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Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning

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  • J. Enrique Sierra-García
  • Matilde Santos

Abstract

In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. A performance analysis of this neurocontrol strategy is carried out, showing the influence of the RBF parameters, wind speed, learning parameters, and control period, on the system response. The neurocontroller has been compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.

Suggested Citation

  • J. Enrique Sierra-García & Matilde Santos, 2020. "Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning," Complexity, Hindawi, vol. 2020, pages 1-15, September.
    • Handle: RePEc:hin:complx:4681767
    DOI: 10.1155/2020/4681767
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    Cited by:

    1. Pablo Zambrana & Javier Fernandez-Quijano & J. Jesus Fernandez-Lozano & Pedro M. Mayorga Rubio & Alfonso J. Garcia-Cerezo, 2021. "Improving the Performance of Controllers for Wind Turbines on Semi-Submersible Offshore Platforms: Fuzzy Supervisor Control," Energies, MDPI, vol. 14(19), pages 1-17, September.
    2. Daniel Villoslada & Matilde Santos & María Tomás-Rodríguez, 2021. "General Methodology for the Identification of Reduced Dynamic Models of Barge-Type Floating Wind Turbines," Energies, MDPI, vol. 14(13), pages 1-16, June.
    3. Antonio Galán-Lavado & Matilde Santos, 2021. "Analysis of the Effects of the Location of Passive Control Devices on the Platform of a Floating Wind Turbine," Energies, MDPI, vol. 14(10), pages 1-19, May.
    4. Jesús Enrique Sierra-García & Matilde Santos, 2021. "Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control," Sustainability, MDPI, vol. 13(6), pages 1-17, March.

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