Robust MPPT Tracking for PV Solar Power using Metaheuristic Algorithms

Abstract

The emphasis of this paper is a study and compare robust of MPP tracking algorithms. In order to achieve the maximum power, a MPPT controller is often associated with photovoltaic panels which controls the DC voltage and current. In this work, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Perturb and Observe (P&O), and Fuzzy Logic Control (FLC) are used in order to achieve the optimum power conditions. The studied PV system contains six 244 W PV panel implemented in series feeding a resistive load through a controllable DC-DC boost converter. To demonstrate the effectiveness of the proposed techniques under standard test conditions, as well as partial shading conditions, simulations are performed under the MATLAB/Simulink environment. It is revealed in simulation results that the PSO and P&O algorithms are more efficient in locating the effective MPP. Furthermore, when working under partial shading, the traditional P&O and FLC are failing to locate the real MPP. Indeed, those MPPT techniques reach the local power peak, while PSO and GWO reach successfully the effective MPP with an efficiency of 99% and 95% in only 92 ms and 69 ms, respectively.