Reactive voltage control partitioning method based on AP clustering

A technology of voltage control partitioning and AP clustering, which is applied in the fields of electrical digital data processing, instruments, calculations, etc., and can solve the problems that the clustering results are easy to fall into local optimum and increase the subjective factors of partitioning, etc.

Active Publication Date: 2015-12-09
SHANDONG UNIV
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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

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  • Reactive voltage control partitioning method based on AP clustering
  • Reactive voltage control partitioning method based on AP clustering
  • Reactive voltage control partitioning method based on AP clustering

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Embodiment Construction

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0058] 1 PQ node partition based on AP clustering algorithm

[0059] Overview of AP clustering algorithm

[0060] At present, the algorithms need to artificially predict the number of clusters as the input of the algorithm, which will increase the subjective interference of the clustering process; in addition, some algorithms are based on randomly selected cluster centers or search directions when starting, making the clustering results prone to localization. best.

[0061] Based on the above considerations, the present invention selects a more advanced affine propagation clustering algorithm (AP clustering) on ​​the clustering algorithm. The AP clustering algorithm is a new unsupervised learning method proposed in recent years. The algorithm only takes the distance matrix between domain nodes as input (the distance matrix can be a symmetric matrix or...

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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...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 贠志皓周琼丰颖孙景文
Owner SHANDONG UNIV
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