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<p>This paper develops and investigates a novel maximum power point tracking (MPPT) approach for photovoltaic (PV) systems, particularly focusing on enhancing system performance under challenging operational conditions such as partial shading, rapid irradiance variations, and abrupt load changes. The proposed method leverages parametric fuzzy interval regression models combined with multiple aggregation operators to dynamically manage uncertainties and optimize voltage and current reference tracking. By integrating proportional–integral controllers and employing aggregation operators including MIN, MAX, ordered weighted average, weighted sum, and the Choquet Integral for 2-additive measures (2A-CI), the proposed method effectively balances robustness and precision. Comprehensive numerical simulations were conducted using MATLAB–Simulink to validate the effectiveness of the proposed MPPT technique across various climatic conditions. The simulation results demonstrate significant improvements in tracking accuracy and response time compared to conventional MPPT algorithms such as Perturb and Observe and Incremental Conductance. Specifically, the proposed approach achieved consistently higher efficiency rates, demonstrated superior stability with minimal oscillations, and effectively tracked the global maximum power point under complex partial shading scenarios. These findings confirm the method's robust adaptability and precision, highlighting its potential for enhancing the operational efficiency and reliability of PV systems under diverse real-world conditions.</p>

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A new MPPT approach based on fuzzy least squares regression with aggregation operator for PV systems under partial shading

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • Abderrahim Fayçal Megri,
  • Houria Bayarassou

摘要

This paper develops and investigates a novel maximum power point tracking (MPPT) approach for photovoltaic (PV) systems, particularly focusing on enhancing system performance under challenging operational conditions such as partial shading, rapid irradiance variations, and abrupt load changes. The proposed method leverages parametric fuzzy interval regression models combined with multiple aggregation operators to dynamically manage uncertainties and optimize voltage and current reference tracking. By integrating proportional–integral controllers and employing aggregation operators including MIN, MAX, ordered weighted average, weighted sum, and the Choquet Integral for 2-additive measures (2A-CI), the proposed method effectively balances robustness and precision. Comprehensive numerical simulations were conducted using MATLAB–Simulink to validate the effectiveness of the proposed MPPT technique across various climatic conditions. The simulation results demonstrate significant improvements in tracking accuracy and response time compared to conventional MPPT algorithms such as Perturb and Observe and Incremental Conductance. Specifically, the proposed approach achieved consistently higher efficiency rates, demonstrated superior stability with minimal oscillations, and effectively tracked the global maximum power point under complex partial shading scenarios. These findings confirm the method's robust adaptability and precision, highlighting its potential for enhancing the operational efficiency and reliability of PV systems under diverse real-world conditions.