基于电压序列最小KL散度索引的低压台区拓扑识别

Low-voltage Substation Area Topology Identification Based on Minimum KL Divergence Index of Voltage Series

为了实现对低压台区拓扑结构的准确刻画,提出一种基于电压序列最小KL(Kullback-Leibler)散度与深度搜索相结合的拓扑识别方法。首先,采用Neville插值修复电压采样序列,利用改进的KL散度计算用户电压序列概率分布,并依据KL散度大小对用户所属台区进行划分;其次,基于最小KL散度确定深度搜索的索引方向,通过主干搜索与分支搜索遍历台区用户节点,判断用户之间的连接关系;最后,针对不同场景分析所提方案拓扑识别性能。仿真结果验证了所提方案的有效性。

To realize an accurate description of the topology of a low-voltage substation area,a topology identification method based on the combination of minimum Kullback-Leibler(KL)divergence of voltage series and depth search is proposed.Firstly,the Neville interpolation is used to repair the voltage sampling series,and the improved KL divergence is used to calculate the probability distribution of user voltage series.In addition,the user’s substation area is divided according to the magnitude of KL divergence.Secondly,the index direction of depth search is determined based on the minimum KL divergence,and the connection relationship between users is judged by traversing the user nodes in the substation area through the main search and branch search.Finally,the topology identification performance of the proposed scheme is analyzed under different scenarios.Simulation results verify the effectiveness of the proposed scheme.