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Deep learning based buck-boost converter for PV modules.

Aoun Muhammad1, Asjad Amin2, Muhammad Ali Qureshi2

  • 1Dept. of Electrical Engineering, The Islamia University of Bahawalpur, Pakistan.

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Summary
This summary is machine-generated.

This study introduces a deep learning model to reduce voltage ripples in DC-DC buck-boost converters for photovoltaic systems. The model improves steady-state time and reduces overshoot compared to traditional controllers.

Keywords:
Buck-boost converterParameters for stabilityPhotovoltaics (PV)Proportional integral derivative (PID) controller

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Artificial Intelligence

Background:

  • DC-DC buck-boost converters are crucial for photovoltaic (PV) energy applications.
  • Undesired output voltage ripples are a significant challenge in these converters.
  • Existing solutions often lack effectiveness, especially for PV systems, and deep learning approaches are underutilized.

Purpose of the Study:

  • To propose a novel deep learning-based model for DC-DC buck-boost converters in PV applications.
  • To reduce steady-state time and achieve desired buck or boost modes.
  • To minimize output voltage ripples and improve overall converter performance.

Main Methods:

  • A deep learning model was developed and trained using data from a conventional PID controller.
  • The model was tested on an experimental setup with varying PV input voltages (10V to 48V).
  • The system utilized a single 12V battery for both off-grid and on-grid configurations in a single building.

Main Results:

  • The proposed deep learning model demonstrated superior performance compared to conventional PID controllers.
  • Significant reduction in steady-state time was observed.
  • Experimental validation confirmed improved performance with less overshoot in output voltage.

Conclusions:

  • The deep learning-based model effectively addresses voltage ripple issues in PV DC-DC buck-boost converters.
  • This approach offers improved efficiency and stability for renewable energy integration.
  • The model shows promise for enhancing the performance of standalone and grid-connected PV systems.