OPTIMIZING MAXIMUM POWER POINT TRACKER (MPPT) USING HYBRID CUCKOO SEARCH-PSO ALGORITHM ON SOLAR ENERGY CONVERSION SYSTEM UNDER PARTIAL SHADING CONDITIONS
Abstract
The efficiency of solar energy systems is highly dependent on the accurate tracking of the maximum power point (MPP), especially under partial shading conditions, which are common in real-world environments. Traditional Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) often fail to track the global MPP under such conditions, resulting in significant energy loss. This study presents a hybrid optimization approach using the Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms to improve the accuracy and speed of MPP tracking in solar energy systems under partial shading. The primary objective is to evaluate the effectiveness of the hybrid Cuckoo Search-PSO (CS-PSO) algorithm compared to conventional MPPT methods. A simulation-based approach was employed to model the solar energy conversion system and assess the performance of the MPPT algorithms. The results show that the CS-PSO algorithm outperforms traditional methods, achieving a tracking accuracy of 98.4%, with a reduced time to reach the MPP (8.7 seconds). In contrast, P&O and IncCond exhibited lower accuracy and slower convergence times. The study concludes that the hybrid CS-PSO algorithm provides a more efficient solution for optimizing MPPT under partial shading conditions, offering significant improvements in energy efficiency and tracking performance.
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References
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