Implementation of Equilibrium Optimizer Algorithm for MPPT in a wind turbine with PMSG

Abstract

This paper proposes a new equilibrium optimizer algorithm to extracting the maximum power point tracking which is proposed in permanent magnet synchronous generator under randomly different varying wind speed conditions. It was inspired by controlled volume mass balance modes for estimating dynamic and equilibrium states. The equilibrium optimizer algorithm can mutate the random solving a problem via exploration and exploitation. A particle with its concentration updates its concentration with particular terms. It defined as best-so-far solution, called the equilibrium candidate and the other is equilibrium state, which encourages a particle to global search the domain. The tracking performances of equilibrium optimizer algorithm based trackers firstly are evaluated based on MATLAB software. Results of equilibrium optimizer are compared to two categories of existing optimization methods, including the most well-known genetic algorithm, particle swarm optimization. The performance of the equilibrium optimizer algorithm is analyzed, evaluated and compared based on some key parameters, which are the active power, and turbine power factor under randomly different varying wind speed conditions. Additionally, the obtained results show that the equilibrium optimizer tracker has superiority compared with in all the studied cases