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Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers

Author

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  • Águila-León, Jesús
  • Vargas-Salgado, Carlos
  • Díaz-Bello, Dácil
  • Montagud-Montalvá, Carla

Abstract

Environmental factors and load conditions influence the efficiency of power converters - key elements in Photovoltaic (PV) systems. This study employs optimization algorithms to fine-tune the converter's operation, focusing on metaoptimization, an algorithm increasing attention in recent research. The analysis introduces the Grey Wolf Optimizer (GWO) to enhance the Particle Swarm Optimization (PSO) algorithm. The optimized PSO algorithm is integrated into a PV system's Maximum Power Point Tracking (MPPT) controller. Implemented in MATLAB/Simulink, this approach is validated by combining measured data and simulation scenarios: 1. staggered vs 2. real irradiation changes. The results underscore the efficacy of the GWO-optimized PSO MPPT algorithm in enhancing the MPPT controller's performance. In Scenario 1, the GWO-optimized PSO algorithm demonstrated 9.1 % higher energy generation than the Incremental Conductance MPPT, 19.8 % more than the PSO MPPT, and 20.7 % more than the Perturb and Observe MPPT. Scenario 2 showed the superior performance of the GWO-optimized PSO MPPT, showcasing a 15.36 % increase in generation over the PSO and a 21.62 % improvement compared to the Perturb and Observe MPPT, with a 4.74 % advantage over the Incremental Conductance MPPT. The results highlight the GWO-optimized PSO MPPT's robustness under diverse conditions, emphasizing its potential PV technologies by optimizing MPPT controllers.

Suggested Citation

  • Águila-León, Jesús & Vargas-Salgado, Carlos & Díaz-Bello, Dácil & Montagud-Montalvá, Carla, 2024. "Optimizing photovoltaic systems: A meta-optimization approach with GWO-Enhanced PSO algorithm for improving MPPT controllers," Renewable Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:renene:v:230:y:2024:i:c:s0960148124009601
    DOI: 10.1016/j.renene.2024.120892
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