Intelligent wave algorithm for maximum power point tracking in flyback converter-based photovoltaic systems

The growing demand for renewable energy sources has made photovoltaic (PV) systems an essential component of sustainable energy production. However, the efficiency of PV systems is significantly affected by variations in irradiance and temperature, making Maximum Power Point Tracking (MPPT) a crucial control strategy. This thesis investigates and compares several MPPT algorithms, including classical methods such as Perturb and Observe (P&O) and Incremental Conductance (InC), as well as metaheuristic optimization techniques such as Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO). Building on these approaches, a novel algorithm, the Intelligent Wave Algorithm (IWA), is proposed and applied for the first time in the literature to MPPT in flyback converter-based PV systems. All algorithms were modeled and simulated under identical operating conditions in MATLAB/Simulink. Performance metrics, including settling time, steady-state stability and power extraction, were analyzed. The results show that while P&O and InC are simple and widely used, they suffer from oscillations and reduced efficiency. PSO and HHO improved convergence speed and robustness, achieving higher steady-state power with fewer oscillations. The proposed IWA algorithm, however, demonstrated superior performance across all scenarios, achieving the fastest convergence (0.007–0.015 s), the highest power output (46–47 W) with negligible overshoot or oscillations. The findings confirm that IWA provides a highly effective and computationally efficient solution for real-time MPPT applications. This contribution represents a novel advancement in the field of renewable energy optimization and offers a promising pathway for enhancing PV system performance in both laboratory and industrial environments.