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This study introduces advanced Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) controllers for photovoltaic (PV) systems. These intelligent maximum power point tracking (MPPT) methods significantly enhance energy efficiency and reliability, especially under partial shading conditions (PSC).

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Engineering
  • Power Electronics

Background:

  • Photovoltaic (PV) systems face challenges with nonlinear power-voltage characteristics, particularly under partial shading conditions (PSC).
  • These issues lead to reduced energy efficiency and tracking accuracy in conventional maximum power point tracking (MPPT) systems.
  • Existing methods like perturb-and-observe (P&O) suffer from steady-state oscillations and slow dynamic responses.

Purpose of the Study:

  • To develop and evaluate novel MPPT controllers for PV systems to overcome limitations caused by PSC.
  • To improve energy efficiency, tracking accuracy, and dynamic response compared to traditional methods.
  • To ensure reliable and efficient power extraction under rapidly changing environmental conditions.

Main Methods:

  • Proposed two improved MPPT controllers utilizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques.
  • Implemented a predictive, non-iterative control architecture using power-voltage and voltage time derivatives as input features.
  • Simulated and compared the performance of the proposed controllers against the conventional P&O algorithm and other AI-based MPPT approaches.

Main Results:

  • The ANN and ANFIS controllers achieved high average tracking efficiencies of 99.4% and 99.75%, respectively.
  • Demonstrated a 55% reduction in response time and over 70% suppression of steady-state oscillations compared to P&O.
  • The ANFIS controller showed superior stability, reducing duty-cycle fluctuations by 20% compared to ANN, with execution times under 0.2 ms.

Conclusions:

  • The proposed intelligent MPPT framework, particularly the ANFIS controller, offers a fast, accurate, and computationally efficient solution for PV systems.
  • These advanced controllers significantly improve reliability and energy yield, especially under dynamic and partial shading conditions.
  • The low computational complexity makes the controllers suitable for real-time deployment on low-cost digital signal processors (DSPs).