Abstract

The increasing need for sustainable energy sources, especially solar power, is driven by factors such as rising energy consumption, high costs of fossil fuels, and environmental concerns like global warming. Solar power plants are desirable because of their low environmental impact and operational costs. However, the efficiency of solar photovoltaic (PV) systems remains challenging due to their nonlinear characteristics. Maximizing the power output for a given load through Maximum Power Point (MPP) tracking is thus vital. This research aims to enhance the efficiency of PV systems by proposing two innovative hybrid meta-heuristic Maximum Power Point Tracking (MPPT) methods. These methods combine hybrid incremental conductance (IC) with Beluga Whale Optimization (BWO), anchored on a Fractional Order Proportional Integral (FOPI) controller. The first approach aims to maximize the power harvested from solar panels while minimizing oscillations, enabling more accurate and faster MPP tracking. The second method also uses the hybrid IC with BWO based on the FOPI controller for improved performance in MPP tracking. The study compares these novel techniques with existing algorithms like BWO-PI, P&O, and IC for their efficiency, complexity, and ease of implementation. Simulation tests conducted using Matlab software indicate that the proposed hybrid FOPI-BWO method achieves an average tracking efficiency of roughly 99.420%. The method stands out for its ease of use, robustness, and stable power tracking, reaching efficiencies of up to 99.603%.The optimized MPPT is also integrated with a standalone PV system through an inverter to ensure high power delivery to the AC load. This research offers significant contributions to solar energy harvesting by proposing robust and efficient MPP tracking methods.