Particle swarm optimization of high-frequency transformer

Abstract

A high frequency transformer is a critical component in a dual active bridge converter (DAB) used in a power electronics-based solid state transformer. Operation of a DAB converter requires its transformer to have a specific amount of winding leakage inductance. The demand for high efficiency requires minimization of transformer copper loss and core loss. Furthermore, available window area limits the winding arrangement of transformer. A hybrid of particle swarm optimization (PSO) and differential evolution (DE) is proposed to solve this multi-objective problem with constraints. DE provides diversity to pbest of each particle, which is missing in standard PSO. The differential evolution particle swarm optimization (DEPSO) algorithm is applied to find optimal transformer designs for DAB converters. Results show the DEPSO method is a generic effective way to find optimal high frequency transformer design.