Low-voltage distribution grid topology identification considering mutual load dependent characteristics

李嘉睿; 高 源

doi:10.3969/j.issn.1009-1831.2025.05.006

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Low-voltage distribution grid topology identification considering mutual load dependent characteristics

更新时间：2026-03-05

- Low-voltage distribution grid topology identification considering mutual load dependent characteristics
- Vol. 27, Issue 5, (2025)
- 作者机构：
  
  上海电力大学电气工程学院,上海,200090
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2025.05.006
  CLC：
- Published：2025
- 稿件说明：
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李嘉睿, 高源. Low-voltage distribution grid topology identification considering mutual load dependent characteristics[J]. 2025, 27(5).
DOI：

李嘉睿, 高源. Low-voltage distribution grid topology identification considering mutual load dependent characteristics[J]. 2025, 27(5). DOI： 10.3969/j.issn.1009-1831.2025.05.006.

摘要

低压配电网的拓扑结构描述了配电系统中各种电气元件的物理互连方式。由于分布式能源的使用，终端用户间存在一种被忽略的互负荷相关特性，这种特性给节点相关性分析和拓扑识别带来了极大挑战。为此，提出一种考虑分布式能源互负荷相关特性的低压配电网拓扑识别方法。首先，提出一种基于支持向量机的动态用户分类方法，根据用户在不同时间对分布式能源的使用情况进行分类。然后，利用卷积循环神经网络对负荷数据进行分布式特征提取；最后，提出了一种基于残差优化的同胞对搜索算法，实现拓扑结构的分层识别。多个仿真场景和实际低压配电网中的测试结果表明，该方法具有较高的识别精度和鲁棒性。

Abstract

The topology of low-voltage distribution grids（LVDGs）depicts how various electrical components are physically interconnected within the distribution system. Due to the use of distributed energy resources（DERs）

there is an overlooked mutual load dependent characteristics among end users

which brings great challenges to node correlation analysis and topology identification. In this regard

a low-voltage topology identification method focusing on the mutual load dependent characteristics of DERs is proposed. First

a user classification method based on support vector machineis proposed to classify users according to usage of DERs at different times. Then

convolutional recurrent neural network is applied for distributed feature extraction among load data to decrease load dependency. Finally

the sibling pair search algorithm with residuals resistance is proposed to hierarchically identify the topology. Test results in different simulation scenarios and practical LVDGs demonstrate the effectiveness and robustness of the proposed method.

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