About this Engineering article

Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm by Jifang Li; Genxu Li; Chen Hai; Mengbo Guo is a Engineering article available to read on EtoBox.

Traditional shallow machine learning algorithms cannot effectively explore the relationship between the fault data of oil-immersed transformers, resulting in low fault diagnosis accuracy. This paper proposes a transformer fault diagnosis method based on Multi-class AdaBoost Algorithms in response to this problem. First, the AdaBoost algorithm is combined with Support Vector Machines (SVM), The SVM is enhanced through the AdaBoost algorithm, and the transformer fault data is deeply explored. Then the dynamic weight is introduced into the Particle Swarm Optimization (PSO); through the realtime update of the particle inertia weight, the search accuracy and optimization speed of the particle swarm optimization algorithm is improved, and the improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM. Finally, by analyzing the relationship between the dissolved gas in the transformer oil and the fault type, the uncoded ratio method forms a new gas group cooperation. The improved ratio method is constructed as the input feature vector. Simulations based on 117 sets of IECTC10 standard data and 419 sets of transformer fault data collected in China s

Author

Jifang Li; Genxu Li; Chen Hai; Mengbo Guo

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Field

Engineering (Physical Sciences)