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Photovoltaic (PV) technology has gained widespread adoption across various industrial sectors and research laboratories due to its ability to convert solar power into usable electrical energy. Our paper evaluates and compares the efficiency of several Maximum Power Point Tracking (MPPT) techniques for controlling a PV system under diverse and rapidly changing irradiance conditions. The primary objective is to assess three methods —Artificial Neural Networks (ANN), Incremental Conductance (INC), and Fuzzy Logic (FL) —and determine their relative performance and suitability. A secondary objective is to minimize energy losses in the PV module, thereby enhancing overall conversion efficiency. The three algorithms are implemented and rigorously tested in the MATLAB/Simulink simulation environment. The simulation results show that the FL technique outperforms INC by providing a faster dynamic response, smaller steady-state oscillations, and better real-time adaptability, while ANN offers intermediate performance. These findings indicate that the FL technique can significantly improve the tracking efficiency of PV under variable environmental conditions.

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Performance Comparison of Fuzzy-Logic, Incremental Conductance, and Artificial Neural Network MPPT Technique in Dynamic Solar Irradiance Environments

  • 会议论文

  • 发表日期：

    2026年

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  • Kamal Boujaghama,
  • Naoufel Khaldi,
  • Rachid Markazi,
  • Lahcen Amhaimar,
  • Mbarek Chahboun,
  • Youssef El Bid

摘要

Photovoltaic (PV) technology has gained widespread adoption across various industrial sectors and research laboratories due to its ability to convert solar power into usable electrical energy. Our paper evaluates and compares the efficiency of several Maximum Power Point Tracking (MPPT) techniques for controlling a PV system under diverse and rapidly changing irradiance conditions. The primary objective is to assess three methods —Artificial Neural Networks (ANN), Incremental Conductance (INC), and Fuzzy Logic (FL) —and determine their relative performance and suitability. A secondary objective is to minimize energy losses in the PV module, thereby enhancing overall conversion efficiency. The three algorithms are implemented and rigorously tested in the MATLAB/Simulink simulation environment. The simulation results show that the FL technique outperforms INC by providing a faster dynamic response, smaller steady-state oscillations, and better real-time adaptability, while ANN offers intermediate performance. These findings indicate that the FL technique can significantly improve the tracking efficiency of PV under variable environmental conditions.