EFFICIENT MPPT FOR PV SYSTEMS UNDER SHADING CONDITION USING HYBRID METAHEURISTIC OPTIMIZATION MODEL

The increasing penetration of photovoltaic (PV) systems in the energy generation mix make it necessary for Maximum Power Point Tracking (MPPT) techniques to be more and more efficient. Solar (PV) systems are generally used under partial shading and operate at many local peaks on the power-voltage (P-V) curve hence making Global Maximum Power Point (GMPP) tracking hard. In such conditions, traditional MPPT methods like Perturb and Observe (P&O) as well as Incremental Conductance (IC), most of the time fails to track GMPP effectively, leading towards higher power losses resulting in decreased efficiency. In this research a new hybrid metaheuristic method, which combines Particle Swarm Optimization (PSO) and Differential Evolution (DE), is suggested for improving MPPT performance in PV systems. With the help of hybrid PSO-DE model, which has better convergence speed and exploration property as well as robustness to local minimum with random search directions (Differential Evolution), it helps locate GMPP correctly even under various shading situations. The work provides novelty by suggesting an organized hybrid PSO-DE based MPPT framework that strictly weighs the quick convergence and worldly search to dependable Global Maximum Power Point tracking to track the partially shaded conditions. The proposed method incorporates both PSO exploitation and DE exploration in one optimization loop, which makes it more stable, converges faster, and is more resilient than current standalone or weakly connected hybrid MPPT techniques. The results clearly indicate substantial improvements in the tracking speed, power output and stability with respect to existing methods when the proposed hybrid PSO-DE method is employed especially for not uniform set points. The results of this study can help in improving the efficiency and reliability of solar energy systems so that PSO-DE hybrid model usable for real world applications. This work advances the field of MPPT by supporting new technologies to respond to a fluctuating set behaviour for renewable energy systems and provides insight into more efficient solar solutions.