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Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

2023-11-23

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

基于自适应最佳聚类数目选择的改进KFCM变电站聚类算法

Yanhui Xu¹, Yihao Gao¹, Yundan Cheng¹,Yuhang Sun¹, Xuesong Li¹, Xianxian Pan², Hao Yu²

1.School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, P. R. China Scan for more details

2.Grid Planning & Research Center, Guangdong Power Grid Co., Ltd., Guangzhou 510030, P. R. China

Abstract

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.

Keywords

Load substation clustering; Simulated annealing genetic algorithm; Kernel fuzzy C-means algorithm; Clustering evaluation

Fig. 1 Flowchart of improved KFCM algorithm with adaptive optimal clustering number selection

Fig. 2 Scatter plot of load composition data

Fig. 3 Relationship between CH/DB and k

Fig. 4 Evolution curve of objective function values of KFCM algorithm

Fig. 5 Evolution curve of objective function values of SAGA-KFCM algorithm

Fig. 6 Scatter plot of FCM algorithm

Fig. 7 Scatter plot of KFCM algorithm

Fig. 8 Scatter plot of PSO-FCM algorithm

Fig. 9 Scatter plot of GA-KFCM algorithm

Fig. 10 Scatter plot of SAGA-KFCM algorithm

Fig. 11 Scatter plots with low clustering performance using KFCM algorithm

本文引文信息

Xu YH, Gao YH, Cheng YD, et al. (2023) Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection, Global Energy Interconnection, 6(4): 493-504

徐衍会，高镱滈，成蕴丹等 (2023) 基于数据挖掘的配电网规划问题关联性知识提取研究. 全球能源互联网（英文）, 6(4): 493-504

Biographies

Yanhui Xu

received his Ph.D. from North China Electric Power University. His research interests include dynamic power system analysis and load modeling.

Yihao Gao

is working toward her master’s degree at North China Electric Power University. Her research interests include power system load modeling.

Yundan Cheng

is working toward her master’s degree at North China Electric Power University. Her research interests include power system stability analysis.

Yuhang Sun

is working toward his master’s degree at North China Electric Power University. His research interests include power system load modeling.

Xuesong Li

is working toward his master’s degree at North China Electric Power University. His research interests include power system load modeling.

Xianxian Pan

received her master’s degree at North China Electric Power University in 2015. She is currently working in Guangdong Power Grid Co., Ltd. Her research interests include power grid planning and power system analysis and control.

Hao Yu

received his master’s degree at North China Electric Power University in 2012. He is currently working at Guangdong Power Grid Co., Ltd. His research interests include power grid planning, new energy power system modeling, and simulation.

编辑：王彦博

审核：王伟

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