A Transformer Fault Diagnosis Method Based on Parameters Optimization of Hybrid Kernel Extreme Learning Machine

Abstract

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.

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