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<p>This paper presents a novel Hybrid Fuzzy Logic Control System (HFLCS) for Doubly-Fed Induction Generator (DFIG)-based wind turbine power systems connected to the grid. The proposed HFLCS integrates fuzzy inference with conventional Proportional-Integral (PI) control to dynamically adapt controller parameters, addressing the nonlinearities and parameter sensitivities that limit conventional Field-Oriented Control (FOC) performance. Comprehensive simulation modeling using MATLAB/Simulink has been conducted to verify the effectiveness of the proposed approaches. The obtained results demonstrate that the HFLCS achieves significant performance improvements, including 40-50% faster recovery from grid disturbances, 30-40% reduction in power/voltage dips, and 75-80% improvement in reactive power regulation compared to conventional PI control. The system maintains robust performance under severe operating conditions, including parameter variations, voltage fluctuations, and extreme wind gusts. This research contributes an intelligent control framework that enhances wind energy harvesting efficiency, grid stability, and power quality for DFIG-based systems, with practical implications for modern wind farm operations and renewable energy integration.</p>

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Simulation Modeling and Evaluation of Intelligent Controller for DFIG Wind Power Systems Connected to Grid

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月21日

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  • Muawia Magzoub,
  • Hussein Shutari,
  • Nordin Saad,
  • Ladon Ahmed Bade,
  • Hakim Abdulrab

摘要

This paper presents a novel Hybrid Fuzzy Logic Control System (HFLCS) for Doubly-Fed Induction Generator (DFIG)-based wind turbine power systems connected to the grid. The proposed HFLCS integrates fuzzy inference with conventional Proportional-Integral (PI) control to dynamically adapt controller parameters, addressing the nonlinearities and parameter sensitivities that limit conventional Field-Oriented Control (FOC) performance. Comprehensive simulation modeling using MATLAB/Simulink has been conducted to verify the effectiveness of the proposed approaches. The obtained results demonstrate that the HFLCS achieves significant performance improvements, including 40-50% faster recovery from grid disturbances, 30-40% reduction in power/voltage dips, and 75-80% improvement in reactive power regulation compared to conventional PI control. The system maintains robust performance under severe operating conditions, including parameter variations, voltage fluctuations, and extreme wind gusts. This research contributes an intelligent control framework that enhances wind energy harvesting efficiency, grid stability, and power quality for DFIG-based systems, with practical implications for modern wind farm operations and renewable energy integration.