IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i8p2846-d793156.html
   My bibliography  Save this article

An Active Disturbance Rejection Control of Large Wind Turbine Pitch Angle Based on Extremum-Seeking Algorithm

Author

Listed:
  • Yarong Zou

    (School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Changping District, Beijing 102206, China)

  • Wen Tan

    (School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Changping District, Beijing 102206, China)

  • Xingkang Jin

    (School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Changping District, Beijing 102206, China)

  • Zijian Wang

    (School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Changping District, Beijing 102206, China)

Abstract

This paper proposes the analysis and design of the linear active disturbance rejection controller (LADRC) for the pitch angle model of a large wind turbine generator (WTG). Since the transfer function of the pitch control system exhibits nonminimum-phase characteristics, the parameters of LADRC are difficult to tune using the conventional bandwidth method. On the basis of PI controller parameters to first-order LADRC parameters, an optimization problem is proposed in this paper to find the parameters of an LADRC for the pitch control system under the constraint of robustness measure, and the extremum-seeking (ES) algorithm is used to solve the problem. Simulation results show that LADRC can achieve better tracking and disturbance rejection performance than traditional PI control without loss of robustness against time delay.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2846-:d:793156
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/8/2846/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/8/2846/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    2. 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.
    3. 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.
    4. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2017. "Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections," Renewable Energy, Elsevier, vol. 101(C), pages 29-40.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    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. Xingqi Hu & Wen Tan & Guolian Hou, 2023. "PIDD2 Control of Large Wind Turbines’ Pitch Angle," Energies, MDPI, vol. 16(13), pages 1-22, July.
    2. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Wang, Zhiwen & Shen, Chen & Liu, Feng, 2018. "A conditional model of wind power forecast errors and its application in scenario generation," Applied Energy, Elsevier, vol. 212(C), pages 771-785.
    8. Aleksei Valentinovich Bogoviz & Svetlana Vladislavlevna Lobova & Yulia Vyacheslavovna Ragulina & Alexander Nikolaevich Alekseev, 2018. "Russia s Energy Security Doctrine: Addressing Emerging Challenges and Opportunities," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 1-6.
    9. Kumarasamy Palanimuthu & Ganesh Mayilsamy & Ameerkhan Abdul Basheer & Seong-Ryong Lee & Dongran Song & Young Hoon Joo, 2022. "A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems," Energies, MDPI, vol. 15(21), pages 1-27, November.
    10. Cui, Yang & Chen, Zhenghong & He, Yingjie & Xiong, Xiong & Li, Fen, 2023. "An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events," Energy, Elsevier, vol. 263(PC).
    11. 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.
    12. Wimhurst, Joshua J. & Greene, J. Scott, 2019. "Oklahoma's future wind energy resources and their relationship with the Central Plains low-level jet," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    13. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    14. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    15. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    16. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    17. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    18. Ke Yan & Yuting Dai & Meiling Xu & Yuchang Mo, 2019. "Tunnel Surface Settlement Forecasting with Ensemble Learning," Sustainability, MDPI, vol. 12(1), pages 1-11, December.
    19. Afef Fekih & Saleh Mobayen & Chih-Chiang Chen, 2021. "Adaptive Robust Fault-Tolerant Control Design for Wind Turbines Subject to Pitch Actuator Faults," Energies, MDPI, vol. 14(6), pages 1-13, March.
    20. Houjian Li & Xinya Huang & Deheng Zhou & Andi Cao & Mengying Su & Yufeng Wang & Lili Guo, 2022. "Forecasting Carbon Price in China: A Multimodel Comparison," IJERPH, MDPI, vol. 19(10), pages 1-16, May.

    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:8:p:2846-:d:793156. 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.