Title: Multi-parameter comprehensive diagnosis technology for electrical faults based on artificial intelligence algorithms

Authors: Haiyan Yao; Lisong Dai; Qiang Guo; Pengcheng Shi; Xufeng Zhang; Feng Liu

Addresses: Hangzhou Power Equipment Manufacturing Co., Ltd., Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou, 311100, China ' State Grid Zhejiang Electric Power Co., Ltd., Hangzhou Linping District Power Supply Company, Hangzhou, 311100, China ' Hangzhou Power Equipment Manufacturing Co., Ltd., Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou, 311100, China ' Hangzhou Power Equipment Manufacturing Co., Ltd., Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou, 311100, China ' Hangzhou Power Equipment Manufacturing Co., Ltd., Yuhang Qunli Complete Electrical Manufacturing Branch, Hangzhou, 311100, China ' State Grid Zhejiang Electric Power Co., Ltd., Hangzhou Linping District Power Supply Company, Hangzhou, 311100, China

Abstract: This study addresses the poor diagnostic performance of conventional methods in multi-parameter electrical fault diagnosis by introducing a backpropagation neural network (BPNN) model enhanced through genetic algorithms and artificial firefly swarm optimisation. The approach leverages the genetic algorithm's search ability and the firefly algorithm's optimisation capability to improve fault diagnosis accuracy. Experimental results show that the proposed model achieves an average fitness of 95.36%, an average absolute error of 1.837, and an average accuracy of 96.54%, significantly outperforming two commonly used diagnostic models. Performance evaluation based on fitness, loss value, accuracy, and absolute error confirms the model's strong diagnostic capability. The proposed method offers a robust solution for accurate multi-parameter electrical fault diagnosis.

Keywords: backpropagation neural network; BPNN; artificial firefly swarm optimisation algorithm; GA; artificial intelligence; fault diagnosis technology.

DOI: 10.1504/IJPT.2025.149514

International Journal of Powertrains, 2025 Vol.14 No.3, pp.277 - 295

Received: 28 Apr 2025
Accepted: 03 Sep 2025
Published online: 04 Nov 2025 *

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