IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v147y2018icp812-825.html
   My bibliography  Save this article

Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis

Author

Listed:
  • Lin, Zhongwei
  • Chen, Zhenyu
  • Wu, Qiuwei
  • Yang, Shuo
  • Meng, Hongmin

Abstract

For the existing pitch and torque control of the wind turbine generator system (WTGS), further development on coordinated control is necessary to improve effectiveness for practical applications. In this paper, the WTGS is modeled as a coupling combination of two subsystems: the generator torque control subsystem and blade pitch control subsystem. Then, the pole positions in each control subsystem are adjusted coordinately to evaluate the controller participation and used as the objective of optimization. A two-level parameters-controllers coordinated optimization scheme is proposed and applied to optimize the controller coordination based on the Pareto optimization theory. Three solutions are obtained through optimization, which includes the optimal torque solution, optimal power solution, and satisfactory solution. Detailed comparisons evaluate the performance of the three selected solutions and provide the optimized controller coordination suggestions according to different requirements.

Suggested Citation

  • Lin, Zhongwei & Chen, Zhenyu & Wu, Qiuwei & Yang, Shuo & Meng, Hongmin, 2018. "Coordinated pitch & torque control of large-scale wind turbine based on Pareto efficiency analysis," Energy, Elsevier, vol. 147(C), pages 812-825.
    • Handle: RePEc:eee:energy:v:147:y:2018:i:c:p:812-825
    DOI: 10.1016/j.energy.2018.01.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218300665
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.01.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    2. Cui, Yunfei & Geng, Zhiqiang & Zhu, Qunxiong & Han, Yongming, 2017. "Review: Multi-objective optimization methods and application in energy saving," Energy, Elsevier, vol. 125(C), pages 681-704.
    3. Hassan, H.M. & ElShafei, A.L. & Farag, W.A. & Saad, M.S., 2012. "A robust LMI-based pitch controller for large wind turbines," Renewable Energy, Elsevier, vol. 44(C), pages 63-71.
    4. Pratama, Yoga Wienda & Purwanto, Widodo Wahyu & Tezuka, Tetsuo & McLellan, Benjamin Craig & Hartono, Djoni & Hidayatno, Akhmad & Daud, Yunus, 2017. "Multi-objective optimization of a multiregional electricity system in an archipelagic state: The role of renewable energy in energy system sustainability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 423-439.
    5. Odgaard, Peter Fogh & Larsen, Lars F.S. & Wisniewski, Rafael & Hovgaard, Tobias Gybel, 2016. "On using Pareto optimality to tune a linear model predictive controller for wind turbines," Renewable Energy, Elsevier, vol. 87(P2), pages 884-891.
    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. 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.
    2. Xie, Jingjie & Dong, Hongyang & Zhao, Xiaowei, 2023. "Data-driven torque and pitch control of wind turbines via reinforcement learning," Renewable Energy, Elsevier, vol. 215(C).
    3. Chen, Zhenyu & Lin, Zhongwei & Ren, Xin & Chen, Kaixuan & Zhang, Guangming & Xie, Zhen & Wang, Chuanxi & She, Chao, 2023. "Amplitude-optimized Koopman-linear flow estimator for wind turbine wake dynamics: Approximation, prediction and reconstruction," Energy, Elsevier, vol. 263(PE).
    4. Xu, Zifei & Mei, Xuan & Wang, Xinyu & Yue, Minnan & Jin, Jiangtao & Yang, Yang & Li, Chun, 2022. "Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors," Renewable Energy, Elsevier, vol. 182(C), pages 615-626.
    5. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
    6. 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).

    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. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
    2. Yuan, Yuan & Chen, Xu & Tang, J., 2020. "Multivariable robust blade pitch control design to reject periodic loads on wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 329-341.
    3. Yuan, Yuan & Tang, J., 2017. "Adaptive pitch control of wind turbine for load mitigation under structural uncertainties," Renewable Energy, Elsevier, vol. 105(C), pages 483-494.
    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. 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. Horasan, Muhammed Bilal & Kilic, Huseyin Selcuk, 2022. "A multi-objective decision-making model for renewable energy planning: The case of Turkey," Renewable Energy, Elsevier, vol. 193(C), pages 484-504.
    7. You, Junyu & Ampomah, William & Sun, Qian, 2020. "Co-optimizing water-alternating-carbon dioxide injection projects using a machine learning assisted computational framework," Applied Energy, Elsevier, vol. 279(C).
    8. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    9. Jia, Min & Zhang, Zhe & Zhang, Li & Zhao, Liang & Lu, Xinbo & Li, Linyan & Ruan, Jianhui & Wu, Yunlong & He, Zhuoming & Liu, Mei & Jiang, Lingling & Gao, Yajing & Wu, Pengcheng & Zhu, Shuying & Niu, M, 2024. "Optimization of electricity generation and assessment of provincial grid emission factors from 2020 to 2060 in China," Applied Energy, Elsevier, vol. 373(C).
    10. 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.
    11. Liu, Junbo & Cai, Chang & Song, Dongran & Zhong, Xiaohui & Shi, Kezhong & Chen, Yinpeng & Cheng, Shijie & Huang, Yupian & Jiang, Xue & Li, Qing'an, 2024. "Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model," Energy, Elsevier, vol. 311(C).
    12. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    13. Nour Eddine, A. & Chalet, D. & Faure, X. & Aixala, L. & Chessé, P., 2018. "Effect of engine exhaust gas pulsations on the performance of a thermoelectric generator for wasted heat recovery: An experimental and analytical investigation," Energy, Elsevier, vol. 162(C), pages 715-727.
    14. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    15. Karimi, Hamid & Jadid, Shahram, 2020. "Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework," Energy, Elsevier, vol. 195(C).
    16. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    17. Deng, Yuanwang & Liu, Huawei & Zhao, Xiaohuan & E, Jiaqiang & Chen, Jianmei, 2018. "Effects of cold start control strategy on cold start performance of the diesel engine based on a comprehensive preheat diesel engine model," Applied Energy, Elsevier, vol. 210(C), pages 279-287.
    18. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    19. Sarjiya, & Budi, Rizki Firmansyah Setya & Hadi, Sasongko Pramono, 2019. "Game theory for multi-objective and multi-period framework generation expansion planning in deregulated markets," Energy, Elsevier, vol. 174(C), pages 323-330.
    20. Tai Li & Yanbo Wang & Sunan Sun & Huimin Qian & Leqiu Wang & Lei Wang & Yanxia Shen & Zhicheng Ji, 2023. "Fuzzy Active Disturbance Rejection-Based Virtual Inertia Control Strategy for Wind Farms," Energies, MDPI, vol. 16(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:eee:energy:v:147:y:2018:i:c:p:812-825. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.