Coherency approach by hybrid PSO, K-Means clustering method in power system
Coherency approach by hybrid PSO, K-Means clustering method in power system
复制标题
电力系统中混合PSO、K-Means聚类方法的一致性方法
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
A. Sarikhani
中科院分区：
文献类型：
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作者：
M. Davodi;H. Modares;E. Reihani;A. Sarikhani
针对电力系统特定故障位置，提出了一种同步发电机相同行为识别的新方法。该方法将粒子群优化（PSO）算法与k-means算法相结合，提出了一种寻找电网中指定数量簇的混合算法，也称为PSO- km算法。每个集群代表若干个生成器，这些生成器被称为相干生成器。在暂态稳定研究中，聚类过程是基于时域数据的相似性。在39路公交车新英格兰测试系统上对新算法进行了测试。结果表明，该算法在寻找相干发生器方面具有很大的潜力。
This paper presents a new method for recognition the identical behaviors of synchronous generators for particular fault location on power system. In this method, a hybrid algorithm combining particle swarm optimization (PSO) algorithm with k-means algorithm, also referred to PSO-KM algorithm is proposed to find the specified number of clusters in electric network. Each cluster represents a number of generators such that these generators named coherent generators. Clustering process is based on similarity of time domain data in transient stability studies. The new algorithm is evaluated on 39-Bus New England test system. Results show that the proposed algorithm has much potential in finding coherent generators.