Identification of pilot nodes for secondary voltage control using K-means clustering algorithm

G. Grigoraș, B. Neagu, Florina Scarlatache, R. Ciobanu
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引用次数: 9

Abstract

The voltage control represents one of the most important qualitative factors, and it is addressed to the secondary power system control level. The performance of secondary voltage control depends strongly on the selection of pilot nodes. This paper proposes the use of K-means clustering algorithm order to the identification of pilot nodes for Secondary Voltage Control (SVC) in the power grids. In the first phase, the algorithm is based on the zoning of power grid into coherent and completely connected clusters (control areas). In the second phase, a node will be selected as pilot node for each control area. The pilot node must satisfy a minimum criterion based on the sum of electrical distances between it and all the others nodes. The obtained results were compared with other classical methods using a test system. The conclusion was that the algorithm can be used successfully in the optimal zoning of power grids and the identification of pilot nodes for SVC.
基于k均值聚类算法的二次电压控制先导节点识别
电压控制是电力系统最重要的定性因素之一,是二级电力系统控制层面的问题。二次电压控制的性能在很大程度上取决于导频节点的选择。本文提出将k均值聚类算法用于电网二次电压控制(SVC)的导频节点识别。在第一阶段,该算法基于将电网划分为连贯且完全连接的集群(控制区域)。在第二阶段,将为每个控制区域选择一个节点作为试点节点。导导节点必须满足基于它与所有其他节点之间电距离总和的最小准则。并在试验系统中对所得结果进行了比较。结果表明,该算法可以成功地应用于SVC的最优电网分区和先导节点的识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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