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<p>Partial shading greatly diminishes the performance of Photovoltaic (PV) systems by creating multiple power points on its characteristic curve, presenting difficulty for standard Maximum Power Point Tracking (MPPT) techniques to locate true optimal tracking point. This results in substantial power losses and potential hot spots, which damage the PV cells. The proposed research introduces an Adaptive Chicken Swarm Optimized Fuzzy (ACSOF) MPPT controller that combats these issues and adaptively tracks the MPP, ensuring optimal PV system energy harvest, even under complex Partial Shading Conditions (PSCs). This adaptive control scheme is designed to respond to rapid changes in shading conditions, which are common in urban environments and under cloud movement. ACSOF identifies and tracks the true maximum power point while adaptively responding to sudden irradiance and shading changes, ensuring smooth transitions without oscillations or instability. This optimized Fuzzy MPPT ensures that the transition between various levels of shading is smooth, mitigating the impact on power generation efficiency. The adaptability is further enhanced by a Quadratic Boost converter, which ensures stable voltage output amidst solar irradiance variability. When tested in MATLAB simulations across unshaded, weakly shaded, and fully shaded scenarios, the proposed solution demonstrates a smooth transition between shading levels, preserving efficiency in power generation. The ACSOF MPPT controller distinguishes itself with an impressive 99.95% efficiency rate and an exceptionally low average power loss of only 0.104%.</p>

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Smart Control of PV Arrays Under Partial Shading Using Adaptive Fuzzy Techniques

  • 期刊论文

  • 发表日期：

    2026年7月13日

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  • Rajesh Prasad,
  • A. A. Mohamed Faizal,
  • Murali Matcha,
  • R. J. Anandhi

摘要

Partial shading greatly diminishes the performance of Photovoltaic (PV) systems by creating multiple power points on its characteristic curve, presenting difficulty for standard Maximum Power Point Tracking (MPPT) techniques to locate true optimal tracking point. This results in substantial power losses and potential hot spots, which damage the PV cells. The proposed research introduces an Adaptive Chicken Swarm Optimized Fuzzy (ACSOF) MPPT controller that combats these issues and adaptively tracks the MPP, ensuring optimal PV system energy harvest, even under complex Partial Shading Conditions (PSCs). This adaptive control scheme is designed to respond to rapid changes in shading conditions, which are common in urban environments and under cloud movement. ACSOF identifies and tracks the true maximum power point while adaptively responding to sudden irradiance and shading changes, ensuring smooth transitions without oscillations or instability. This optimized Fuzzy MPPT ensures that the transition between various levels of shading is smooth, mitigating the impact on power generation efficiency. The adaptability is further enhanced by a Quadratic Boost converter, which ensures stable voltage output amidst solar irradiance variability. When tested in MATLAB simulations across unshaded, weakly shaded, and fully shaded scenarios, the proposed solution demonstrates a smooth transition between shading levels, preserving efficiency in power generation. The ACSOF MPPT controller distinguishes itself with an impressive 99.95% efficiency rate and an exceptionally low average power loss of only 0.104%.