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 is used to form a new gas group cooperation, and 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 not only has strong search ability and fast convergence speed but also has a significant improvement in diagnostic accuracy compared with traditional methods.

Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree

“…Although SVM has been widely used in transformer fault diagnosis, study has shown that a single SVM is inferior to some ensemble learning methods [23,12,[24][25][26]. Wang et al [23] proposed a transformer fault diagnosis method based on Bayesian optimization random forest, compared with a single intelligent diagnosis method, it has a higher accuracy rate.…”

“…Wang et al [23] proposed a transformer fault diagnosis method based on Bayesian optimization random forest, compared with a single intelligent diagnosis method, it has a higher accuracy rate. Another example is boosting ensemble learning algorithm, Liu et al [24] proposed a AdaBoost-RBF method, the accuracy of fault diagnosis can be effectively improved through boosting ensemble method. There is also PSO-ELM-Adaboost [25] and Adaboost-cloud [26] transformer ensemble learning fault diagnosis method.…”

“…Although SVM has been widely used in transformer fault diagnosis, study has shown that a single SVM is inferior to some ensemble learning methods [23, 12, 24–26]. Wang et al.…”

“…Another example is boosting ensemble learning algorithm, Liu et al. [24] proposed a AdaBoost‐RBF method, the accuracy of fault diagnosis can be effectively improved through boosting ensemble method. There is also PSO‐ELM‐Adaboost [25] and Adaboost‐cloud [26] transformer ensemble learning fault diagnosis method.…”

A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System

“…The performance of SVM mainly depends on the selection of kernel function and parameters; therefore, it motivates many scholars to use other intelligent algorithms to optimize the hyperparameters of SVM for the sake of achieving better classification results, such as particle swarm optimization [84], Krill Herd algorithm [92] and other methods [90]. Jifang et al [93] enhanced the SVM by applying the AdaBoost algorithm to deeply explore the transformer fault data. Moreover, improved particle swarm optimization algorithm (IPSO) is used to optimize the parameters of the SVM.…”