Scholarlink

<p>This work focuses on the optimization and design of HRES combining Photovoltaic (PV), battery storage and wind energy to deliver quality power to meet demands of modern applications. The solar PV system is incorporated with a Soft Clamped Quadratic Single Ended Primary Inductor Converter (SC-QSEPIC), which boosts the minimal DC outcome from the panels to a desired voltage level with maximum efficiency. To harvest the most power from a solar energy system, a Horned Lizard Optimized Radial Bias Function Neural Network (HLO-RBFNN) Maximum Power Point Tracking (MPPT) is adopted. This intelligent approach ensures faster convergence to optimum operating point with minimal oscillations. The wind energy system utilizes a Doubly Fed Induction Generator (DFIG) to convert wind energy into electrical power. For energy storage, a bidirectional DC-DC converter accomplishes the discharging and charging of the battery, ensuring efficient energy flow during periods of excess generation or load demand. This process is also controlled by a PI controller to maintain optimal battery performance. The combined DC output from the solar, wind, and battery systems is fed into a 3-phase Voltage Source Inverter (VSI), which converts the DC power into high-quality AC power for the load. The Hybrid Empirical Decomposition with Wavelet Transform (HED-WT) approach is utilized to extract clean and accurate reference current signals by eliminating harmonics, providing precise control for grid. The entire hybrid renewable energy architecture, including the converter models, control algorithms, and neural network-based MPPT, is developed and simulated within MATLAB to evaluate performance under various dynamic conditions. This simulation-based validation demonstrates the effectiveness and feasibility of designed approach before potential real-time implementation. Thereby the research addresses the technical challenges of renewable energy integration and also emphasizes the importance of optimization techniques for enhancing efficiency (96.31%) and stability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Grid-Connected Hybrid PV-Wind System Using SC-QSEPIC Converter and HED-WT Based Reference Current Generation

  • 期刊论文

  • 发表日期：

    2025年12月8日

查看全文
  • Shekaina Justin

摘要

This work focuses on the optimization and design of HRES combining Photovoltaic (PV), battery storage and wind energy to deliver quality power to meet demands of modern applications. The solar PV system is incorporated with a Soft Clamped Quadratic Single Ended Primary Inductor Converter (SC-QSEPIC), which boosts the minimal DC outcome from the panels to a desired voltage level with maximum efficiency. To harvest the most power from a solar energy system, a Horned Lizard Optimized Radial Bias Function Neural Network (HLO-RBFNN) Maximum Power Point Tracking (MPPT) is adopted. This intelligent approach ensures faster convergence to optimum operating point with minimal oscillations. The wind energy system utilizes a Doubly Fed Induction Generator (DFIG) to convert wind energy into electrical power. For energy storage, a bidirectional DC-DC converter accomplishes the discharging and charging of the battery, ensuring efficient energy flow during periods of excess generation or load demand. This process is also controlled by a PI controller to maintain optimal battery performance. The combined DC output from the solar, wind, and battery systems is fed into a 3-phase Voltage Source Inverter (VSI), which converts the DC power into high-quality AC power for the load. The Hybrid Empirical Decomposition with Wavelet Transform (HED-WT) approach is utilized to extract clean and accurate reference current signals by eliminating harmonics, providing precise control for grid. The entire hybrid renewable energy architecture, including the converter models, control algorithms, and neural network-based MPPT, is developed and simulated within MATLAB to evaluate performance under various dynamic conditions. This simulation-based validation demonstrates the effectiveness and feasibility of designed approach before potential real-time implementation. Thereby the research addresses the technical challenges of renewable energy integration and also emphasizes the importance of optimization techniques for enhancing efficiency (96.31%) and stability.