An Improved AP Clustering Algorithm Based Critical Nodes Identification for Distribution Network With High PV Penetration

Abstract

The power flow of the distributed network (DN) with high penetration of distributed PV changes significantly, such as bidirectional power flow and larger deviation of volage magnitude, under the variation of PV power output, and the number and the position of critical nodes would change accordingly. Hence, a data-driven nodes clustering and critical node identification method is proposed in this paper. Since the relationship between different nodes are nonlinear, the autoencoder method is firstly applied to obtain the unified features of the nodes connected with different number of branches. Using the unified features, the Frechet distance is calculated to demonstrate the similarity between any two nodes. The improved affinity propagation (AP) clustering algorithm is also proposed to classify the nodes into different clusters, and the number the clusters and the critical nodes in different clusters could be obtained automatically. The electrical distance is considered in the improved AP clustering algorithm, so that nodes in different branches with similar features are avoided to be classified into one cluster. The simulations are carried out on the IEEE 123-bus system and an actual DN system, the effectiveness and efficiency of proposed critical node identification method are verified.