Hot-Spot Temperature Reduction in Oil-Immersed Transformers via Kriging-Based Structural Optimization of Winding Channels

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method.