IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5029-d418706.html
   My bibliography  Save this article

Real-Time Flow Control of Blade Section Using a Hydraulic Transmission System Based on an H-Inf Controller with LMI Design

Author

Listed:
  • Tingrui Liu

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Kang Zhao

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Changle Sun

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jiahao Jia

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Guifang Liu

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

Vibration and real-time flow control of the 2D blade section of wind turbines with three degrees of freedom (3-DOF), excited by external pitch motion, are investigated based on an H-inf (H ∞ ) controller using linear-matrix-inequality (HIC/LMI) design. The real-time flow control for the purpose of aeroelastic flutter suppression includes not only the driving process of real-time physical equipment, but also the realization of real-time control algorithm in the physical controller. The aeroelastic system combined with pitch motion is controlled by a kind of HIC/LMI algorithm. The real-time external pitch motion is driven by rack-piston cylinder (RPC) using a hydraulic transmission system (HTS). The unsteady aerodynamic loads model is simplified by the HTS system. The HTS is actuated by a proportional-flow valve (PFV) which is controlled by another HIC/LMI algorithm, a novel algorithm for waveform tracking. According to the result of waveform tracking, the input current signal of PFV is realized by the configuration of the controller hardware system and its external circuits. In two types of HIC/LMI algorithms, controller stabilities are affirmed using Lyapunov analyses, and controller values are derived and obtained by using LMI designs. Flutter suppression for divergent and instable displacements is shown, with obvious controlled effects illustrated. An online monitoring experimental platform using hardware-in-the-loop simulation, based on Siemens S7-200 programmable logic controller (PLC) hardware and Kingview detection system, is built to implement pitch motion based on HTS and configure the signal input of PFV in pitch control.

Suggested Citation

  • Tingrui Liu & Kang Zhao & Changle Sun & Jiahao Jia & Guifang Liu, 2020. "Real-Time Flow Control of Blade Section Using a Hydraulic Transmission System Based on an H-Inf Controller with LMI Design," Energies, MDPI, vol. 13(19), pages 1-16, September.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5029-:d:418706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5029/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/19/5029/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
    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. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    2. Janusz Baran & Andrzej Jąderko, 2020. "An MPPT Control of a PMSG-Based WECS with Disturbance Compensation and Wind Speed Estimation," Energies, MDPI, vol. 13(23), pages 1-20, December.
    3. 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.
    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. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2019. "Tube-based explicit model predictive output-feedback controller for collective pitching of wind turbines," Renewable Energy, Elsevier, vol. 131(C), pages 549-562.
    6. Masood, Nahid-Al- & Mahmud, Sajjad Uddin & Ansary, Md Nazmuddoha & Deeba, Shohana Rahman, 2022. "Improvement of system strength under high wind penetration: A techno-economic assessment using synchronous condenser and SVC," Energy, Elsevier, vol. 246(C).
    7. Longfu Luo & Xiaofeng Zhang & Dongran Song & Weiyi Tang & Jian Yang & Li Li & Xiaoyu Tian & Wu Wen, 2018. "Optimal Design of Rated Wind Speed and Rotor Radius to Minimizing the Cost of Energy for Offshore Wind Turbines," Energies, MDPI, vol. 11(10), pages 1-17, October.
    8. Hui, Jiuwu & Yuan, Jingqi, 2022. "Load following control of a pressurized water reactor via finite-time super-twisting sliding mode and extended state observer techniques," Energy, Elsevier, vol. 241(C).
    9. Wakui, Tetsuya & Yoshimura, Motoki & Yokoyama, Ryohei, 2017. "Multiple-feedback control of power output and platform pitching motion for a floating offshore wind turbine-generator system," Energy, Elsevier, vol. 141(C), pages 563-578.
    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. Pires, Thiago S. & Cruz, Manuel E. & Colaço, Marcelo J. & Alves, Marco A.C., 2018. "Application of nonlinear multivariable model predictive control to transient operation of a gas turbine and NOX emissions reduction," Energy, Elsevier, vol. 149(C), pages 341-353.
    12. Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    13. Yao Liu & Lin Guan & Fang Guo & Jianping Zheng & Jianfu Chen & Chao Liu & Josep M. Guerrero, 2019. "A Reactive Power-Voltage Control Strategy of an AC Microgrid Based on Adaptive Virtual Impedance," Energies, MDPI, vol. 12(16), pages 1-15, August.
    14. Lei, Hang & Su, Jie & Bao, Yan & Chen, Yaoran & Han, Zhaolong & Zhou, Dai, 2019. "Investigation of wake characteristics for the offshore floating vertical axis wind turbines in pitch and surge motions of platforms," Energy, Elsevier, vol. 166(C), pages 471-489.
    15. Li, Qing’an & Xu, Jianzhong & Kamada, Yasunari & Takao, Maeda & Nishimura, Shogo & Wu, Guangxing & Cai, Chang, 2020. "Experimental investigations of airfoil surface flow of a horizontal axis wind turbine with LDV measurements," Energy, Elsevier, vol. 191(C).
    16. Kaman Thapa Magar & Mark Balas & Susan Frost & Nailu Li, 2017. "Adaptive State Feedback—Theory and Application for Wind Turbine Control," Energies, MDPI, vol. 10(12), pages 1-15, December.
    17. Song, Dongran & Yang, Jian & Su, Mei & Liu, Anfeng & Cai, Zili & Liu, Yao & Joo, Young Hoon, 2017. "A novel wind speed estimator-integrated pitch control method for wind turbines with global-power regulation," Energy, Elsevier, vol. 138(C), pages 816-830.
    18. Gianluca Pepe & Federica Mezzani & Antonio Carcaterra & Luca Cedola & Franco Rispoli, 2020. "Variational Control Approach to Energy Extraction from a Fluid Flow," Energies, MDPI, vol. 13(18), pages 1-20, September.
    19. Jingrong Yu & Limin Deng & Dongran Song & Maolin Pei, 2019. "Wide Bandwidth Control for Multi-Parallel Grid-Connected Inverters with Harmonic Compensation," Energies, MDPI, vol. 12(3), pages 1-22, February.
    20. Mi Dong & Xiaoyu Tian & Li Li & Dongran Song & Lina Wang & Miao Zhao, 2018. "Model-Based Current Sharing Approach for DCM Interleaved Flyback Micro-Inverter," Energies, MDPI, vol. 11(7), pages 1-21, June.

    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:13:y:2020:i:19:p:5029-:d:418706. 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.