基于欧氏形态距离与AP聚类分析的配电台区拓扑结构辨识方法

Identification Method for Topology in Distribution Station Area Based on Euclidean Morphological Distance and AP Clustering Analysis

针对低压配电台区户变关系辨识过程中存在的用户信息混乱、丢失和不准确等现象,提出基于欧氏形态距离和近邻传播聚类分析的配电台区拓扑结构辨识方法。首先,将电压曲线转化为离散序列,应用改进最长公共子序列方法给出形态距离;然后,利用熵权法配比欧氏距离与形态距离形成欧氏形态距离,度量曲线整体分布差异与形态变化差异;最后,利用近邻传播聚类算法实现用户台区辨识与台区内相别辨识。仿真算例验证了欧氏形态距离的有效性,进一步验证了该方法具有较高的聚类有效性和算法稳健性。

In view of the confusion,loss,and inaccuracy of user information in the process of identifying the relation⁃ship between households and transformers in low-voltage distribution station areas,a topology identification method for distribution station area based on Euclidean morphological distance and affinity propagation(AP)clustering analysis is proposed.First,the voltage curves are transformed into discrete sequences,and the morphological distance is given by using the improved longest common subsequence method.Then,the Euclidean distance and morphological distance are matched by the entropy weight method to form Euclidean morphological distance,which effectively measures the over⁃all distribution difference and morphological characteristic difference of curves.Finally,the AP clustering algorithm is used to realize the identification of the relationship between users and transformers and the phase identification within the station area.The result of a simulation example shows the effectiveness of Euclidean morphological distance,which further verifies that the proposed method has a high clustering effectiveness and a high robustness of algorithm.