Global MPPT in Solar Photovoltaics: Evaluating PSO, FPA, and CSA Under Partial Shading
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Abstract
Maximizing the power output of solar photovoltaic (SPV) systems under partial shading conditions is crucial for improving renewable energy efficiency. Traditional Maximum Power Point Tracking (MPPT) methods often struggle to locate the global Maximum Power Point (MPP) due to multiple local maxima on the power-voltage (P-V) curve. This study evaluates three advanced optimization algorithms—Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Cuckoo Search Algorithm (CSA)—to ensure global MPPT under varying environmental conditions. A PV model with three series-connected modules, each containing 20 cells with bypass diodes, is used to mitigate shading effects. Partial shading scenarios involve irradiance levels of 1000 W/m², 300 W/m², and 600 W/m² for the modules, with constant temperature. A DC-DC boost converter extracts maximum power using a 30 mH inductor, 100 µF capacitors, and a 10 kHz switching frequency. PSO adjusts particle positions dynamically, FPA employs Lévy flights for global exploration, and CSA replaces suboptimal solutions to maintain diversity. Performance metrics, including convergence time, tracking efficiency, and precision, are analysed under various irradiance and temperature conditions. Results show that PSO offers rapid convergence, CSA delivers the highest power output with stability, and FPA provides smoother performance but lower efficiency. These findings highlight the trade-offs among these algorithms and their potential for enhancing MPPT in diverse scenarios.
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