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

Yanhui Xu; Yihao Gao; Yundan Cheng; Yuhang Sun; Xuesong Li; Xianxian Pan; Hao Yu

doi:10.1016/j.gloei.2023.08.010

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

更新时间：2026-10-05

- Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection
- Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection
- 全球能源互联网(英文) 2023年6卷第4期页码：505-516
- 作者机构：
  
  1. School of Electrical and Electronic Engineering, North China Electric Power University,Beijing,PR China,102206
  2. Grid Planning & Research Center, Guangdong Power Grid Co., Ltd.,Guangzhou,PR China,510030
- 作者简介：
- 基金信息：
- DOI：10.1016/j.gloei.2023.08.010
  中图分类号：
- 网络首发：2023-08-31，
  
  纸质出版：2023-08-31
- 稿件说明：
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Yanhui Xu, Yihao Gao, Yundan Cheng, 等. Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection[J]. 全球能源互联网(英文), 2023,6(4):505-516.

Yanhui Xu, Yihao Gao, Yundan Cheng, et al. Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection[J]. Global Energy Interconnection, 2023, 6(4): 505-516.
Yanhui Xu, Yihao Gao, Yundan Cheng, 等. Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection[J]. 全球能源互联网(英文), 2023,6(4):505-516. DOI： 10.1016/j.gloei.2023.08.010.

Yanhui Xu, Yihao Gao, Yundan Cheng, et al. Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection[J]. Global Energy Interconnection, 2023, 6(4): 505-516. DOI： 10.1016/j.gloei.2023.08.010.

摘要

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

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