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Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation

Author

Listed:
  • Abhinandan Routray

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Yiza Srikanth Reddy

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Sung-ho Hur

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

Abstract

Predictive control is an advanced control technique that performs well in various application domains. In this work, linearised control design models are first derived in state-space form from the full nonlinear model of the 5 MW Supergen (Sustainable Power Generation and Supply) exemplar wind turbine. Feedback model predictive controllers (FB-MPCs) and feedforward model predictive controllers (FF-MPCs) are subsequently designed based on these linearised models. A neural network (NN)-based wind speed estimation method is then employed to obtain the accurate wind estimation required for designing a FF-MPC. This method uses a LiDAR to be shared between multiple wind turbines in a cluster, i.e., one turbine is mounted with a LiDAR, and each of the remaining turbines from the cluster is provided with a NN-based estimator, which replaces the LiDAR, making the approach more economically viable. The resulting controllers are tested by application to the full nonlinear model (based on which the linearised models are derived). Moreover, the mismatch between the control design model and the simulation model (model–plant mismatch) allows the robustness of the controllers’ design to be tested. Simulations are carried out at varying wind speeds to evaluate the robustness of the controllers by applying them to a full nonlinear 5 MW Matlab/SIMULINK model of the same exemplar Supergen wind turbine. Improved torque/speed plane tracking is achieved with a FF-MPC compared to a FB-MPC. Simulation results further demonstrate that the control performance is enhanced in both the time and frequency domains without increasing the wind turbine’s control activity; that is, the controller’s gain crossover frequency (or bandwidth) remains within the acceptable range, which is about 1 rad/s.

Suggested Citation

  • Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9697-:d:1173110
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    References listed on IDEAS

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    1. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
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    6. Bossoufi, Badre & Karim, Mohammed & Lagrioui, Ahmed & Taoussi, Mohammed & Derouich, Aziz, 2015. "Observer backstepping control of DFIG-Generators for wind turbines variable-speed: FPGA-based implementation," Renewable Energy, Elsevier, vol. 81(C), pages 903-917.
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    Cited by:

    1. Qianlong Zhu & Wenjing Xiong & Haijiao Wang & Xiaoqiang Jin, 2023. "Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data," Energies, MDPI, vol. 16(20), pages 1-17, October.
    2. Abhinandan Routray & Nitin Sivakumar & Sung-ho Hur & Deok-je Bang, 2023. "A Comparative Study of Optimal Individual Pitch Control Methods," Sustainability, MDPI, vol. 15(14), pages 1-25, July.

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