Fault Diagnosis Method for Transformer Winding Based on Differentiated M-Training Classification Optimized by White Shark Optimization Algorithm

Guochao Qian (), Kun Yang, Jin Hu, Hongwen Liu, Shun He, Dexu Zou, Weiju Dai, Haozhou Wang and Dongyang Wang
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Guochao Qian: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Kun Yang: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Jin Hu: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Hongwen Liu: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Shun He: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Dexu Zou: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Weiju Dai: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Haozhou Wang: Electric Power Research Institute of Yunnan Power Grid, Kunming 650214, China
Dongyang Wang: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China

Energies, 2025, vol. 18, issue 9, 1-20

Abstract: Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate the accuracy limitations of single-classifier approaches in winding status assessment, this paper proposes a differentiated M-training classification algorithm based on White Shark Optimization (WSO). The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. For each base classifier, a parameter optimization method leveraging WSO is developed to enhance diagnostic precision. Second, an experimental platform for transformer fault simulation is constructed, capable of replicating various fault types with programmable severity levels. Through controlled experiments, frequency response curves and associated characteristic parameters are systematically acquired under diverse winding statuses. Finally, the model undergoes comprehensive training and validation using experimental datasets, and the model is verified and analyzed by the actual transformer test results. The experimental findings demonstrate that implementing WSO for base classifier optimization enhances the M-training algorithm’s diagnostic precision by 8.92% in fault-type identification and 8.17% in severity-level recognition. The proposed differentiated M-training architecture achieves classification accuracies of 98.33% for fault-type discrimination and 97.17% for severity quantification, representing statistically significant improvements over standalone classifiers.

Keywords: transformer; frequency response method; fault identification; white shark optimizer; M-training algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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