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<p>This paper presents an advanced MPPT algorithm for photovoltaic (PV) systems based on the fractional open-circuit voltage (FOCV) technique, enhanced using a musical chairs algorithm (MCA). The proposed MCA-FOCV method addresses the limitations of fixed factors in conventional approaches by introducing an adjustable voltage factor <i>K</i><sub><i>v</i></sub> ​, which adapts dynamically to changes in irradiance conditions. Three significant enhancements are incorporated to improve convergence speed, tracking accuracy, and steady-state performance. First, an adaptive inertia weighting (AIW) strategy is employed to adjust the balance between exploration and exploitation during optimization, reducing premature convergence and enhancing tracking performance. Second, a Top-2 best chair reinforcement (T2BCR) mechanism is introduced to guide the search agents toward the two globally best solutions in each iteration, facilitating faster convergence. Third, the initialization phase is accelerated by a lookup table initialized with Gaussian interpolation, which generates high-quality initial estimates for <i>K</i><sub><i>v</i></sub> ​ based on pre-characterized irradiance-voltage profiles, thereby reducing computational overhead. Simulation results under rapidly varying weather conditions demonstrate that the enhanced MCA-FOCV algorithm outperforms conventional MPPT methods, achieving a faster tracking speed and maintaining minimal oscillations, with an average tracking accuracy exceeding 99.73%. These findings confirm the applicability of the proposed method for real-time embedded PV systems with limited computational resources.</p>

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An Enhanced Musical Chairs Algorithm-Based Fractional Open-Circuit Voltage MPPT Method with Adaptive Enhancements for High-Precision Tracking in Photovoltaic Systems

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Muhannad Alshareef

摘要

This paper presents an advanced MPPT algorithm for photovoltaic (PV) systems based on the fractional open-circuit voltage (FOCV) technique, enhanced using a musical chairs algorithm (MCA). The proposed MCA-FOCV method addresses the limitations of fixed factors in conventional approaches by introducing an adjustable voltage factor Kv ​, which adapts dynamically to changes in irradiance conditions. Three significant enhancements are incorporated to improve convergence speed, tracking accuracy, and steady-state performance. First, an adaptive inertia weighting (AIW) strategy is employed to adjust the balance between exploration and exploitation during optimization, reducing premature convergence and enhancing tracking performance. Second, a Top-2 best chair reinforcement (T2BCR) mechanism is introduced to guide the search agents toward the two globally best solutions in each iteration, facilitating faster convergence. Third, the initialization phase is accelerated by a lookup table initialized with Gaussian interpolation, which generates high-quality initial estimates for Kv ​ based on pre-characterized irradiance-voltage profiles, thereby reducing computational overhead. Simulation results under rapidly varying weather conditions demonstrate that the enhanced MCA-FOCV algorithm outperforms conventional MPPT methods, achieving a faster tracking speed and maintaining minimal oscillations, with an average tracking accuracy exceeding 99.73%. These findings confirm the applicability of the proposed method for real-time embedded PV systems with limited computational resources.