Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection  被引量:1

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作  者:Yanhui Xu Yihao Gao Yundan Cheng Yuhang Sun Xuesong Li Xianxian Pan Hao Yu 

机构地区:[1]School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,P.R.China [2]Grid Planning&Research Center,Guangdong Power Grid Co.,Ltd.,Guangzhou 510030,P.R.China

出  处:《Global Energy Interconnection》2023年第4期505-516,共12页全球能源互联网(英文版)

基  金:supported by the Planning Special Project of Guangdong Power Grid Co.,Ltd.:“Study on load modeling based on total measurement and discrimination method suitable for system characteristic analysis and calculation during the implementation of target grid in Guangdong power grid”(0319002022030203JF00023).

摘  要:The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.

关 键 词:Load substation clustering Simulated annealing genetic algorithm Kernel fuzzy C-means algorithm Clustering evaluation 

分 类 号:TM63[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]

 

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