Transformer Fault Diagnosis Based on Multi-Class AdaBoost Algorithm

Abstract

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 real-time 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 show that the diagnosis method proposed in this paper has strong search ability and fast convergence speed and has a significant improvement in diagnostic accuracy compared with traditional methods.

Keywords

• Power transformers
• fault diagnosis
• support vector machine
• improved particle swarm optimization
• DGA feature
• multi-class AdaBoost algorithm