A particle swarm optimization-based maximum power point tracking algorithm for PV Systems

The need for energy continues to rise as civilization progresses. Because of its sustainable and ecologically favorable benefits, solar energy has sparked enormous interest. The output power of photovoltaic (PV) cells, being one of the most efficient solar energy systems, is easily impacted by the external environment. This research developed a maximum power point tracking (MPPT) approach to handle the problem of PV cell maximum power output. The pulse width modulation (PWM) control module settings are set according to the characteristics of the PV cells' output voltage using the online particle swarm optimization (PSO) variable step length technique. The output of the PV cells is stabilized near the maximum power point by dynamically modifying the output voltage step of the PV cells online (MPP). Experimental and simulation results are presented to demonstrate the effectiveness of the proposed MPPT-PSO algorithm, when it is compared with the well-known Perturb and Observe (P&O) MPPT technique. According to the findings, the suggested approach may achieve rapid convergence to the MPP, increased efficiency, and low oscillation under a variety of real-world environmental circumstances, enhancing the efficacy and validity of the proposed MPPT.