IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3136-d801888.html
   My bibliography  Save this article

Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators

Author

Listed:
  • Chan Roh

    (Department of Energy Engineering, In-Je University, 197 Inje-ro, Gimhae-si 50834, Korea)

Abstract

The pitch controller of a floating offshore wind power system has an important influence on the power generation and movement of the floating body. It drives the turbine blade pitch using a hydraulic actuator, whose inherent characteristics cause a delay in response, which increases with the system capacity. As a result, the power generation is reduced, and the pitch motion of the floating body is increased. This paper proposes an advanced pitch controller designed to compensate for the delay in the hydraulic actuator response. The proposed pitch controller applies an artificial-intelligence-based deep learning algorithm to predict the delay time in the hydraulic actuator. This delay is compensated for by preferentially predicting the blade pitch control angle even if a delay occurs in the hydraulic actuator. The performance of the proposed pitch controller was analyzed using the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) v8 model developed by the US National Renewable Energy Laboratory and was compared against that of the ideal pitch controller and the pitch controller that reflects the response delay. Compared with the latter, the proposed method increased the average power generation by approximately 5% and reduced the standard deviation of the floating body’s pitch motion by approximately 50%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3136-:d:801888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3136/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/9/3136/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luis Arturo Soriano & Wen Yu & Jose de Jesus Rubio, 2013. "Modeling and Control of Wind Turbine," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, August.
    2. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    3. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    4. Yun-Su Kim & Il-Yop Chung & Seung-Il Moon, 2015. "Tuning of the PI Controller Parameters of a PMSG Wind Turbine to Improve Control Performance under Various Wind Speeds," Energies, MDPI, vol. 8(2), pages 1-20, February.
    5. 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.
    6. Boukhezzar, B. & Lupu, L. & Siguerdidjane, H. & Hand, M., 2007. "Multivariable control strategy for variable speed, variable pitch wind turbines," Renewable Energy, Elsevier, vol. 32(8), pages 1273-1287.
    7. Hu, Jianming & Wang, Jianzhou & Zeng, Guowei, 2013. "A hybrid forecasting approach applied to wind speed time series," Renewable Energy, Elsevier, vol. 60(C), pages 185-194.
    8. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    9. Cross, Philip & Ma, Xiandong, 2014. "Nonlinear system identification for model-based condition monitoring of wind turbines," Renewable Energy, Elsevier, vol. 71(C), pages 166-175.
    10. Moodi, Hoda & Bustan, Danyal, 2019. "Wind turbine control using T-S systems with nonlinear consequent parts," Energy, Elsevier, vol. 172(C), pages 922-931.
    11. Lei Wang & Shan Zuo & Y. D. Song & Zheng Zhou, 2014. "Variable Torque Control of Offshore Wind Turbine on Spar Floating Platform Using Advanced RBF Neural Network," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-7, March.
    12. Zi Lin & Xiaolei Liu, 2020. "Assessment of Wind Turbine Aero-Hydro-Servo-Elastic Modelling on the Effects of Mooring Line Tension via Deep Learning," Energies, MDPI, vol. 13(9), pages 1-21, May.
    13. 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.
    14. 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.
    15. Yin, Xiu-xing & Lin, Yong-gang & Li, Wei & Gu, Ya-jing & Wang, Xiao-jun & Lei, Peng-fei, 2015. "Design, modeling and implementation of a novel pitch angle control system for wind turbine," Renewable Energy, Elsevier, vol. 81(C), pages 599-608.
    16. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    17. Duong, Minh Quan & Grimaccia, Francesco & Leva, Sonia & Mussetta, Marco & Ogliari, Emanuele, 2014. "Pitch angle control using hybrid controller for all operating regions of SCIG wind turbine system," Renewable Energy, Elsevier, vol. 70(C), pages 197-203.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Azizi, Askar & Nourisola, Hamid & Shoja-Majidabad, Sajjad, 2019. "Fault tolerant control of wind turbines with an adaptive output feedback sliding mode controller," Renewable Energy, Elsevier, vol. 135(C), pages 55-65.
    4. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    5. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    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. Xingqi Hu & Wen Tan & Guolian Hou, 2023. "PIDD2 Control of Large Wind Turbines’ Pitch Angle," Energies, MDPI, vol. 16(13), pages 1-22, July.
    8. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    9. Meysam Yousefzadeh & Shahin Hedayati Kia & Mohammad Hoseintabar Marzebali & Davood Arab Khaburi & Hubert Razik, 2022. "Power-Hardware-in-the-Loop for Stator Windings Asymmetry Fault Analysis in Direct-Drive PMSG-Based Wind Turbines," Energies, MDPI, vol. 15(19), pages 1-17, September.
    10. Camelo, Henrique do Nascimento & Lucio, Paulo Sérgio & Leal Junior, João Bosco Verçosa & Carvalho, Paulo Cesar Marques de & Santos, Daniel von Glehn dos, 2018. "Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks," Energy, Elsevier, vol. 151(C), pages 347-357.
    11. Yanwei Jing & Hexu Sun & Lei Zhang & Tieling Zhang, 2017. "Variable Speed Control of Wind Turbines Based on the Quasi-Continuous High-Order Sliding Mode Method," Energies, MDPI, vol. 10(10), pages 1-21, October.
    12. 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.
    13. 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.
    14. 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.
    15. Golnary, Farshad & Moradi, Hamed, 2022. "Identification of the dynamics of the drivetrain and estimating its unknown parts in a large scale wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 50-69.
    16. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.
    17. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    18. 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.
    19. Wang, Feng & Chen, Jincheng & Xu, Bing & Stelson, Kim A., 2019. "Improving the reliability and energy production of large wind turbine with a digital hydrostatic drivetrain," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    20. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3136-:d:801888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.