Shape Optimization of Coils and Cooling Ducts in Dry-Type Transformers Using Computational Fluid Dynamics and Genetic Algorithm

Abstract

In this paper, shapes of cooling ducts in dry-type transformers are optimized using computational fluid dynamics (CFD) and the Genetic Algorithm (GA). The GA is used to optimize diameters of both ducts and coils. Constraints in the optimization process are the minimum distance between the high-voltage (HV) and the low-voltage (LV) windings and the outer diameter of coils. Since the investigated transformer is a special unit, two objective functions (OF) were applied to minimize the average and the maximum temperature of the windings, and thus the coil power losses. The OF value is determined using a CFD model that accounts for all three heat transfer modes. The local total heat fluxes are specified on the model external boundaries. The thermal properties of the coils and core are treated as anisotropic and temperature-dependent quantities, while the power losses are treated as heat sources and are computed based on the coupled CFD-EMAG model. Both coil properties and losses vary with each generated coil configuration. The results show that the nonuniform positioning of the wires and air ducts can significantly improve the heat dissipation. Consequently, the coil losses are substantially reduced.