Optimum Design of a High-Temperature Superconducting Induction/Synchronous Motor to Increase Torque Density Using Collective Decision Optimization Algorithm

Today, with the rapid advances in technology and the expansion of industries, electric motors are used extensively and consume a large part of the electrical energy produced by power plants. Therefore, researchers and experts have always been seeking solutions to make electric motors with high reliability and low losses. The machines are made of a high- temperature superconducting motor with high efficiency, came to be called high-temperature superconductor/induction synchronous motor. This paper studies the high-temperature superconducting induction/synchronous motor (HTS-ISM). The torque density and structural dimensions of HTS-ISM are optimized using one of the newest optimization methods, the collective decision optimization algorithm (CDOA). The results show a torque of about 51.75% increase via the optimization process. Also, the particle swarm optimization algorithm (PSO) as a commonly used optimization method is employed to compare the results. The comparison proves the high capability of the CDOA method to optimize the motor design parameters. All algorithms in this paper are run with the MATLAB software package.