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Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems

Author

Listed:
  • Jau-Woei Perng

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, 70 Lienhai Road, Kaohsiung 80424, Taiwan)

  • Guan-Yan Chen

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, 70 Lienhai Road, Kaohsiung 80424, Taiwan)

  • Shan-Chang Hsieh

    (Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, 70 Lienhai Road, Kaohsiung 80424, Taiwan)

Abstract

A strategy was proposed to determine the optimal operating point for the proportional-integral-derivative (PID) controller of a wind turbine, and identify the stability regions in the parameter space. The proposed approach combined particle swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are artificial learning mechanisms that can determine the optimal operating points, and were used to generate the function representing the most favorable operating parameters from each parameter of for the stability region of the PID controller. A graphical method was used to determine the 2D or 3D vision boundaries of the PID-type controller space in closed-loop wind turbine systems. The proposed techniques were demonstrated using simulations of a drive train model without time delay and a pitch control model with time delay. Finally, the 3D stability boundaries were determined the proposed graphical approach with and without time delay systems.

Suggested Citation

  • Jau-Woei Perng & Guan-Yan Chen & Shan-Chang Hsieh, 2014. "Optimal PID Controller Design Based on PSO-RBFNN for Wind Turbine Systems," Energies, MDPI, vol. 7(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:1:p:191-209:d:31937
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    References listed on IDEAS

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    1. Yolanda Vidal & Leonardo Acho & Ningsu Luo & Mauricio Zapateiro & Francesc Pozo, 2012. "Power Control Design for Variable-Speed Wind Turbines," Energies, MDPI, vol. 5(8), pages 1-18, August.
    2. Asier Diaz De Corcuera & Aron Pujana-Arrese & Jose M. Ezquerra & Edurne Segurola & Joseba Landaluze, 2012. "H ∞ Based Control for Load Mitigation in Wind Turbines," Energies, MDPI, vol. 5(4), pages 1-30, April.
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    Cited by:

    1. Chan Roh, 2022. "Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators," Energies, MDPI, vol. 15(9), pages 1-18, April.
    2. Vincenzo Iannino & Valentina Colla & Mario Innocenti & Annamaria Signorini, 2017. "Design of a H ∞ Robust Controller with μ -Analysis for Steam Turbine Power Generation Applications," Energies, MDPI, vol. 10(7), pages 1-31, July.
    3. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    4. Yarong Zou & Wen Tan & Xingkang Jin & Zijian Wang, 2022. "An Active Disturbance Rejection Control of Large Wind Turbine Pitch Angle Based on Extremum-Seeking Algorithm," Energies, MDPI, vol. 15(8), pages 1-15, April.
    5. Muthana Alrifai & Mohamed Zribi & Mohamed Rayan, 2016. "Feedback Linearization Controller for a Wind Energy Power System," Energies, MDPI, vol. 9(10), pages 1-23, September.
    6. Md Rasel Sarkar & Sabariah Julai & Chong Wen Tong & Moslem Uddin & M.F. Romlie & GM Shafiullah, 2020. "Hybrid Pitch Angle Controller Approaches for Stable Wind Turbine Power under Variable Wind Speed," Energies, MDPI, vol. 13(14), pages 1-19, July.
    7. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    8. Jongmin Cheon & Jinwook Kim & Joohoon Lee & Kichang Lee & Youngkiu Choi, 2019. "Development of Hardware-in-the-Loop-Simulation Testbed for Pitch Control System Performance Test," Energies, MDPI, vol. 12(10), pages 1-20, May.
    9. Jinghan Cui & Su Liu & Jinfeng Liu & Xiangjie Liu, 2018. "A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems," Energies, MDPI, vol. 11(11), pages 1-23, November.
    10. Shrabani Sahu & Sasmita Behera, 2022. "A review on modern control applications in wind energy conversion system," Energy & Environment, , vol. 33(2), pages 223-262, March.
    11. Nguyen Gia Minh Thao & Kenko Uchida, 2018. "An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems," Energies, MDPI, vol. 11(3), pages 1-26, February.

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