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The efficiency of power conversion in PV systems is a critical parameter significantly influenced by diverse environmental conditions, with partial shading being a predominant factor contributing to the decline in power output. Various research methodologies, utilizing artificial intelligence and metaheuristics in Maximum Power Point Tracking (MPPT) methodologies, as well as array configuration and reconfiguration strategies, have been explored to mitigate the adverse effects of partial shading. This study introduces a direct control Cuckoo Search Optimizer (CSO) as an innovative approach for tracking the Global Maximum Power (GMP) in PV systems, employing a physical array reconfiguration-based Magic Square (MS) technique. The drawbacks of conventional MPPT algorithms under partial shading conditions (PSCs) are addressed by the suggested CSO algorithm, as well as those of particle swarm optimization (PSO), including reduced tracking efficiency and prolonged tracking times. A comparative analysis conducted using MATLAB/Simulink evaluated the performance of the CSO-MPPT in a PV array during PSCs, with results compared to those obtained using the PSO-MPPT. The simulation outcomes demonstrate that the CSO technique consistently achieves efficiencies exceeding 95% under various conditions, underscoring its efficacy in enhancing the performance of PV systems. Furthermore, the findings indicate that CSO surpasses the PSO technique in both convergence speed and tracking efficiency.

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Direct Control Cuckoo Search Optimizer with Magic Square Array Reconfiguration in PV Systems Under Partial Shading Conditions

  • 会议论文

  • 发表日期：

    2026年

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  • Mimoun Ilyas,
  • Mihoub Youcef,
  • Asri Aicha,
  • Hassaine Said

摘要

The efficiency of power conversion in PV systems is a critical parameter significantly influenced by diverse environmental conditions, with partial shading being a predominant factor contributing to the decline in power output. Various research methodologies, utilizing artificial intelligence and metaheuristics in Maximum Power Point Tracking (MPPT) methodologies, as well as array configuration and reconfiguration strategies, have been explored to mitigate the adverse effects of partial shading. This study introduces a direct control Cuckoo Search Optimizer (CSO) as an innovative approach for tracking the Global Maximum Power (GMP) in PV systems, employing a physical array reconfiguration-based Magic Square (MS) technique. The drawbacks of conventional MPPT algorithms under partial shading conditions (PSCs) are addressed by the suggested CSO algorithm, as well as those of particle swarm optimization (PSO), including reduced tracking efficiency and prolonged tracking times. A comparative analysis conducted using MATLAB/Simulink evaluated the performance of the CSO-MPPT in a PV array during PSCs, with results compared to those obtained using the PSO-MPPT. The simulation outcomes demonstrate that the CSO technique consistently achieves efficiencies exceeding 95% under various conditions, underscoring its efficacy in enhancing the performance of PV systems. Furthermore, the findings indicate that CSO surpasses the PSO technique in both convergence speed and tracking efficiency.