Maximum power point tracking techniques for photovoltaic systems: a comparative study

Photovoltaic systems (PV) are one of the most important renewable energy resources (RER). It has limited efficiency leading to increasing number PV units required for certain input power i.e. higher initial cost. To overcome this problem, maximum point tracking (MPPT) controllers used. This work introduces a comparative study seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb observe (P&O), modified (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), cuckoo search (CS). Under same climatic conditions, comparison between these techniques in view some criteria’s: efficiencies, response, implementation cost, others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that improve lowest resulted without control. ANFIS is highest efficiency, but it requires more sensors. CS ANN produce best performance, provided significant advantages over others low fast computing time. P&O oscillation, drawback eliminated M-P&O. FLC longest time due software complexity, INC