A Comparative Study of Metaheuristic Optimization Algorithms for GMPP Tracking in Photovoltaic Systems Under Partial Shading Conditions

In this paper, a comparative analysis of the most popular metaheuristic optimization algorithms used for maximum power point tracking (MPPT) in the case of partial shading conditions (PSC) is carried out. The algorithms include Particle Swarm Optimization (PSO), improved Grey Wolf Optimizer (IGWO), Firefly Algorithm (FA), Salp Swarm Algorithm (SSA) as well as Harris Hawks Optimization (HOA). Moreover, the solar photovoltaic (PV) system of 700W is used and it is linked with DC/DC boost converter for supplying a standalone load. The PSO, IGWO, FA, SSA, and HOA are conducted and validated under three patterns of irradiance for uniform and non-uniform irradiation conditions. The results of the uniform irradiance show that the PSO and HOA algorithms have high efficiencies of 95% and 95,2%, respectively. Moreover, the results under the complex PSC the FA, HOA, and IGWO presents good efficiencies which are the efficiency of the FA is 95 %, for the HOA method is 94.9%, and the efficiency of the IGWO is 90.6 %. The worst method under the complex PSC is the PSO method which is failed to track the GMPPT. Finally, from the simulation results under complex PSC, the effectiveness of PSO and HOA raises some concerns in terms of both convergence and stability, whereas IGWO and SSA yield fairly decent results, while the best algorithm is the FA which has more stable results under PSC.