A novel EPSO algorithm based on shifted sigmoid function parameters for maximizing the energy yield from photovoltaic arrays: An experimental investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19 % in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %. • Proposing a novel EPSO MPPT technique for PV systems. • The performance of the EPSO algorithm is compared to other soft computing algorithms. • Simulation results validate the superiority of the EPSO over the other algorithms. • Experimental results confirm the effectiveness of the proposed EPSO algorithm. • The MPPT efficiency achieved is 99.19 % with an experimental tracking time of 2.4 s.