Experimental Investigation of Two Bio-inspired MPPT Algorithms for Partially Shaded PV Arrays

Maximum power point tracking (MPPT) of partially shaded photovoltaic (PV) arrays is a very complex task due to the emergence of multiple local peaks (LPs) along with the global peak (GP) in the P–V array characteristic curve. Traditional tracking algorithms are unable to track the MPP under partial shading conditions (PSCs) and may trick to operate at LP instead of the GP. Recently, Bio-inspired MPPT Algorithms are widely utilized to enhance the power yield from partially shaded PV arrays and efficiently track the GP. This paper presents an experimental investigation of two bio-inspired MPPT algorithms for partially shaded photovoltaic (PV) arrays. The first algorithm is the Autonomous Group Particle Swarm Optimization (AGPSO) algorithm, and the second is the Cuckoo Search (CS) Algorithm. Firstly, the performance of both algorithms was evaluated under different PSCs using MATLAB/SIMULINK. Then, an experimental study was carried out to validate the simulation results. The simulation and experimental results show that the CS outperforms the AGPSO in terms of tracking accuracy and speed.