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Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS

Author

Listed:
  • Abrar Ahmed Chhipa

    (College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313001, Rajasthan, India)

  • Vinod Kumar

    (College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313001, Rajasthan, India)

  • Raghuveer Raj Joshi

    (College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313001, Rajasthan, India)

  • Prasun Chakrabarti

    (Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India)

  • Michal Jasinski

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Alessandro Burgio

    (Independent Researcher, 87036 Rende, Italy)

  • Zbigniew Leonowicz

    (Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Elzbieta Jasinska

    (Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Rajkumar Soni

    (Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India)

  • Tulika Chakrabarti

    (Department of Basic Sciences, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India)

Abstract

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller’s output, which further inputs the rotor side converter’s speed control loop to control the rotor’s actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller’s performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller’s wide range of input wind speed.

Suggested Citation

  • Abrar Ahmed Chhipa & Vinod Kumar & Raghuveer Raj Joshi & Prasun Chakrabarti & Michal Jasinski & Alessandro Burgio & Zbigniew Leonowicz & Elzbieta Jasinska & Rajkumar Soni & Tulika Chakrabarti, 2021. "Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS," Energies, MDPI, vol. 14(19), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6275-:d:648656
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    References listed on IDEAS

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    Cited by:

    1. Abrar Ahmed Chhipą & Prąsun Chakrabarti & Vadim Bolshev & Tulika Chakrabarti & Gennady Samarin & Alexey N. Vasilyev & Sandeep Ghosh & Alexander Kudryavtsev, 2022. "Modeling and Control Strategy of Wind Energy Conversion System with Grid-Connected Doubly-Fed Induction Generator," Energies, MDPI, vol. 15(18), pages 1-26, September.
    2. Mourad Yessef & Badre Bossoufi & Mohammed Taoussi & Saad Motahhir & Ahmed Lagrioui & Hamid Chojaa & Sanghun Lee & Byeong-Gwon Kang & Mohamed Abouhawwash, 2022. "Improving the Maximum Power Extraction from Wind Turbines Using a Second-Generation CRONE Controller," Energies, MDPI, vol. 15(10), pages 1-23, May.
    3. Zbigniew Leonowicz & Michal Jasinski, 2022. "Machine Learning and Data Mining Applications in Power Systems," Energies, MDPI, vol. 15(5), pages 1-2, February.
    4. Erdal Bekiroglu & Muhammed Duran Yazar, 2022. "MPPT Control of Grid Connected DFIG at Variable Wind Speed," Energies, MDPI, vol. 15(9), pages 1-19, April.
    5. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.

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