Artificial Neural Network Based Controller for A Buck Converter for Improving Efficiency

  • Unique Paper ID: 167089
  • Volume: 11
  • Issue: 3
  • PageNo: 355-361
  • Abstract:
  • This article proposes an artificial neural network (ANN) as a controller to improve the performance of converters used in photovoltaic (PV) systems. Buck converters play an important role in such systems by regulating the voltage of the PV array to meet the load or battery requirements. However, the effectiveness of traditional control methods may be limited due to changes in the environment and load requirements. To solve this challenge, ANN based controller is proposed to instantly correct the fault of the converter. ANN uses inputs such as solar irradiance level, temperature, battery voltage and load demand to optimize the conversion function for maximum power point tracking (MPPT) and good operation. Significant improvements in performance using the ability of neural networks to learn from data patterns and adapt to changes should be compared to normal control of layers. The performance of the proposed ANN-based controller has been verified by simulation and experimental results. These results show that this controller achieves better performance and better performance in different environments and data loads than traditional methods. The integration of the MPPT algorithm into the ANN framework further improves the converter's ability to extract maximum power from the PV array, increasing the overall efficiency. This research contributes to the state of the art of renewable energy technology by using artificial intelligence to optimize the performance of electricity from renewable energy sources, leading to efficient and reliable photovoltaic systems in practical applications.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 3
  • PageNo: 355-361

Artificial Neural Network Based Controller for A Buck Converter for Improving Efficiency

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