Optimal control strategies-based maximum power point tracking for photovoltaic systems under variable environmental conditions

Abstract

To increase the efficiency of photovoltaic (PV) array output under variable environmental conditions, maximum power point tracking (MPPT) of the solar arrays is needed. This paper proposes fuzzy logic controller (FLC)-based MPPT, artificial neural network (ANN)-based MPPT, neuro-fuzzy (NF)-based MPPT, particle swarm optimisation (PSO)-based MPPT, and cuckoo search (CS) algorithm-based MPPT to combine an adaptive controller and an optimisation, to guarantee global stability and a constant settling time for all operation conditions. This combination enables an increase in the power generated in comparison with conventional MPPT techniques. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions, and achieve good performance. It is also noticed that operating the photovoltaic array based on maximum power point tracking conditions gives about 43% extra power generation than in the case of normal operation.