【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。更多还原

【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 Mat Power,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.更多还原