为了解决源数据维数较大的问题,提出了一种基于非负矩阵分解(NMF)的同调机群识别方法。采用发电机角速度作为源数据,使用NMF算法对其进行降维。由于此低维矩阵具有非负性质,因而该模型在消除冗余数据、降低维数的同时,保留了原始问题的实际意义。对低维矩阵归一化,再利用K均值聚类算法对其进行聚类,达到同调机群的分群目的。通过New England 10机39节点系统比较了基于NMF和主成分分析方法的分群效果,验证了基于NMF的同调机群识别方法的有效性。
Abstract
To deal with the high-dimensional source data,a new method for identifying the coherent generators is proposed based on the non-negative matrix factorization(NMF)algorithm.Taking the angular speed of the generator as source data,its dimension can be reduced by using NMF algorithm.Because of the non-negative property of the obtained lower-dimensional matrix,it possesses some prior properties such as eliminating redundant data,reducing dimension,etc.,and could keep the practical significance of the original problem.The normalized lower-dimensional matrix is then clustered by K-means method to achieve grouping of coherent generators.Finally,simulation results are performed on the New England10-machine39-node system,some comparison results about grouping performance between NMF algorithm and principal component analysis(PCA)algorithm are obtained,which could show the effectiveness of the proposed method in the identification of coherent generators.