Generation of Maximum Possible Power in Solar Photovoltaic system utilizing modified HMFA based Artificial Neural Network Controller

Abstract

Solar or Photovoltaic (PV) system is an alternative source of clean electricity aid that has received more attention in the industries and research. The connotation, MPPT or, the maximum power point-tracking (MPPT) is utilized within PV systems to haul out the greatest power from the PV-arrays. The algorithm related to MPPT utilized within conventional methods suffers from being slow and inaccurate during sudden variation in (T) i.e. temperature and (G) that connotes to irradiance. As we know regarding the Solar PV array parameters that change continuously according to the sunlight. Thus, if we apply the conventional algorithm pertaining to MPPT, it may lead to generating losses in devices connected across on the panel or the load. To avoid the above loss rates, the incumbent paper proposes a hybrid modified firefly algorithm (HMFA) based MPPT algorithm. The hybrid MFA is put to utilization within an artificial neural network regulator to get the maximum possible power from within the PV arrays. Here the proposed controller generates required signals to regulate the DC/DC based boost converting mechanism. The MATLAB/Simulink tool has been used to get the outcome of the simulation. This proposed HMFA based ANN controller reduces the oscillations around the greatest possible power-point and improves its efficiency. The outcome of the simulation as obtained from the suggested method is subjected to comparison in regard to the existing algorithm of Particle Swarm Optimization (PSO).