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Renewable energy systems have garnered significant global attention as sustainable and eco-friendly alternatives to conventional energy sources. Considering their accessibility and scalability, solar photovoltaic (PV) systems have become the most widely used technology among them. In the integration of RESs to the electric power utility grid, multilevel inverters (MLIs) play a crucial role in enhancing power quality and conversion efficiency. In this paper, a five-level solar PV-powered reduced switched-capacitor MLI is proposed and studied. In order to harvest maximum power, artificial neural network maximum power point tracking (ANN-MPPT) is used in this study and an optimal setting of ANN weights is employed using Scaled Conjugate Gradient (SCG) machine learning algorithm. The entire analysis is carried out using MATLAB/Simulink software.

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Analysis of Solar PV-Powered Reduced Component Switched-Capacitor-Based Multilevel Inverter

  • 章节

  • 发表日期：

    2025年

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  • J. Vijaychandra,
  • Yatindra Gopal,
  • K. Veda Prakash,
  • B. Vanajakshi,
  • Lukasz Knypinski,
  • D. Vijaya Kumar,
  • N. Sowjanya

摘要

Renewable energy systems have garnered significant global attention as sustainable and eco-friendly alternatives to conventional energy sources. Considering their accessibility and scalability, solar photovoltaic (PV) systems have become the most widely used technology among them. In the integration of RESs to the electric power utility grid, multilevel inverters (MLIs) play a crucial role in enhancing power quality and conversion efficiency. In this paper, a five-level solar PV-powered reduced switched-capacitor MLI is proposed and studied. In order to harvest maximum power, artificial neural network maximum power point tracking (ANN-MPPT) is used in this study and an optimal setting of ANN weights is employed using Scaled Conjugate Gradient (SCG) machine learning algorithm. The entire analysis is carried out using MATLAB/Simulink software.