-Recently, electricity generation from solar photovoltaic (PV) has gained popularity throughout 8 the world due to its profuse availability and eco-friendly nature. Consequently, extraction of maximum 9 power from solar PV energy systems was the point of interest in the current researches. Various techniques 10 have been proposed to track the maximum power point (MPP) from solar PV energy systems under 11 variable environmental conditions. Conventional maximum power point tracking (MPPT) techniques have 12 demonstrated the ability to track MPP with uniform solar irradiance. However, the ability of these 13 techniques to track the accurate MPP with the condition of partial shading (PS) is not guaranteed. Hence, 14 this paper intended to present novel optimization techniques to mitigate the PS effect and proficiently track 15 the global maximum power point (GMPP). Grey Wolf Optimization (GWO), Moth-Flame Optimization 16 (MFO), Salp Swarm Algorithm (SSA) and Hybrid Particle Swarm Optimization-Gravitational Search 17 Algorithm (PSO-GSA) techniques have been proposed to handle this dilemma. The proposed techniques 18 have been simulated and analyzed using MATLAB/SIMULINK. Furthermore, these techniques have been 19 compared with the conventional PSO algorithm for validation. Statistical and sensitivity analysis have been 20 established to compare the performance, check the stability, and determine the best technique out of the 21 proposed techniques. Results showed the superiority of GWO in the speed of convergence and the time to 22 catch GMPP. Moreover, the sensitivity analysis demonstrated the stability, successfully rate, and tracking 23 efficiency of PSO-GSA technique. Finally, this paper gives an open reference to these optimizers to 24 attempt mass research works in PV systems under PS.
