A Diversified Multiobjective Simulated Annealing and Genetic Algorithm for Optimizing a Three-Phase HTS Transformer

In this paper, a diversified multiobjective optimization of a transformer built from high-temperature superconducting (HTS) windings is presented. The main goal is an effective approach for an optimal HTS transformer design that involves the determination of selective transformer parameters when selected objectives are optimized. However, multiobjective optimization parameters are usually complex functions of the design variables and available only from an analysis of a finite-element model of the structure. As such, this requires the need for advanced numerical techniques for simulation and analysis of the HTS transformer by FLUX software. In addition, Python software is used along with two-dimensional FLUX for running the optimal design concepts based on simulated annealing and the genetic algorithm for the multiobjective optimization of the HTS transformer, which is the main motivation of this paper.