| 引用本文: | 李博通,孙铭阳,张 婧,等.基于深度神经网络融合欧氏距离的多环配电网拓扑辨识方法[J].电力系统保护与控制,2025,53(05):123-134.[点击复制] |
| LI Botong,SUN Mingyang,ZHANG Jing,et al.Topology identification method for multi-ring distribution networks based on deepneural networks and Euclidean distance[J].Power System Protection and Control,2025,53(05):123-134[点击复制] |
|
| 摘要: |
| 针对多环配电网的拓扑辨识问题，考虑到量测信息可能部分缺失的情况，提出了基于深度神经网络融合欧氏距离的多环配电网拓扑辨识方法。首先，分析了传统拓扑辨识中相关性判断法应用于环状配电网的局限性，在此基础上提出基于欧氏距离的拓扑辨识判据。然后，针对量测信息缺失时的多环拓扑辨识问题，研究了利用深度神经网络融合欧氏距离判据的拓扑辨识方法。最后，在Matlab中利用MatPower搭建32节点“蜂巢”电网模型，在缺失不同比例的量测数据情况下验证方法的准确性。结果表明，当缺失大量量测数据时，所提方法仍有较高的拓扑辨识准确率。 |
| 关键词: 欧氏距离 多环配电网 深度神经网络 拓扑辨识 量测信息缺失 |
| DOI：10.19783/j.cnki.pspc.240746 |
| 投稿时间：2024-06-16修订日期：2024-08-11 |
| 基金项目:国家电网有限公司总部科技项目资助(5400- 202412189A-1-1-ZN)“低压配电网环网供电模式与运行策略研究” |
|
| Topology identification method for multi-ring distribution networks based on deepneural networks and Euclidean distance |
| LI Botong1,SUN Mingyang2,ZHANG Jing1,CHEN Fahui1,CHEN Xiaolong1,WANG Yongqi3,WU Jiaowen3,WEI Ran3 |
| (1. Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China; 2. North China Branch of
State Grid Corporation of China, Beijing 100053, China; 3. State Grid Tianjin Electric Power Company, Tianjin 300010, China) |
| Abstract: |
| In response to the problem of topological identification for multi-ring power distribution networks and considering the possibility of partial loss of measurement information, a method for topological identification of multi-ring power distribution networks based on deep neural networks and Euclidean distance is proposed. First, the limitations of the traditional topological identification method using correlation judgment in ring-shaped power distribution networks are analyzed. Based on this, a topological identification criterion based on Euclidean distance is proposed. Then, to address the issue of topological identification of multi-ring networks with missing measurement information, a method combing deep neural networks with the Euclidean distance criteria is proposed. Finally, a 32-node “honeycomb” power grid model is built in Matlab using MatPower, and the accuracy of the method is verified under different levels of missing measurement data. The results show that even with a large amount of missing measurement data, the proposed method still maintains a high accuracy for topological identification. |
| Key words: Euclidean distance multi-ring power distribution network deep neural network topology identification measurement information loss |
