This paper proposes application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the design of direct-driven permanent magnet synchronous generator machine (PMSGs) for wind turbine applications. The power rating of these machines is in the mega watt (MW) level. The constraints and requirements of the generator are outlined. The proposed design scheme optimizes various PMSG parameters like Pole pair number, Linear current density, Air gap thickness, Rotor outer diameter, Relative width of the permanent magnet etc to achieve certain objectives like maximizing efficiency, increasing Torque, improving power factor etc. The results obtained by GA algorithm and those by PSO algorithm are compared. The performance of Particle Swarm Optimization is found to be better than the Genetic Algorithm, as the PSO carries out global search and local searches simultaneously, whereas the Genetic Algorithm concentrates mainly on the global search. Results show that the proposed PSO optimization algorithm is easy to develop and apply and produced competitive designs compared to the GA algorithm.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.