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Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems.

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This study introduces a novel controller for photovoltaic (PV) systems to maximize energy harvesting. The generalized global sliding mode controller (GGSMC) ensures efficient power extraction despite changing environmental conditions.

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Control Theory

Background:

  • Growing energy demands and environmental concerns necessitate efficient renewable energy solutions.
  • Photovoltaic (PV) systems are crucial but their energy output fluctuates with environmental factors like temperature and solar radiation.
  • Effective Maximum Power Point Tracking (MPPT) is vital for optimizing energy yield from PV arrays under varying conditions.

Purpose of the Study:

  • To present a nonlinear generalized global sliding mode controller (GGSMC) for enhanced maximum power extraction from PV arrays.
  • To utilize a feed-forward neural network (FFNN) for generating optimal reference voltage under dynamic environmental conditions.
  • To validate the proposed GGSMC strategy's performance against a standard nonlinear backstepping controller.

Main Methods:

  • Design of a GGSMC controller integrated with a DC-DC buck-boost converter for PV energy harvesting.
  • Implementation of a feed-forward neural network (FFNN) to provide dynamic reference voltage tracking.
  • Modification of the sliding mode control to eliminate the reaching phase, ensuring continuous sliding mode operation.
  • Simulation analysis using MATLAB/Simulink to evaluate system response, accuracy, and tracking speed.

Main Results:

  • The proposed GGSMC strategy effectively tracks the FFNN-generated reference voltage under fluctuating temperature and sunlight.
  • The control system demonstrated no chattering or harmonic distortions in its response.
  • Simulation results confirmed the effectiveness, accuracy, and rapid tracking capabilities of the GGSMC.
  • Comparative analysis showed superior or comparable performance against the nonlinear backstepping controller under abrupt environmental changes.

Conclusions:

  • The developed GGSMC offers a robust and efficient solution for maximizing power output from PV systems.
  • The elimination of the reaching phase and absence of chattering contribute to a stable and reliable control system.
  • The FFNN-enhanced GGSMC is a promising strategy for optimizing PV energy generation in diverse environmental conditions.