and essential issue nowadays. Each PV module has its own specific
characteristics and its own maximum power point (MPP). This maximum power
point varies according to the change in temperature and solar irradiation. Therefore,
it is important to use a maximum power point tracker (MPPT) in the PV system; to
guarantee a maximum output power from solar module under varying conditions.
There are many algorithms used to perform controller function that are
conventional and meta-heuristic methodologies, in this paper a proposed metaheuristic
algorithm based on an improved teaching-learning based optimization
algorithm (ITLBO) is presented and investigated to track the MPP extracted from
the PV system under variable operating conditions. The proposed algorithm gives
the available maximum power under non-uniform solar irradiation. The obtained
results are compared with those obtained via the conventional perturb and observe
(P&O), particle swarm optimization (PSO) and TLBO algorithms. The proposed
ITLBO results are more accurate and give fast convergence output power.