IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v87y2016ip1p646-655.html
   My bibliography  Save this article

Hydro-viscous transmission based maximum power extraction control for continuously variable speed wind turbine with enhanced efficiency

Author

Listed:
  • Yin, Xiu-xing
  • Lin, Yong-gang
  • Li, Wei
  • Gu, Hai-gang

Abstract

The hydro-viscous transmission is potentially attractive for large scale wind turbines and is capable of significantly improving the turbine controllability and reliability due to the direct drive-train speed control. The hydro-viscous transmission based maximum power extraction control is presented in this paper. The system design, basic dynamic characteristics, thermal losses and management are presented and thoroughly analysed. The system model and the maximum power extraction control loop are also presented and designed. By using the designed control loop and controlling the hydro-viscous transmission, the turbine rotating speed can be continuously adapted to the incoming wind speed to maintain the optimum power points. Simulation results of a 2.0 MW wind turbine demonstrate that the hydro-viscous transmission based wind turbine has increased power capture due to the continuously variable speed feature as compared to a variable speed wind turbine with merely converter control. The hydro-viscous transmission works well enough and has an enhanced overall efficiency than the conventional system with fixed gear ratio.

Suggested Citation

  • Yin, Xiu-xing & Lin, Yong-gang & Li, Wei & Gu, Hai-gang, 2016. "Hydro-viscous transmission based maximum power extraction control for continuously variable speed wind turbine with enhanced efficiency," Renewable Energy, Elsevier, vol. 87(P1), pages 646-655.
  • Handle: RePEc:eee:renene:v:87:y:2016:i:p1:p:646-655
    DOI: 10.1016/j.renene.2015.10.032
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115303852
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2015.10.032?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. Hall, John F. & Chen, Dongmei, 2012. "Performance of a 100 kW wind turbine with a Variable Ratio Gearbox," Renewable Energy, Elsevier, vol. 44(C), pages 261-266.
    2. Yin, Xiu-xing & Lin, Yong-gang & Li, Wei & Liu, Hong-wei & Gu, Ya-jing, 2014. "Output power control for hydro-viscous transmission based continuously variable speed wind turbine," Renewable Energy, Elsevier, vol. 72(C), pages 395-405.
    3. Fernandez, L.M. & Garcia, C.A. & Jurado, F., 2008. "Comparative study on the performance of control systems for doubly fed induction generator (DFIG) wind turbines operating with power regulation," Energy, Elsevier, vol. 33(9), pages 1438-1452.
    4. González, L.G. & Figueres, E. & Garcerá, G. & Carranza, O., 2010. "Maximum-power-point tracking with reduced mechanical stress applied to wind-energy-conversion-systems," Applied Energy, Elsevier, vol. 87(7), pages 2304-2312, July.
    5. Kot, R. & Rolak, M. & Malinowski, M., 2013. "Comparison of maximum peak power tracking algorithms for a small wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 29-40.
    6. Petković, Dalibor & Ćojbašić, Žarko & Nikolić, Vlastimir & Shamshirband, Shahaboddin & Mat Kiah, Miss Laiha & Anuar, Nor Badrul & Abdul Wahab, Ainuddin Wahid, 2014. "Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission," Energy, Elsevier, vol. 64(C), pages 868-874.
    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. Govind, Bala, 2017. "Increasing the operational capability of a horizontal axis wind turbine by its integration with a vertical axis wind turbine," Applied Energy, Elsevier, vol. 199(C), pages 479-494.
    2. Roggenburg, Michael & Esquivel-Puentes, Helber A. & Vacca, Andrea & Bocanegra Evans, Humberto & Garcia-Bravo, Jose M. & Warsinger, David M. & Ivantysynova, Monika & Castillo, Luciano, 2020. "Techno-economic analysis of a hydraulic transmission for floating offshore wind turbines," Renewable Energy, Elsevier, vol. 153(C), pages 1194-1204.
    3. López-Queija, Javier & Robles, Eider & Jugo, Josu & Alonso-Quesada, Santiago, 2022. "Review of control technologies for floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    4. Kim, Joon-Hyung & Cho, Bo-Min & Kim, Sung & Kim, Jin-Woo & Suh, Jun-Won & Choi, Young-Seok & Kanemoto, Toshiaki & Kim, Jin-Hyuk, 2017. "Design technique to improve the energy efficiency of a counter-rotating type pump-turbine," Renewable Energy, Elsevier, vol. 101(C), pages 647-659.
    5. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(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. Phan, Dinh-Chung & Yamamoto, Shigeru, 2016. "Rotor speed control of doubly fed induction generator wind turbines using adaptive maximum power point tracking," Energy, Elsevier, vol. 111(C), pages 377-388.
    2. Derafshian, Mehdi & Amjady, Nima, 2015. "Optimal design of power system stabilizer for power systems including doubly fed induction generator wind turbines," Energy, Elsevier, vol. 84(C), pages 1-14.
    3. Francesco Bottiglione & Giacomo Mantriota & Marco Valle, 2018. "Power-Split Hydrostatic Transmissions for Wind Energy Systems," Energies, MDPI, vol. 11(12), pages 1-15, December.
    4. Ganjefar, Soheil & Ghasemi, Ali Akbar, 2014. "A novel-strategy controller design for maximum power extraction in stand-alone windmill systems," Energy, Elsevier, vol. 76(C), pages 326-335.
    5. Yang, Jian & Song, Dongran & Dong, Mi & Chen, Sifan & Zou, Libing & Guerrero, Josep M., 2016. "Comparative studies on control systems for a two-blade variable-speed wind turbine with a speed exclusion zone," Energy, Elsevier, vol. 109(C), pages 294-309.
    6. Ganjefar, Soheil & Mohammadi, Ali, 2016. "Variable speed wind turbines with maximum power extraction using singular perturbation theory," Energy, Elsevier, vol. 106(C), pages 510-519.
    7. 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.
    8. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    9. Alizadeh, Mojtaba & Kojori, Shokrollah Shokri, 2015. "Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller," Energy, Elsevier, vol. 91(C), pages 610-629.
    10. Govind, Bala, 2017. "Increasing the operational capability of a horizontal axis wind turbine by its integration with a vertical axis wind turbine," Applied Energy, Elsevier, vol. 199(C), pages 479-494.
    11. Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "On-line designed hybrid controller with adaptive observer for variable-speed wind generation system," Energy, Elsevier, vol. 35(7), pages 3022-3030.
    12. Dixon, Christopher & Reynolds, Steve & Rodley, David, 2016. "Micro/small wind turbine power control for electrolysis applications," Renewable Energy, Elsevier, vol. 87(P1), pages 182-192.
    13. Hachicha, Fatma & Krichen, Lotfi, 2012. "Rotor power control in doubly fed induction generator wind turbine under grid faults," Energy, Elsevier, vol. 44(1), pages 853-861.
    14. Tania García-Sánchez & Arbinda Kumar Mishra & Elías Hurtado-Pérez & Rubén Puché-Panadero & Ana Fernández-Guillamón, 2020. "A Controller for Optimum Electrical Power Extraction from a Small Grid-Interconnected Wind Turbine," Energies, MDPI, vol. 13(21), pages 1-16, November.
    15. Ganjefar, Soheil & Ghassemi, Ali Akbar & Ahmadi, Mohamad Mehdi, 2014. "Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network," Energy, Elsevier, vol. 67(C), pages 444-453.
    16. Marwa Hassan & Alsnosy Balbaa & Hanady H. Issa & Noha H. El-Amary, 2018. "Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator," Energies, MDPI, vol. 11(10), pages 1-25, October.
    17. Trujillo, C.L. & Velasco, D. & Figueres, E. & Garcerá, G., 2010. "Analysis of active islanding detection methods for grid-connected microinverters for renewable energy processing," Applied Energy, Elsevier, vol. 87(11), pages 3591-3605, November.
    18. Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    19. Sayyaadi, Hoseyn & Baghsheikhi, Mostafa, 2019. "Retrofit of a steam power plant using the adaptive neuro-fuzzy inference system in response to the load variation," Energy, Elsevier, vol. 175(C), pages 1164-1173.
    20. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).

    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:renene:v:87:y:2016:i:p1:p:646-655. 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/renewable-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.