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<p>This research presents a novel approach to addressing the challenge of Global Maximum Power Point tracking (GMPP) in environments with partial shading condition. It reformulates it as a multi-objective optimization problem and introduces an innovative algorithm inspired by the ant lion optimizer (ALO) to effectively solve this complex task. This research introduces a novel approach to deriving Pareto solutions through implementing a multi-objective ALO (MOALO) algorithm. Furthermore, a fuzzy logic method is utilized to identify the optimal solutions. The proposed methodology incorporates two objective functions designed to address specific challenges: (i) reducing oscillations around the GMPP, as well as mitigating large tracking errors, and (ii) enhancing convergence rates. This research introduces an online implementation of a tracking system for GMPP. The system’s design involves real-time environmental monitoring and dynamic setting of the duty cycle DC–DC converter, ensuring precise tracking of the PV array’s GMPP. To assess its effectiveness, various tests were conducted using complex P–V curves with multiple peaks. The validation process employed MATLAB/SIMULINK simulations and practical laboratory experiments, demonstrating the method's accuracy in GMPP tracking. The suggested MOALO can improve the PV systems efficiency by up to 99%, while significantly mitigating transient fluctuations in the range around the maximum power point (MPP). </p>

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Experimental Design and Process Optimization to Track the Global Maximum Power Point in PV System

  • 期刊论文

  • 发表日期：

    2025年7月7日

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  • Habib Danaeifar,
  • Afshin Lashkarara,
  • Alireza Tavakoli

摘要

This research presents a novel approach to addressing the challenge of Global Maximum Power Point tracking (GMPP) in environments with partial shading condition. It reformulates it as a multi-objective optimization problem and introduces an innovative algorithm inspired by the ant lion optimizer (ALO) to effectively solve this complex task. This research introduces a novel approach to deriving Pareto solutions through implementing a multi-objective ALO (MOALO) algorithm. Furthermore, a fuzzy logic method is utilized to identify the optimal solutions. The proposed methodology incorporates two objective functions designed to address specific challenges: (i) reducing oscillations around the GMPP, as well as mitigating large tracking errors, and (ii) enhancing convergence rates. This research introduces an online implementation of a tracking system for GMPP. The system’s design involves real-time environmental monitoring and dynamic setting of the duty cycle DC–DC converter, ensuring precise tracking of the PV array’s GMPP. To assess its effectiveness, various tests were conducted using complex P–V curves with multiple peaks. The validation process employed MATLAB/SIMULINK simulations and practical laboratory experiments, demonstrating the method's accuracy in GMPP tracking. The suggested MOALO can improve the PV systems efficiency by up to 99%, while significantly mitigating transient fluctuations in the range around the maximum power point (MPP).