FEM-based transformer design optimization technique with evolutionary algorithms and geometric programming

Abstract

Cost optimization of power transformers is a complex engineering task. The design engineers have to solve this multidisciplinary, non-linear optimization problem in a very short-time. Due to the importance of the topic, numerous optimization methods have been developed in the industry to solve this problem. These methods generally use different, simplified two-winding transformer models, where the windings are generally modeled by their copper filling factors. These models cannot consider the eddy current losses and the temperature gradients of the windings properly, because the calculation of these quantities needs knowledge of the conductor dimensions. This paper proposes a novel method, which uses a general geometric programming search based sub-problem to determine the optimal conductor dimensions for the optimal winding shape. The proposed method considers the temperature gradients of the windings and uses FEM to determine the eddy losses of the windings. The whole optimization process is made by an evolutionary algorithm (NSGA-II)-based search. The paper presents this novel transformer optimization methodology and then illustrates it with a practical example.