基于有限关键节点及Wasserstein距离的配网拓扑识别

doi:10.11930/j.issn.1004-9649.202303122

中国电力 ›› 2024, Vol. 57 ›› Issue (4): 151-161.DOI: 10.11930/j.issn.1004-9649.202303122

基于有限关键节点及Wasserstein距离的配网拓扑识别

赵耀¹(), 陈永江¹(), 纪坤华²(), 王云²()

1. 上海电力大学电气工程学院，上海　200090
2. 国网上海市电力公司，上海　200122

收稿日期:2023-03-29 接受日期:2024-01-02 出版日期:2024-04-28 发布日期:2024-04-26
作者简介:赵耀（1987—），男，通信作者，博士，副教授，从事配电网态势感知、新能源发电、电力设备故障诊断研究，Email：nihaozhaoyao@163.com
陈永江（1998—），男，硕士研究生，从事配电网态势感知研究，E-mail：c15059571829@163.com
纪坤华（1980—），男，硕士，高级工程师，从事电力系统运行工作，E-mail：1093681984@qq.com
王云（1988—），男，博士，工程师，从事电力系统运行工作，E-mail：276768624@qq.com
基金资助:
国家自然科学基金资助项目（高渗透率电力系统有效惯量与一次调频相互作用机理及优化控制研究，51977128）；国家电网有限公司科技项目（基于融合终端的配网海量分布式可控资源协同优化研究，SGSH0000SCJS2100533）。

Distribution Network Topology Identification Based on Finite Key Nodes and Wasserstein Distance

Yao ZHAO¹(), Yongjiang CHEN¹(), Kunhua JI²(), Yun WANG²()

1. School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2. State Grid Shanghai Electric Power Company, Shanghai 200122, China

Received:2023-03-29 Accepted:2024-01-02 Online:2024-04-28 Published:2024-04-26
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Interaction Mechanism and Optimal Control of Effective Inertia and Primary Frequency Regulation in High Permeability Power System, No.51977128) and Science & Technology Project of SGCC (Research on Collaborative Optimization of Massive Distributed and Controllable Resources of Distribution Network Based on Converged Terminals, No.SGSH0000SCJS2100533).

摘要/Abstract

摘要：

明确配电网结构是配电网最优潮流、安全评估、网络重建、故障定位的基础。针对现有配电网拓扑识别方法缺乏结合现有网络结构参数和潮流信息，仅通过量测数据来进行拓扑识别效率低的问题，提出一种基于有限关键节点及Wasserstein距离的配电网拓扑识别方法。首先，利用子空间扰动模型证明配网拓扑变化时，可以利用有限的关键节点来进行拓扑识别，基于熵值法的混合K-Shell算法引入影响度概念，通过影响度与节点电气距离得出节点的重要度，确定配电网拓扑结构中的关键节点。其次，基于密度的噪声应用聚类算法通过电压、电流、有功、无功等4个特征来进行节点的聚类，将其他节点与关键节点进行类别归属，再结合Wasserstein距离得出节点间的连接关系从而得出配电网的拓扑结构。最后，通过IEEE 33节点算例和某小区实例，验证该方法的有效性。该方法极大地提高配电网拓扑识别效率与正确率，实现了配网拓扑结构的动态识别。

关键词: 配网拓扑识别, 子空间扰动模型, 节点重要度, 关键节点, Wasserstein距离

Abstract:

Defining the distribution network structure is the basis for optimal power flow, safety assessment, network reconstruction and fault location of the distribution network. Aiming at the problem that the existing distribution network topology identification methods are poor in efficiency due to their topology identification achieved only through measurement data without combination of existing network structure parameters and power flow information, a distribution network topology identification method based on finite key nodes and Wasserstein distance is proposed. Firstly, the finite key nodes can be used to identify the topology when the subspace perturbation model is used to prove the topology change of the distribution network, and the concept of influence degree is introduced through the entropy method based hybrid K-Shell algorithm and the importance of nodes is obtained by the influence degree and the electrical distance between the nodes, thus determining the key nodes in the distribution network topology. Secondly, the nodes are clustered with the density-based noise application clustering algorithm through four characteristics of voltage, current, active and reactive power, and other nodes and key nodes are classified into nodes and key nodes. And then, the connection relationship between nodes is obtained with the Wasserstein distance, consequently obtaining the topology of distribution network. Finally, a case study of the IEEE 33 node and a residential area has verified the effectiveness of the proposed method. This method greatly improves the identification efficiency and accuracy of distribution network topology, and realizes the dynamic identification of distribution network topology.

Key words: distribution network topology identification, subspace perturbation model, node importance, key nodes, Wasserstein distance

赵耀, 陈永江, 纪坤华, 王云. 基于有限关键节点及Wasserstein距离的配网拓扑识别[J]. 中国电力, 2024, 57(4): 151-161.

Yao ZHAO, Yongjiang CHEN, Kunhua JI, Yun WANG. Distribution Network Topology Identification Based on Finite Key Nodes and Wasserstein Distance[J]. Electric Power, 2024, 57(4): 151-161.

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图/表 10

参考文献 29

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拓扑结构	关键节点	识别结果
a)	1, 4, 5, 7, 9, 10, 13, 14, 15, 20, 21, 22, 25, 26, 30	1-2, 2-3, 3-4, 4-5, 4-7, 5-6, 7-8, 8-9, 9-10, 10-11, 11-12, 9-13, 13-14, 14-15, 14-20, 15-16, 16-17, 17-18, 18-19, 19-29, 20-21, 21-22, 21-25, 22-23, 23-24, 25-26, 25-30, 26-27, 27-28, 28-29, 30-31, 31-32, 32-33
b)	1, 4, 5, 6, 14, 15, 18, 20, 23, 24, 25	1-2, 2-3, 3-4, 4-5, 4-6, 6-7, 7-8, 8-9, 9-13, 13-14, 14-15, 14-18, 15-16, 16-17, 18-19, 19-23, 23-20, 23-24, 23-25, 20-21, 21-22, 24-26, 26-27, 27-28, 28-29, 25-30, 30-31, 31-32, 32-33

拓扑结构	关键节点	识别结果
a)	1, 4, 5, 7, 9, 10, 13, 14, 15, 20, 21, 22, 25, 26, 30	1-2, 2-3, 3-4, 4-5, 4-7, 5-6, 7-8, 8-9, 9-10, 10-11, 11-12, 9-13, 13-14, 14-15, 14-20, 15-16, 16-17, 17-18, 18-19, 19-29, 20-21, 21-22, 21-25, 22-23, 23-24, 25-26, 25-30, 26-27, 27-28, 28-29, 30-31, 31-32, 32-33
b)	1, 4, 5, 6, 14, 15, 18, 20, 23, 24, 25	1-2, 2-3, 3-4, 4-5, 4-6, 6-7, 7-8, 8-9, 9-13, 13-14, 14-15, 14-18, 15-16, 16-17, 18-19, 19-23, 23-20, 23-24, 23-25, 20-21, 21-22, 24-26, 26-27, 27-28, 28-29, 25-30, 30-31, 31-32, 32-33

时刻	U₁	I₁	P₁	U₂	I₂	P₂
00:00	229.8	0.933	0.196	226.9	1.691	0.360
01:00	229.5	0.849	0.180	226.9	1.424	0.303
02:00	229.6	1.016	0.215	226.4	1.805	0.375
03:00	228.5	1.046	0.220	225.2	1.655	0.340
···	···	···	···	···	···	···
21:00	226.8	1.626	0.336	221.6	2.355	0.478
22:00	226.7	2.796	0.582	218.0	5.951	1.206
23:00	227.6	1.845	0.378	221.6	2.384	0.484

时刻	U₁	I₁	P₁	U₂	I₂	P₂
00:00	229.8	0.933	0.196	226.9	1.691	0.360
01:00	229.5	0.849	0.180	226.9	1.424	0.303
02:00	229.6	1.016	0.215	226.4	1.805	0.375
03:00	228.5	1.046	0.220	225.2	1.655	0.340
···	···	···	···	···	···	···
21:00	226.8	1.626	0.336	221.6	2.355	0.478
22:00	226.7	2.796	0.582	218.0	5.951	1.206
23:00	227.6	1.845	0.378	221.6	2.384	0.484

用户	U间Wasserstein距离值			I间Wasserstein距离值			P间Wasserstein距离值
用户	表箱1	表箱2	表箱3	表箱1	表箱2	表箱3	表箱1	表箱2	表箱3
1	0.0033	0.2046	0.2013	0.0135	0.1493	0.1293	0.0101	0.1577	0.1571
2	0.0202	0.2100	0.2068	0.0129	0.1680	0.1347	0.0145	0.1593	0.1629
3	0.0324	0.2130	0.1734	0.0173	0.1669	0.1377	0.0262	0.1648	0.1648
4	0.0448	0.1766	0.1739	0.0106	0.1758	0.1382	0.0208	0.1677	0.1677
5	0.1085	0.0170	0.1859	0.1706	0.0203	0.1139	0.1287	0.0116	0.1682
6	0.1130	0.0292	0.1760	0.1722	0.0128	0.1251	0.1341	0.0108	0.1318
7	0.1097	0.0415	0.1754	0.1805	0.0075	0.1270	0.1370	0.0233	0.1439
8	0.1213	0.0721	0.1779	0.1811	0.0027	0.1329	0.1376	0.0205	0.1340
9	0.1605	0.1771	0.0246	0.1442	0.1370	0.0045	0.1358	0.1682	0.0102
10	0.1315	0.1892	0.0251	0.1447	0.1576	0.0012	0.1245	0.1318	0.0117
11	0.1330	0.1793	0.0128	0.1568	0.1556	0.0230	0.1264	0.1439	0.0126
12	0.2030	0.1787	0.0157	0.1468	0.1635	0.0246	0.1323	0.1551	0.0191

基于有限关键节点及Wasserstein距离的配网拓扑识别

Distribution Network Topology Identification Based on Finite Key Nodes and Wasserstein Distance

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R_SN/dB	拓扑识别准确率/%
R_SN/dB	皮尔逊相关系数法	T型灰色关联法	Wasserstein 距离值法
6	75.00	83.33	91.67
2	58.33	75.00	83.33
0	50.00	66.67	75.00
–2	41.67	58.33	66.67

模态框（Modal）标题

基于有限关键节点及Wasserstein距离的配网拓扑识别

Distribution Network Topology Identification Based on Finite Key Nodes and Wasserstein Distance

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