Abstract :

This Project Focuses On The Maximum Power Point Tracking (MPPT) Under Partial Shading Conditions. Partial Shading Conditions Significantly Impact The Performance Of Photovoltaic (PV) Systems, Leading To Reduced Energy Output And Efficiency. MPPT Algorithms Play A Crucial Role In Optimizing PV System Performance Under Partial Shading Conditions. Metaheuristic Algorithms Have Gained Popularity In Recent Years Due To Their Ability To Efficiently Track The Global Maximum Power Point (GMPP) Under Partial Shading Conditions. Among Metaheuristic Algorithms, Three Popular Optimization MPPT Techniques Are Compared In This Work: Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), And Dragonfly Optimization Algorithm (DFO). A Novel Hybrid PSO-DFO Algorithm Is Proposed In This Paper, Which Stands Out For Its Higher Stability And Efficiency In Converging To The MPP Under Partial Shading Conditions. Simulation Results On A Four-panel PV System With A DC-DC Boost Converter In MATLAB/Simulink Demonstrate That The Proposed Hybrid PSODFO Achieves A Tracking Factor Of 97%, Outperforming GWO (92%), PSO (90%), And Standalone DFO (94%).

Published:

24-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

3000 - 3007

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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