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

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

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
- Global Energy Interconnection Vol. 6, Issue 4, Pages: 505-516(2023)
- 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
  CLC：
- Online First：31 August 2023，
  
  Published：31 August 2023
- 稿件说明：
移动端阅览
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：

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

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Distribution network data asset protection considering multiple topology and multi-dimensional knowledge graph

Modeling and clustering of residential electricity consumption for short-term forecasting using seasonal autoregressive integrated moving average (SARIMA)

Forecasting the average daily and during the hours of maximum electric load based on the data of the average monthly electricity consumption with single and other energy sources

Two-stage wind-solar-storage capacity allocation method research for active distribution network based on whale migrating algorithm

EV＄{7*7}AL

Related Author

No data

Related Institution

No data

AI问答

⁰