Performance Improvement of Wind Generator Using Hybrid Particle Swarm Algorithm and Grey Wolf Optimizer

Abstract

The past few years have witnessed a massive increase in energy demand and severe environmental deterioration. As a result, renewable energy’s, specifically wind energy’s contribution, to the electric power network has increased. This paper introduces a new technique combining two optimization methods, which are Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) in order to increase the efficiency of variable-speed wind turbine connected to a permanent-magnet synchronous generator. This system is connected to the grid by a frequency converter using a conventional proportional-integral controller. PSO is an optimization technique that aims at ascertaining the optimal solution by changing the location of particles in a predetermined searching field. GWO is also an optimization technique whose idea depends on the behavior of grey wolves. This article shows that the performance of the GWO is improved by hybridizing GWO with PSO. Nowadays, controller designers are facing a great challenge for tuning proportional-integral controllers, principally in non-linear systems like Wind Energy Conversion Systems. For assuring the efficiency of the introduced hybrid control scheme, a comparison between the simulation outcomes of the GWO with PSO and another optimization technique, which is the Genetic Algorithm is implemented. The simulation outcomes depict the potency of the GWO with the PSO method in order to improve the grid-connected wind generator response under different operating conditions. In order to model realistic performance, data from the Zaafarana wind farm in Egypt has been used as a guide to provide realistic details on wind speed in this article. The MATLAB/Simulink program is utilized to demonstrate the simulation process.