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<p>This paper proposes a novel hybrid control strategy for the Rotor-Side Converter (RSC) of Doubly-Fed Induction Generators (DFIGs) in wind energy systems operating under variable wind conditions. The proposed approach integrates Single-Input Interval Type-2 Fuzzy Logic Control (SI-IT2-FLC) with Super-Twisting Sliding Mode Control (ST-SMC) to enhance tracking accuracy, mitigate chattering, and improve robustness against external disturbances. Additionally, an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) algorithm optimizes energy extraction by adapting to wind speed fluctuations. A rigorous Lyapunov stability analysis guarantees global finite-time stability and resilience to system uncertainties. Comparative simulations against conventional ST-SMC demonstrate that the proposed hybrid controller achieves a 35% reduction in steady-state error, a 45% improvement in disturbance rejection, and enhanced energy efficiency with minimal chattering effects. These results establish the superiority of the SI-IT2-FLC-ST-SMC approach in ensuring stable, efficient, and resilient wind energy conversion. Future work will focus on real-time implementation and adaptive tuning mechanisms for further performance enhancement.</p>

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Hybrid ANN-Based MPPT Control for DFIG Wind Systems Using Type-2 Fuzzy Logic and Super-Twisting Sliding Mode Control

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Godspower S. Bruno,
  • Diaa-Eldin A. Mansour,
  • Ayman Nada,
  • Tamer F. Megahed

摘要

This paper proposes a novel hybrid control strategy for the Rotor-Side Converter (RSC) of Doubly-Fed Induction Generators (DFIGs) in wind energy systems operating under variable wind conditions. The proposed approach integrates Single-Input Interval Type-2 Fuzzy Logic Control (SI-IT2-FLC) with Super-Twisting Sliding Mode Control (ST-SMC) to enhance tracking accuracy, mitigate chattering, and improve robustness against external disturbances. Additionally, an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) algorithm optimizes energy extraction by adapting to wind speed fluctuations. A rigorous Lyapunov stability analysis guarantees global finite-time stability and resilience to system uncertainties. Comparative simulations against conventional ST-SMC demonstrate that the proposed hybrid controller achieves a 35% reduction in steady-state error, a 45% improvement in disturbance rejection, and enhanced energy efficiency with minimal chattering effects. These results establish the superiority of the SI-IT2-FLC-ST-SMC approach in ensuring stable, efficient, and resilient wind energy conversion. Future work will focus on real-time implementation and adaptive tuning mechanisms for further performance enhancement.