Reactive voltage control partitioning method based on AP clustering

Abstract

The invention discloses a reactive voltage control partitioning method based on AP clustering. The reactive voltage control partitioning method comprises the following steps that voltage reactive sensitivity is obtained based on a Jacobian matrix obtained through power flow calculation, and a PQ node electrical distance matrix is structured based on voltage sensitivity among PQ nodes; the minus of all elements of the PQ node electrical distance matrix is taken, and a similarity matrix is structured; the optimal clustering center, the optimal clustering number and a partitioning clustering result are obtained, and clustering of the PQ nodes is completed; the voltage regulating and control sensitivity of all PV nodes to all PQ partitions is calculated based on a perturbation method, and the PV nodes are partitioned into the PQ partition most sensitive in voltage regulating and control. The AP clustering algorithm is applied to voltage reactive partitions, the clustering number can be automatically obtained, unsupervised learning is carried out based on definitive evidence spreading, subjective factors in the clustering process are effectively reduced, the problem of random searching is effectively solved, and random factors are not included in the algorithm process.

Description

technical field [0001] The invention relates to an AP clustering-based reactive voltage control partition method. Background technique [0002] Grid node partitioning is the basis of three-level voltage control, so an effective partitioning method is the basic subject of voltage control. At present, the research results of partition methods are relatively rich, and the traditional voltage partition methods can be summarized into the following four categories: fuzzy clustering, graph theory, heuristic algorithms (such as simulated annealing method, tabu search, etc.), and learning methods (such as K-means). Existing methods can be better applied to traditional power grids, but rely on experience to manually specify the number of partitions, which will increase the subjective factors in the partitioning process; at the same time, when the clustering algorithm starts, its initial clustering center and search direction are random It is determined that the clustering result is e...

AI Technical Summary

Problems solved by technology

Existing methods can be better applied to traditional power grids, but rely on experience to manually specify the number of partitions, which will increase the subjective factors in the partitioning process; at the same time, when the clustering algorithm starts, its initial clustering center and search direction are random Determined, making the clustering result easy to fall into local optimum

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