A comparative study of two meta-heuristic techniques for solar PV systems under partially shaded conditions

PV system is regarded as the most dependable source of energy because it is clean and environmentally friendly, but despite these qualities, it also faces challenges. As the output of the PV system is not constant, it depends on the position of the sun, and temperature, which is sufficient to place the system in partial shading conditions (PSC). Under these conditions, the photovoltaic (PV) system has one global peak and several local peaks. The traditional MPPT techniques are incapable of achieving global MPP. As a consequence, the goal of this work is to study the tracking performance of two meta-heuristic optimization techniques. They were designed to extract the GMPP from PV energy systems partially shaded. The two techniques are Crow Search Algorithm(CSA) and Flower Pollination Algorithm (FPA). To assess the effectiveness of the suggested two techniques, MATLAB simulation is used to investigate their performance under different PSCs. The results show that CSA and FPA-based trackers are capable of tracking global MPP with an average efficiency of 99.95%, and 99.98%, respectively, and an average tracking time of 0.19 s, and 0.25 s respectively for all tested conditions.