Dissolved gas analysis (DGA) is a widely used method for diagnosing internal transformer defects. The traditional single intelligent diagnostic method cannot efficiently process large amounts of incomplete defect information with DGA, which affects the accuracy of fault diagnosis. To this end, this paper proposes a transformer fault diagnosis method based on the optimization of kernel parameters and weight parameters of a kernel extreme learning machine (KELM). First, based on Mercer's theorem, we combine the radial basis kernel function with the polynomial kernel function to construct a new hybrid kernel function. Then, the gray wolf optimization (GWO) algorithm and the differential evolution (DE) algorithm are combined to improve the diversity of the gray wolf population, enhance the searchability of GWO, and prevent GWO from falling into a local optimum during the iterative process. Finally, the kernel parameters and weight parameters of the hybrid kernel function are optimized by using the modified grey wolf optimization (MGWO) algorithm. The International Electrotechnical Commission Technical Committee (IEC TC) 10 transformer fault data and constructed hybrid feature set is used as the input set of the model, the model is simulated and analyzed, and the transformer fault data collected at a site are used for training and verification. The simulation results on the two sets of data show that the method can accurately and effectively diagnose transformer faults, and has a higher fault diagnosis accuracy rate than traditional methods.

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.

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