Optimum Design of Three-Phase Wound Rotor Induction Machine Using Giant Trevally Optimizer Technique

Conventional sizing methods for electric machines often result in large dimensions, excessive weight, even when following correct design principles. In this paper promising Giant Trevally Optimizer “GTO” is employed as a swarm intelligence-based optimization algorithm inspired by the social behavior of giant trevally, to design a three-phase wound rotor induction machine “3WRIM”. The optimization process targets four key objectives: maximizing efficiency , and minimizing weight per kilowatt “ ”, temperature rise , and the no-load current to phase current ratio . These objectives are optimized both simultaneously and individually while adhering to a set of practical design constraints. To validate the effectiveness of GTO, two widely recognized metaheuristic algorithms: Particle Swarm Optimizer (PSO) and Wild Horse Optimizer (WHO) are applied to the problem using identical objective functions under the same constraints. GTO proved faster convergence than PSO and WHO. The optimization process applied to 30 kW, 440 V, 4-pole wound rotor 3IM, and the results are compared to conventional design. The optimizers proved their capabilities to decrease the objectives , and by 9.14%, 3.42%, and 0.8% respectively while slight increases of than the conventional design. Also, the per phase equivalent circuit parameters are obtained based on GTO results, prove enhancement of motor performance. The findings demonstrate that the optimizer achieves superior performance, producing a highly competitive solution. Additionally, the GTO enables the optimal selection of machine parameters leading to an appropriate magnetic material choice. This research confirms the efficacy of GTO in electrical machine design.