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<p>This paper focuses on an effective control technique for enhancing the Maximum Power Point Tracking (MPPT) performance of a grid-connected DFIG-based wind power plant under continuously varying wind conditions. However, rapid fluctuations in wind speeds, uncertainties in parameters, and grid disturbances are key challenges to enhancing the MPPT performance capability. Considering these struggles, this study aims to model a modified dynamic DFIG-based wind turbine system and develop a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Proportional-Integral (PI) controller on the back-to-back converter at the rotor and grid sides. The actual limited ranges of wind speed and output power generation data of the Adama II wind power plant in Ethiopia are utilized as input and output variables for the ANFIS controller. The simulation results from the latest version of R2024a-MATLAB-Simulink software show that the proposed ANFIS-PI reached an MPPT of 2.22&#xa0;MW compared to the FLC-PI controller attained 2.2&#xa0;MW using the benchmark as the reference value of 1.561&#xa0;MW in the PI controller, by improving the maximum power coefficient of 0.5504 compared to 0.5473 using the baseline as the reference value of 0.4109, respectively, at a rated wind speed of 12.5&#xa0;m/s and an optimal pitch angle of 0°.</p>

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Enhancing MPPT performance of a grid-connected Doubly-Fed induction generator-based wind power plant using hybrid ANFIS-PI control strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月19日

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  • Likenesh Walle Biyazne,
  • Milkias Berhanu Tuka,
  • Yoseph Mekonnen Abebe,
  • Anatoli Wellhöfer

摘要

This paper focuses on an effective control technique for enhancing the Maximum Power Point Tracking (MPPT) performance of a grid-connected DFIG-based wind power plant under continuously varying wind conditions. However, rapid fluctuations in wind speeds, uncertainties in parameters, and grid disturbances are key challenges to enhancing the MPPT performance capability. Considering these struggles, this study aims to model a modified dynamic DFIG-based wind turbine system and develop a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Proportional-Integral (PI) controller on the back-to-back converter at the rotor and grid sides. The actual limited ranges of wind speed and output power generation data of the Adama II wind power plant in Ethiopia are utilized as input and output variables for the ANFIS controller. The simulation results from the latest version of R2024a-MATLAB-Simulink software show that the proposed ANFIS-PI reached an MPPT of 2.22 MW compared to the FLC-PI controller attained 2.2 MW using the benchmark as the reference value of 1.561 MW in the PI controller, by improving the maximum power coefficient of 0.5504 compared to 0.5473 using the baseline as the reference value of 0.4109, respectively, at a rated wind speed of 12.5 m/s and an optimal pitch angle of 0°.