Performance Comparison of Equilibrium Optimization Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm to the Design Optimization of Wind Turbine PMSG

This research optimises the design of a permanent magnet synchronous generator to meet the output power needs of a small direct-drive wind turbine. Extra care has been taken to reduce the generator's total volume to reduce expenses. The proposed method aims to reduce the cost of PMSG by reducing its volume. In this study, the optimal values of PMSG parameters for minimising the overall volume of the PMSG generator while maintaining its output power at the rated value are determined. To estimate the optimal values of design parameters, three algorithms have been considered. Equilibrium Optimization Algorithm (EOA) as the proposed algorithm, Gravitational Search Algorithm (GSA) as the first existing algorithm, and Particle Swarm Optimization as the second existing algorithm. Comparing the results of the Equilibrium Optimization algorithm (EOA) with those of the Gravitational Search Algorithm (GSA) and the Particle Swarm Optimization algorithm (PSO) (PSO). Simulation results demonstrate that the Equilibrium Optimization algorithm (EOA) outperforms both the Gravitational Search Algorithm (GSA) and the Particle Swarm Optimization algorithm (PSO). When simulated and statistical results of EOA were compared to those of other optimization methods, it was found that EOA is more effective and superior, resulting in the lowest volume value for wind turbine PMSG.