考虑罕见因素熵值差异的配电网故障风险评估方法

doi:10.16183/j.cnki.jsjtu.2023.145

上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (12): 1857-1867.doi: 10.16183/j.cnki.jsjtu.2023.145

• 新型电力系统与综合能源 • 上一篇下一篇

考虑罕见因素熵值差异的配电网故障风险评估方法

黄俊贤¹, 陈春¹(), 曹一家¹, 全少理², 王益³

1.长沙理工大学电气与信息工程学院,长沙 410000
2.国网河南省电力公司经济技术研究院,郑州 450052
3.国网湖北电力有限公司,湖北咸宁 437000

收稿日期:2023-04-20 修回日期:2023-06-16 接受日期:2023-06-17 出版日期:2024-12-28 发布日期:2025-01-06
通讯作者: 陈春,副教授;E-mail:chch3266@126.com.
作者简介:黄俊贤(1998—),硕士生,从事配电网数据的分析与挖掘研究.
基金资助:
湖南省自然科学基金优秀青年项目(2023JJ20039);国家自然科学基金(52007009)

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

HUANG Junxian¹, CHEN Chun¹(), CAO Yijia¹, QUAN Shaoli², WANG Yi³

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:2023-04-20 Revised:2023-06-16 Accepted:2023-06-17 Online:2024-12-28 Published:2025-01-06

摘要/Abstract

摘要：

恶劣天气、外力破环等外部环境因素严重影响配电网供电可靠性,为了能够全面、准确地评估配电网故障风险,提出考虑罕见因素熵值差异的配电网故障风险评估方法.首先,以故障造成的后果为依据,采用K-均值聚类算法,划分故障风险等级;提出改进关联规则算法深入挖掘罕见环境因子,评估高风险低概率要素,实现对罕见因子与风险等级的关联量化分析;基于此量化结果,结合Pearson相关系数分析各环境特征的关联性,剔除不同风险等级下的冗余特征.然后,利用部件关键度分析法调节罕见元素的风险权重,定量衡量单个元素的出现与系统整体风险的波动相关程度,根据不同因子的波动差异,结合信息熵理论进一步优化,得到计及罕见因子熵值差异的风险权重优化矩阵,建立配电网故障风险评估模型.最后,对某城市实际配电网进行案例分析,验证了评估模型的准确性和有效性.

关键词: 配电网风险评估, 罕见风险因子, 风险熵值差异, 关联分析, 权重调整优化

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

中图分类号:

TM732

黄俊贤, 陈春, 曹一家, 全少理, 王益. 考虑罕见因素熵值差异的配电网故障风险评估方法[J]. 上海交通大学学报, 2024, 58(12): 1857-1867.

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 Jiao Tong University, 2024, 58(12): 1857-1867.

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故障/环境特征	所含元素
月份	1~12
日期	1~31
时刻	0:00—23:00
变电站电压等级/kV	10、35、110、220
影响后果	影响低压用户数、影响公变数、影响专变数
气温/℃	A₁≤12,13≤A₂≤21,22≤A₃≤28,A₄>28
气压/hPa	C₁≤993,994≤C₂≤1 000,1 001≤C₃≤1 009,C₄>1 009
降水量/(mm·h^-1)	E₁≤1.8,1.9≤E₂≤3.8,3.9≤E₃≤5.6,E₄>5.6
湿度/%	B₁≤0.6,0.7≤B₂≤0.8,0.8≤B₃≤0.9,B₄>0.9
地面风速/(m·s^-1)	D₁≤1.8,1.9≤D₂≤3,3.1≤D₃≤4.5,D₄>4.5
风向	F₁(东)、F₂(南)、F₃(西)、F₄(北)、F₅(东南)、F₆(东北)、F₇(西南)、F₈(西北)

风险等级	比例/%	影响用户数	影响变压器数
1	44.4	0~1565	0~76
2	34.8	1641~3659	10~105
3	20.8	3800~8410	17~143

风险等级	ARM			CCA			CCA-IET
风险等级	AUROC	SE	CI	AUROC	SE	CI	AUROC	SE	CI
等级1	0.802	0.381	0.598~0.782	0.853	0.288	0.655~0.824	0.899	0.241	0.742~0.896
等级2	0.754	0.287	0.587~0.758	0.823	0.245	0.643~0.789	0.866	0.182	0.751~0.878
等级3	0.690	0.242	0.576~0.787	0.787	0.220	0.582~0.784	0.828	0.179	0.720~0.907

考虑罕见因素熵值差异的配电网故障风险评估方法

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

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