Bio-inspired algorithms based on maximum power point tracking control for PMSG wind power generation systems

Abstract

In this paper, a studying the performance of three optimization techniques include genetic algorithm, particle swarm optimization and differential evolution algorithm. They are designed to extract the maximum power point tracking and suggested in permanent magnet synchronous generator wind energy systems under randomly variable wind speed cases. The performance of the three algorithms is studied, assessed, and compared using key characteristics such as turbine power coefficient, convergence time, standard deviation, reliability, and turbine power under the same operating conditions. The tracking performances based on the three algorithms are assessed using MATLAB software. The results show that the differential evolution algorithm has a convergence to the global maximum power point that better solution quality while particle swarm optimization has a faster execution time in comparison with the genetic algorithm for solving the maximum power point tracking