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

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  • Zhongqiang Zhou
  • Jianwei Ma
  • Yusong Huang
  • Ling Liang
  • Zhiqi Chen

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.

Suggested Citation

  • Zhongqiang Zhou & Jianwei Ma & Yusong Huang & Ling Liang & Zhiqi Chen, 2025. "Utilisation of fuzzy logic control in self-healing of power systems: improved fuzzy C-means clustering method," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 47(6), pages 601-622.
    • Handle: RePEc:ids:ijgeni:v:47:y:2025:i:6:p:601-622
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