A Topology Identification Approach for Active Distribution Networks Based on Grid Support Vector Machine

ZHOU Qian; AN Haiyun; CAO Yongji; JI Jingwen; YU Xiaorong; CEN Bingcheng; CHEN Zhe

doi:10.19725/j.cnki.1007-2322.2024.0032

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A Topology Identification Approach for Active Distribution Networks Based on Grid Support Vector Machine

更新时间：2026-04-02

- A Topology Identification Approach for Active Distribution Networks Based on Grid Support Vector Machine
- Vol. 43, Issue 2, Pages: 287-297(2026)
- 作者机构：
  
  1. 国网江苏省电力科学研究院,江苏省,南京市,211100
  2. 山东大学智能创新研究院,山东省,济南市,250101
  3. 山东大学电气工程学院,山东省,济南市,250061
  4. 国网江苏电动汽车服务有限公司,江苏省,南京市,210019
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0032
  CLC： 2
- Published：2026
- 稿件说明：
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ZHOU Qian, AN Haiyun, CAO Yongji, et al. A Topology Identification Approach for Active Distribution Networks Based on Grid Support Vector Machine[J]. 2026, 43(2): 287-297.
DOI：

ZHOU Qian, AN Haiyun, CAO Yongji, et al. A Topology Identification Approach for Active Distribution Networks Based on Grid Support Vector Machine[J]. 2026, 43(2): 287-297. DOI： 10.19725/j.cnki.1007-2322.2024.0032.

摘要

由于柔性负荷资源的大规模接入，配电网变得更加活跃和复杂。拓扑结构变化频繁，给配电网的运行、分析和控制带来了极大的挑战。针对现有的配电网全局拓扑辨识方法未考虑不同区域间差异性的问题，提出一种基于网格化支持向量机（support vector machine，SVM）的主动配电网分区拓扑辨识方法。首先，建立基于SVM的主动配电网全局拓扑辨识模型，对配电网实时量测数据进行初步拓扑辨识。其次，计算节点间的电气距离，对初步辨识得到的配网拓扑进行网格划分，建立基于网格化SVM的拓扑辨识模型，对配电网进行分区拓扑辨识，识别并修正初步拓扑辨识错误。最后，为了进一步提高拓扑辨识的精确度，采用循环迭代法，反复进行网格划分及分区拓扑辨识校验，得到最佳拓扑辨识结果。以IEEE 33节点配网系统为算例，验证所提方法的优越性。

Abstract

Owing to the large-scale integration of flexible load resources

the distribution network has become increasingly complex and active. The frequent variation in the topology of distribution networks poses great challenges to the operation

analysis and control of distribution networks. To address the limitation of existing global topology identification methods which have not adequately considered regional differences

a partition topology identification method is proposed based on grid SVM for active distribution networks. Firstly

an active distribution network global topology identification model is established based on SVM

and the real-time measurement data of distribution network are preliminarily identified. Subsequently

the electrical distance between nodes is calculated to mesh the distribution network topology obtained by the preliminary identification. A topology identification model based on grid SVM is then established to identify the partition topology of the distribution network

and detect and correct the initial topology identification errors. Finally

to further enhance the accuracy of topology identification

the cyclic iterative method is utilized to repeatedly performing meshing and partition topology identification validation

so as to obtain the best topology identification result. The superiority of the proposed method is verified through an instance of an IEEE 33-bus distribution network.

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references

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