Title: Fault diagnosis method for power grid transformers by integrating improved AFSA and radial basis function network

Authors: Man Xie; Limin Liu

Addresses: School of Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China ' Electronic Information Engineering College, Guangdong University of Petrochemical Technology, Maoming, 525000, China

Abstract: Power grid transformers are vital for stable power supply, and their failure can disrupt grid operations. Accurate fault diagnosis is essential to ensure reliability. This study proposes an improved artificial fish swarm algorithm (AFSA) for transformer fault diagnosis, integrating radial basis function networks (RBF) and kernel limit learning models to enhance accuracy. The method processes transformer data more effectively, reducing diagnosis errors by 0.014-0.029 compared to standalone RBF. Fusion models achieved 4.9%-5.9% higher accuracy than RBF alone. Notably, it excelled in gas concentration prediction, achieving zero deviation for C₂H₂. The results demonstrate superior performance over traditional methods, significantly improving fault diagnosis precision. This approach offers valuable guidance for maintaining transformer reliability in power grids.

Keywords: improved AFSA; radial basis function network; power grid transformer; fault diagnosis; FD; gas prediction; GP.

DOI: 10.1504/IJPELEC.2026.153509

International Journal of Power Electronics, 2026 Vol.22 No.3, pp.296 - 313

Received: 13 May 2025
Accepted: 30 Aug 2025
Published online: 12 May 2026 *

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