution system[J]. Proceedings of the CSEE, 2018, 38(1): 85-96.
[10] 周毅, 秦康平, 孙近文, 等. 台风气象环境电网设备风险量化预警及其N-m故障处置预案在线生成方法[J]. 上海交通大学学报, 2021, 55(Sup.2): 22-30.
  ZHOU Yi, QIN Kangping, SUN Jinwen, et al. Real-time risk evaluation method of power system equipment and N-m fault contingency plan generation under typhoon meteorological environment[J]. Journal of Shanghai Jiao Tong University, 2021, 55(Sup.2): 22-30.
[11] 张春梅, 许兴雀, 刘思麟. 基于多源数据融合的配电网故障诊断技术[J]. 上海交通大学学报, 2024, 58(5): 739-746.
  ZHANG Chunmei, XU Xingque, LIU Silin. Distribution network fault diagnosis technology based on multi-source data fusion[J]. Journal of Shanghai Jiao Tong University, 2024, 58(5): 739-746.
[12] 侯慧, 朱韶华, 俞菊芳, 等. 基于高效数据降维的配电网风灾停电用户数量预测模型[J]. 电力系统自动化, 2022, 46(7): 69-76.
  HOU Hui, ZHU Shaohua, YU Jufang, et al. Prediction model for user number in power outage caused by wind disaster for distribution networks based on high-efficient data dimensionality reduction[J]. Automation of Electric Power Systems, 2022, 46(7): 69-76.
[13] 马丽叶, 王海锋, 卢志刚, 等. 计及相关性影响的增强台风灾害下配电网韧性灵活性资源规划[J]. 电力系统自动化, 2022, 46(7): 60-68.
  MA Liye, WANG Haifeng, LU Zhigang, et al. Flexible resource planning for improving distribution network resilience under typhoon disasters considering relevance impact[J]. Automation of Electric Power Systems, 2022, 46(7): 60-68.
[14] 张稳, 盛万兴, 刘科研, 等. 计及天气因素相关性的配电网故障风险等级预测方法[J]. 电网技术, 2018, 42(8): 2391-2398.
  ZHANG Wen, SHENG Wanxing, LIU Keyan, et al. A prediction method of fault risk level for distribution network considering correlation of weather factors[J]. Power System Technology, 2018, 42(8): 2391-2398.
[15] 刘科研, 吴心忠, 石琛, 等. 基于数据挖掘的配电网故障风险预警[J]. 电力自动化设备, 2018, 38(5): 148-153.
  LIU Keyan, WU Xinzhong, SHI Chen, et al. Fault risk early warning of distribution network based on data mining[J]. Electric Power Automation Equipment, 2018, 38(5): 148-153.
[16] 徐特威, 鲁宗相, 乔颖, 等. 基于典型故障与环境场景关联识别的城市配电网运行风险预警方法[J]. 电网技术, 2017, 41(8): 2577-2584.
  XU Tewei, LU Zongxiang, QIAO Ying, et al. A risk warning method for urban distribution network based on associated recognition of typical fault and environment scenario[J]. Power System Technology, 2017, 41(8): 2577-2584.
[17] 刘鑫蕊, 李欣, 孙秋野, 等. 考虑冰灾环境的配电网态势感知和薄弱环节辨识方法[J]. 电网技术, 2019, 43(7): 2243-2252.
  LIU Xinrui, LI Xin, SUN Qiuye, et al. A new method for situation awareness and weakness identification of distribution network considering ice disaster[J]. Power System Technology, 2019, 43(7): 2243-2252.
[18] DU Y, LIU Y D, WANG X H, et al. Predicting weather-related failure risk in distribution systems using Bayesian neural network[J]. IEEE Transactions on Smart Grid, 2021, 12(1): 350-360.
[19] 王朝盛, 邵峰, 谭锐, 等. 全厂AGC调度模式负荷经济分配方法研究[J]. 湖南电力, 2023, 43(1): 90-94.
  WANG Chaosheng, SHAO Feng, TAN Rui, et al. Study on load economic distribution method of AGC scheduling mode in plant[J]. Hunan Electric Power, 2023, 43(1): 90-94.
[20] 梁永亮, 郭汉琮, 薛永端. 基于特征气体关联特征的变压器故障诊断方法[J]. 高电压技术, 2019, 45(2): 386-392.
  LIANG Yongliang, GUO Hancong, XUE Yong-duan. Transformer fault diagnosis method based on association characteristics of characteristic gases[J]. High Voltage Engineering, 2019, 45(2): 386-392.
[21] 汪颖, 王曼, 陈韵竹, 等. 基于多维关联信息的电压暂降治理需求识别[J]. 电网技术, 2022, 46(11): 4391-4402.
  WANG Ying, WANG Man, CHEN Yunzhu, et al. Identify the mitigation demand against voltage sag based on multidimensional related information[J]. Power System Technology, 2022, 46(11): 4391-4402.
[22] AVEN T, N?KLAND T E. On the use of uncertainty importance measures in reliability and risk analysis[J]. Reliability Engineering & System Safety, 2010, 95(2): 127-133.
[23] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874.
[24] SWETS J A. Signal detection theory and ROC analysis in psychology and diagnostics: Collected papers[M]. New York, USA: Psychology Press, 1996.
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