Analysis of Hybrid Meta Heuristic Optimization Based MPPT Controller for Improved Operational Efficiency of Solar PV System

Abstract

The incorporation of sophisticated control methodologies is essential. The goal of this work is to optimise the performance of solar PV systems through the design and development of a hybrid duty cycle controller based on the Grey Wolf Optimizer-Cuckoo Search Algorithm (GWO-CSA). The main goal is to maximise power point tracking (MPPT) in a variety of environmental settings, which will increase the system's overall efficiency and dependability. The suggested hybrid GWO-CSA algorithm makes use of the cuckoo bird's brood parasitism and the social hierarchy and hunting behaviour of grey wolves to provide a reliable and effective search mechanism for the ideal duty cycle. The shortcomings of traditional MPPT approaches are addressed by this unique methodology, which improves convergence speed, accuracy, and responsiveness to sudden changes in temperature and sun irradiation. MATLAB/Simulink simulation simulations were performed to verify the effectiveness of the hybrid GWO-CSA controller. Traditional MPPT methods including Particle Swarm Optimisation (PSO), Incremental Conductance (IC), and Perturb and Observe (P&O) were evaluated using the performance metrics. The outcomes show that the hybrid GWO-CSA controller continuously beats the traditional techniques, obtaining faster reaction times and greater energy conversion efficiency. Furthermore, the hybrid GWO-CSA algorithm demonstrated enhanced stability and resilience, reducing power fluctuations and guaranteeing dependable functioning in the presence of partial shade and further environmental disruptions. The application of this cutting-edge control approach in solar photovoltaic systems has the potential to greatly improve their operational effectiveness, hence augmenting the sustainability and financial feasibility of solar energy solutions. To sum up, the hybrid GWO-CSA based duty cycle controller offers a viable way to raise the solar PV systems' operational efficiency. The construction of more robust and efficient renewable energy systems is facilitated by this research, which sets the way for future developments in intelligent control techniques.