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<p>Photovoltaic (PV) systems have shown growth in the world’s electrical matrix. However, the non-linear nature of PV arrays and their strong dependence on ambient conditions decrease the maximum power they can produce and, consequently, reduce their performance and commercial attractiveness. Maximum Power Point Tracking (MPPT) techniques have been studied over the years to minimize these problems. This research proposes new input variables for intelligent algorithms modeled for tracking the maximum power point (MPP) of a photovoltaic (PV) system. The intelligent algorithms modeled were artificial neural networks (ANN), fuzzy logic controllers (FLC), and adaptive-neuron fuzzy inference systems (ANFIS). The new input variables are the irradiance (environmental parameter) and output power of the photovoltaic array (electrical parameter). The output variable is duty-cycle (<i>D</i>) of the buck-boost converter. We compared the dynamic response and power generation of the modeled PV systems with a real 3&#xa0;kW PV plant controlled by a perturb and observe (P&amp;O) algorithm. The research method used modeling, simulation, and experiment data. The findings showed intelligent algorithms bettered the P&amp;O algorithm in tracking speed, tracking accuracy, and stability. The ANN algorithm was the MPPT algorithm with better performance, it managed to recover up to 12% power.</p>

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Evaluating the power generation and dynamic response of a photovoltaic installation using intelligent algorithms to control the maximum power point tracking

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Maria I. S. Guerra,
  • Fábio M. U. de Araújo,
  • Romênia G. Vieira

摘要

Photovoltaic (PV) systems have shown growth in the world’s electrical matrix. However, the non-linear nature of PV arrays and their strong dependence on ambient conditions decrease the maximum power they can produce and, consequently, reduce their performance and commercial attractiveness. Maximum Power Point Tracking (MPPT) techniques have been studied over the years to minimize these problems. This research proposes new input variables for intelligent algorithms modeled for tracking the maximum power point (MPP) of a photovoltaic (PV) system. The intelligent algorithms modeled were artificial neural networks (ANN), fuzzy logic controllers (FLC), and adaptive-neuron fuzzy inference systems (ANFIS). The new input variables are the irradiance (environmental parameter) and output power of the photovoltaic array (electrical parameter). The output variable is duty-cycle (D) of the buck-boost converter. We compared the dynamic response and power generation of the modeled PV systems with a real 3 kW PV plant controlled by a perturb and observe (P&O) algorithm. The research method used modeling, simulation, and experiment data. The findings showed intelligent algorithms bettered the P&O algorithm in tracking speed, tracking accuracy, and stability. The ANN algorithm was the MPPT algorithm with better performance, it managed to recover up to 12% power.