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<p>This work presents a detailed exploration of a hybrid renewable energy system that intricately combines a Photovoltaic (PV) array with a wind energy conversion system, each supported by novel power electronics and control methodologies. The PV system is integrated with a Trans Z-Source Quadratic Single-Ended Primary-Inductor Converter (SEPIC) with coupled inductor, which is specifically designed to address the challenges of low and fluctuating output voltages inherent in solar energy generation generating an efficiency of 97.25%. To maximize the energy harvested from the PV array, a Crayfish Optimized Recurrent Neural Fuzzy Inference System (CO-RNFIS) is employed as the Maximum Power Point Tracking (MPPT) technique with an efficiency of 99.3%. Furthermore, the system also includes a battery, which is connected through a bidirectional converter managed by an adaptive Proportional-Integral (PI) controller. Moreover, the designed system incorporates Direct-Quadrature (DQ) theory and Discrete Cosine Transform (DCT)–Discrete Orthogonal S-Transform (DOST) for harmonics mitigation and grid synchronization. Together, these techniques ensure high power quality by reducing harmonic distortion in the system with outcomes of 1.69%, 1.58% and 1.29%. The validated outcome using MATLAB simulations demonstrates the superiority of proposed system by showing that it performs better than existing solutions in terms of key metrics such as energy harvesting efficiency, stability of DC output, and overall system performance.</p>

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Integrated Control of Hybrid PV-Wind Energy Systems Using Crayfish-Optimized Neuro-Fuzzy Inference and a Trans Z-Source QSEPIC Converter

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • N. Sridhar,
  • K. Sathish Kumar

摘要

This work presents a detailed exploration of a hybrid renewable energy system that intricately combines a Photovoltaic (PV) array with a wind energy conversion system, each supported by novel power electronics and control methodologies. The PV system is integrated with a Trans Z-Source Quadratic Single-Ended Primary-Inductor Converter (SEPIC) with coupled inductor, which is specifically designed to address the challenges of low and fluctuating output voltages inherent in solar energy generation generating an efficiency of 97.25%. To maximize the energy harvested from the PV array, a Crayfish Optimized Recurrent Neural Fuzzy Inference System (CO-RNFIS) is employed as the Maximum Power Point Tracking (MPPT) technique with an efficiency of 99.3%. Furthermore, the system also includes a battery, which is connected through a bidirectional converter managed by an adaptive Proportional-Integral (PI) controller. Moreover, the designed system incorporates Direct-Quadrature (DQ) theory and Discrete Cosine Transform (DCT)–Discrete Orthogonal S-Transform (DOST) for harmonics mitigation and grid synchronization. Together, these techniques ensure high power quality by reducing harmonic distortion in the system with outcomes of 1.69%, 1.58% and 1.29%. The validated outcome using MATLAB simulations demonstrates the superiority of proposed system by showing that it performs better than existing solutions in terms of key metrics such as energy harvesting efficiency, stability of DC output, and overall system performance.