Title: Utilisation of fuzzy logic control in self-healing of power systems: improved fuzzy C-means clustering method

Authors: Zhongqiang Zhou; Jianwei Ma; Yusong Huang; Ling Liang; Zhiqi Chen

Addresses: Power Dispatching Control Centre of Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, China ' Power Dispatching Control Centre of Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, China ' Power Dispatching Control Centre of Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, China ' Power Dispatching Control Centre of Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, China ' Power Dispatching Control Centre of Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, China

Abstract: This paper used a fuzzy logic controller based on an enhanced fuzzy C-means clustering method to address the current issues with power system self-healing. The augmented fuzzy C-means algorithm based on learning automata (LAFCMA) was produced by analysing the conventional fuzzy C-means algorithm (FCMA) and integrating and referencing the research methodologies of other academics. A Fuzzy Logic Controller (FLC) was built using LAFCMA, which enhanced the controller's clustering impact by analysing and grouping power data. The accuracy of using LAFCMA-FLC for power system fault detection was above 96.73%, and the average accuracy of detecting 20 fault points was 97.95%. The fuzzy logic control based on the improved fuzzy C-means clustering method had broad application prospects and research value in the self-healing of power systems. By achieving self-healing of power systems, the frequency of manual intervention and maintenance can be reduced, thereby reducing operation and maintenance costs and improving economic benefits.

Keywords: fuzzy C-means algorithm; fuzzy C-means algorithm based on learning automata; self-healing of power system; fuzzy logic control; load forecasting.

DOI: 10.1504/IJGEI.2025.149588

International Journal of Global Energy Issues, 2025 Vol.47 No.6, pp.601 - 622

Received: 22 May 2024
Accepted: 26 Nov 2024
Published online: 07 Nov 2025 *

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