Power transformers fault diagnosis based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm

• Published in: Journal of intelligent & fuzzy systems Vol. 44; no. 1; p. 455
• Main Authors: Song, Yu, Tan, Weimin, Zhang, Chengming, Tang, Chao, Cai, Lihong, Hu, Dong
• Format: Journal Article
• Language: English
• Published: London Sage Publications Ltd 01.01.2023
• Subjects: Algorithms; Artificial neural networks; Dissolved gases; Fault diagnosis; Gas analysis; Kernels; Machine learning; Optimization; Search algorithms; Training; Transformers
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-211862

Considering the power transformers fault diagnosis model has unstable performance and prone to over-fitting, we propose a transformers fault diagnosis model based on a meta-learning approach to kernel extreme learning machine with opposition-based learning sparrow search algorithm optimization (Meta-OSSA-KELM) in this paper. Its learning proceeds in two steps. Firstly, the base-learner KELMs is trained on the disjoint training subset. Then, meta-learner KELM is trained with the hidden codes of training set in base-learner KELMs that have been trained. In this paper, chaotic mapping and opposition-based learning are integrated into Sparrow search algorithm(SSA) and used it to optimize each learner. We simulate this model with measured dissolved gas analysis(DGA) data, the results show that compared with PSO and SSA, opposition-based learning sparrow search algorithm(OSSA) has better global search-ability on the optimization for the proposed model. In addition, compared with Adaboost.M1, BPNN, SVM and KELM, Meta-OSSA-KELM has a higher average accuracy (90.9% vs 78.5%, 74.0%, 76.9%, 76.9%) and a lower standard deviation (1.56×10–2 vs 4.21×10–2, 10.5×10–2, 3.7×10–2, 2.18×10–2) in simulation tests for 30 times. It is shown that the proposed model is a stable and better performance transformers fault diagnosis method.