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<p>The effective integration of renewable energy sources into the electrical grid is essential to the shift to a sustainable energy system. However, smooth grid integration is hampered by issues like power transfer inefficiencies, harmonic distortion, and variability. This paper proposes a novel dual input <i>Z</i>-source indirect matrix converter (DIZIMC) coupled with an improved dynamic group cooperative search-based artificial neural network (IDGC-ANN) to address these limitations. The proposed DIZIMC with IDGC-ANN enhances energy conversion efficiency by minimizing switching losses, reducing harmonic distortion, and simplifying component design. The proposed system utilizes an ultra-sparse <i>Z</i>-source matrix converter (USZMC) to enhance energy conversion efficiency by reducing switching losses, harmonic distortion, and component complexity. The <i>Z</i>-source network plays a pivotal role in stabilizing the DC link voltage under fluctuating input conditions, enabling reliable operation across a wide range of RE scenarios. Simultaneously, the IDGC-ANN controller enhances overall system performance by dynamically adjusting control parameters in real time, ensuring optimal power conversion efficiency and grid compliance. This intelligent coordination is particularly valuable in real-world applications such as smart microgrids, off-grid hybrid energy systems, and grid-connected solar-wind farms, where variable generation and load demands require adaptive and resilient power management. The integration of an LCL filter minimizes grid harmonics, ensuring the delivery of clean power. Simulation results in MATLAB demonstrate significant improvements in total harmonic distortion (THD) with 0.5%, switching loss with 0.11%, conduction loss with 0.13%, and compared to existing methods. This intelligent control approach ensures the reliable, efficient, and stable integration of renewable energy into the grid.</p>

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Improve power transfer efficiency of renewable energy to grid using matrix converter

  • 期刊论文

  • 发表日期：

    2026年1月29日

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  • Akhilesh Kumar,
  • Pradip Kumar Sadhu,
  • Jay Singh,
  • Shiv Prakash Bihari

摘要

The effective integration of renewable energy sources into the electrical grid is essential to the shift to a sustainable energy system. However, smooth grid integration is hampered by issues like power transfer inefficiencies, harmonic distortion, and variability. This paper proposes a novel dual input Z-source indirect matrix converter (DIZIMC) coupled with an improved dynamic group cooperative search-based artificial neural network (IDGC-ANN) to address these limitations. The proposed DIZIMC with IDGC-ANN enhances energy conversion efficiency by minimizing switching losses, reducing harmonic distortion, and simplifying component design. The proposed system utilizes an ultra-sparse Z-source matrix converter (USZMC) to enhance energy conversion efficiency by reducing switching losses, harmonic distortion, and component complexity. The Z-source network plays a pivotal role in stabilizing the DC link voltage under fluctuating input conditions, enabling reliable operation across a wide range of RE scenarios. Simultaneously, the IDGC-ANN controller enhances overall system performance by dynamically adjusting control parameters in real time, ensuring optimal power conversion efficiency and grid compliance. This intelligent coordination is particularly valuable in real-world applications such as smart microgrids, off-grid hybrid energy systems, and grid-connected solar-wind farms, where variable generation and load demands require adaptive and resilient power management. The integration of an LCL filter minimizes grid harmonics, ensuring the delivery of clean power. Simulation results in MATLAB demonstrate significant improvements in total harmonic distortion (THD) with 0.5%, switching loss with 0.11%, conduction loss with 0.13%, and compared to existing methods. This intelligent control approach ensures the reliable, efficient, and stable integration of renewable energy into the grid.