Scholarlink

<p>This paper presents a control framework for enhancing power quality and energy harvesting in hybrid photovoltaic (PV) and wind energy sources (RESs) using a shunt active power filter (SAPF). The proposed system integrates nonlinear control techniques with intelligent and predictive algorithms to address RESs ripples, harmonic distortion, and reactive power compensation. Hybrid maximum power point tracking (MPPT) methods are introduced, combining adaptive super-twisting sliding mode control (STSMC) and deep artificial neural networks (ANNs) for PV and wind energy optimization. For inverter switching and SAPF regulation, optimal relation-based model predictive control (ORB-MPC) and direct power model predictive control (DPMPC) strategies are employed. Simulation results demonstrate the effectiveness of the proposed approaches, achieving 99.9% and 99.3% MPPT efficiency for PV and wind systems and reducing the RESs current ripple to 2.09%, with a significant reduction in the total harmonic distortion (THD) of the source current to 1.01% was achieved, within IEEE-519 limits. The system exhibits dynamic response, minimal DC-link voltage ripple, and enhanced tracking convergence under varying meteorological and load conditions, outperforming conventional and model reference adaptive system (MRAS) methods.</p>

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Ripple reduction and power quality improvement in photovoltaic and wind integration using hybrid intelligent nonlinear control with a shunt active power filter

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Mustapha Meraouah,
  • Said Hassaine,
  • Faiza Kaddari,
  • Sandrine Moreau,
  • Youcef Mihoub,
  • Ahmed Benkhaled

摘要

This paper presents a control framework for enhancing power quality and energy harvesting in hybrid photovoltaic (PV) and wind energy sources (RESs) using a shunt active power filter (SAPF). The proposed system integrates nonlinear control techniques with intelligent and predictive algorithms to address RESs ripples, harmonic distortion, and reactive power compensation. Hybrid maximum power point tracking (MPPT) methods are introduced, combining adaptive super-twisting sliding mode control (STSMC) and deep artificial neural networks (ANNs) for PV and wind energy optimization. For inverter switching and SAPF regulation, optimal relation-based model predictive control (ORB-MPC) and direct power model predictive control (DPMPC) strategies are employed. Simulation results demonstrate the effectiveness of the proposed approaches, achieving 99.9% and 99.3% MPPT efficiency for PV and wind systems and reducing the RESs current ripple to 2.09%, with a significant reduction in the total harmonic distortion (THD) of the source current to 1.01% was achieved, within IEEE-519 limits. The system exhibits dynamic response, minimal DC-link voltage ripple, and enhanced tracking convergence under varying meteorological and load conditions, outperforming conventional and model reference adaptive system (MRAS) methods.