A swarm-based soft computing approach for harvesting maximum power in practical photovoltaic system

In order to satisfy the depletion of fossil fuels and the increasing demand, researchers are concentrating on renewable energy sources such as photovoltaic (PV) systems. To guarantee the effective operation of PV systems, a robust Maximum Power Point (MPP) Tracking technique is required to address nonlinear and multimodal challenges, such as MPP Tracking under uniformly distributed irradiance and partially shaded situations. In this context, this work proposes a Zone Segregated Adaptive Particle Swarm Optimization (ZSAPSO) technique for addressing the MPP Tracking (MPPT) problem in partially shaded and uniformly distributed irradiance situations. The proposed ZSAPSO technique basically uses the basic structure of the APSO algorithm, but in the APSO technique, only one local best particle is considered throughout the execution, whereas in ZSAPSO, three local best particles are considered from three zones of the search space to incur a well exploration virtue of the algorithm during the start of its execution. Moreover, the sensitivity analysis is carried out in search of better performance of the ZSAPSO. Thereafter, the performance based on Settling Time (ST), steady state error (ESS), and efficiency of MPP tracking of the proposed ZSAPSO is compared with other cutting-edge techniques in MPPT application. All the simulated works are executed through MATLAB/SIMULINK, and the same are authenticated with the help of a practical setup. Every outcome shows how effectively the ZSAPSO manages the MPPT problem. Specifically, under uniform irradiance, ZSAPSO achieved a tracking efficiency of 99.987% with the lowest steady-state error (0.037%) and improved settling time compared to conventional PSO variants. Under partial shading, it attained 99.977% efficiency with the minimum ESS (0.025%) and faster convergence than other swarm-based techniques.