Modified grey wolf optimizer based MPPT design and experimentally performance evaluations for wind energy systems

Abstract

The method that aims to operate the wind energy system (WES) at the maximum power point (MPP) is called the maximum power point tracking (MPPT) method in the literature. The grey wolf optimization (GWO) is one of the newest population-based meta-heuristic methods, and its performance as an MPPT algorithm in WESs has not been extensively studied yet. In this study, the standard GWO algorithm has been modified considering the requirements of WES, so that the system can reach the MPP quickly and stably, thereby improving the system’s efficiency. Moreover, the performance of the proposed method is examined comparatively with the well-known MPPT methods via simulation and experimental studies for many possible scenarios. It is demonstrated that the proposed modified GWO (MGWO) performance is better than the classic and modified perturb and observe methods. The results have also been compared with the Fibonacci Search (FS) and Golden Section (GS) Search-based MPPT algorithms newly presented in the literature for WES. Although the results of FS, GS, and MGWO-based MPPT algorithms are very close to each other, it has been observed that FS has a slightly better performance.

Keywords

• Wind power systems
• Maximum power point tracking
• Grey wolf optimizer