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<p>In the design of integrated photovoltaic (PV) systems, partial shading emerges as a significant hurdle, adversely affecting power output, efficiency, and leading to losses due to power mismatch. However, the integration of bypass diodes can effectively counteract the emergence of multiple peaks within the P–V curve. This necessitates the implementation of a robust Maximum Power Point Tracking (MPPT) controller to accurately track the global maximum power (GMP) in the face of Partial Shading Conditions (PSCs). Despite a range of conventional methods and optimization algorithms available, their performance in identifying the GMP among multiple peaks is often subpar. This study introduces an MPPT technique called Adaptive Coefficients Particle Swarm Optimization (ACPSO), specifically designed for the innovative triple-tied PV system configuration, to enhance the extraction of maximum power under PSCs. The ACPSO method diverges from traditional PSO techniques by incorporating adaptive coefficients, which accelerate convergence and improve the distinction between local and global peaks. This MPPT method has been simulated in Simulink/MATLAB and evaluated against Grey-Wolf Optimization, Particle Swarm Optimization (PSO), Perturbation &amp; Observation, and Cuckoo Search in terms of convergence time, GMP tracking, and tracking efficiency and speed. Additionally, the proposed MPPT has undergone experimental validation in various PSC scenarios, establishing its effectiveness and superiority over other MPPT techniques.</p>

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A Fast-Adaptive Coefficient Particle Swarm Optimization MPPT Technique for TT Configured PV System Under PSCs

  • 期刊论文

  • 发表日期：

    2025年2月4日

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  • Praveen Kumar Bonthagorla,
  • Santhosh Yedla,
  • Abhilash Sakhare,
  • Suresh Mikkili

摘要

In the design of integrated photovoltaic (PV) systems, partial shading emerges as a significant hurdle, adversely affecting power output, efficiency, and leading to losses due to power mismatch. However, the integration of bypass diodes can effectively counteract the emergence of multiple peaks within the P–V curve. This necessitates the implementation of a robust Maximum Power Point Tracking (MPPT) controller to accurately track the global maximum power (GMP) in the face of Partial Shading Conditions (PSCs). Despite a range of conventional methods and optimization algorithms available, their performance in identifying the GMP among multiple peaks is often subpar. This study introduces an MPPT technique called Adaptive Coefficients Particle Swarm Optimization (ACPSO), specifically designed for the innovative triple-tied PV system configuration, to enhance the extraction of maximum power under PSCs. The ACPSO method diverges from traditional PSO techniques by incorporating adaptive coefficients, which accelerate convergence and improve the distinction between local and global peaks. This MPPT method has been simulated in Simulink/MATLAB and evaluated against Grey-Wolf Optimization, Particle Swarm Optimization (PSO), Perturbation & Observation, and Cuckoo Search in terms of convergence time, GMP tracking, and tracking efficiency and speed. Additionally, the proposed MPPT has undergone experimental validation in various PSC scenarios, establishing its effectiveness and superiority over other MPPT techniques.