IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v251y2019ic4.html
   My bibliography  Save this article

Improving the reliability and energy production of large wind turbine with a digital hydrostatic drivetrain

Author

Listed:
  • Wang, Feng
  • Chen, Jincheng
  • Xu, Bing
  • Stelson, Kim A.

Abstract

Gearbox failure is one of the major factors causing the increased downtime in modern wind turbines. This increases the turbine maintenance cost and therefore the turbine cost of energy. A hydrostatic transmission not only improves the turbine reliability through its “soft” transmission, but also eliminates the use of power converter through its continuous variable transmission function. A hydrostatic wind turbine usually employs a fixed displacement pump to drive a variable displacement motor. The variable displacement motor runs at partial displacement when the wind speed is below the rated wind speed, leading to low drivetrain efficiency. Moreover, large variable displacement motors for large utility wind turbine are not commercially available. Therefore in this paper a digital hydrostatic transmission solution for large utility wind turbine has been proposed. The large variable displacement motor was replaced by combining several fixed displacement motors and a small variable displacement motor with some digital encoding scheme. Two encoding schemes have been proposed for digital hydrostatic transmission. The modeling and design of the digital hydrostatic wind turbine were presented. A hydrostatic wind turbine control based on kw2 control law has been proposed. A dynamic simulation model of the digital hydrostatic wind turbine has been developed. The proposed digital hydrostatic drive solution has been compared with a conventional hydrostatic solution in a commercial 2.5 MW wind turbine. Simulation studies verified the feasibility and the engineering practice of the proposed digital hydrostatic drive solution.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:4
    DOI: 10.1016/j.apenergy.2019.113309
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919309717
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113309?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. Lin, Yonggang & Tu, Le & Liu, Hongwei & Li, Wei, 2016. "Fault analysis of wind turbines in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 482-490.
    2. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    3. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    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. Nikolić, Vlastimir & Sajjadi, Shahin & Petković, Dalibor & Shamshirband, Shahaboddin & Ćojbašić, Žarko & Por, Lip Yee, 2016. "Design and state of art of innovative wind turbine systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 258-265.
    6. 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.
    7. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    8. Silva, Paolo & Giuffrida, Antonio & Fergnani, Nicola & Macchi, Ennio & Cantù, Matteo & Suffredini, Roberto & Schiavetti, Massimo & Gigliucci, Gianluca, 2014. "Performance prediction of a multi-MW wind turbine adopting an advanced hydrostatic transmission," Energy, Elsevier, vol. 64(C), pages 450-461.
    9. Petković, Dalibor & Ćojbašič, Žarko & Nikolić, Vlastimir, 2013. "Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 191-195.
    10. 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. Jiang, Zhiyu & Yang, Limin & Gao, Zhen & Moan, Torgeir, 2022. "Integrated dynamic analysis of a spar floating wind turbine with a hydraulic drivetrain," Renewable Energy, Elsevier, vol. 201(P1), pages 608-623.
    2. Xin Wang & Zhongyu Wang & Lei Xie & Songlin Wang & Zhongshan Wang & Wenxing Ma, 2024. "Research on the New Hydrostatic Transmission System of Wheel Loaders Based on Fuzzy Sliding Mode Control," Energies, MDPI, vol. 17(3), pages 1-23, January.
    3. Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
    4. 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.
    5. Chao Ai & Wei Gao & Qinyu Hu & Yankang Zhang & Lijuan Chen & Jiawei Guo & Zengrui Han, 2020. "Application of the Feedback Linearization in Maximum Power Point Tracking Control for Hydraulic Wind Turbine," Energies, MDPI, vol. 13(6), pages 1-18, March.
    6. Zielinski, Michał & Myszkowski, Adam & Pelic, Marcin & Staniek, Roman, 2022. "Low-speed radial piston pump as an effective alternative power transmission for small hydropower plants," Renewable Energy, Elsevier, vol. 182(C), pages 1012-1027.
    7. Postnikov, Ivan, 2022. "A reliability assessment of the heating from a hybrid energy source based on combined heat and power and wind power plants," Reliability Engineering and System Safety, 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. Cevasco, D. & Koukoura, S. & Kolios, A.J., 2021. "Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    2. Shamshirband, Shahaboddin & Petković, Dalibor & Amini, Amineh & Anuar, Nor Badrul & Nikolić, Vlastimir & Ćojbašić, Žarko & Mat Kiah, Miss Laiha & Gani, Abdullah, 2014. "Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission," Energy, Elsevier, vol. 67(C), pages 623-630.
    3. Kim, Dongwoo & Song, Kang Sub & Lim, Junyub & Kim, Yongchan, 2018. "Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions," Energy, Elsevier, vol. 155(C), pages 117-127.
    4. 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.
    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. 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.
    7. 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).
    8. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    9. Anicic, Obrad & Jovic, Srdjan, 2016. "Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1111-1116.
    10. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    11. 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.
    12. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    13. Tao Wang & He Wang, 2017. "Research on an Integrated Hydrostatic-Driven Electric Generator with Controllable Load for Renewable Energy Applications," Energies, MDPI, vol. 10(9), pages 1-17, August.
    14. Li, Jianlan & Zhang, Xuran & Zhou, Xing & Lu, Luyi, 2019. "Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model," Renewable Energy, Elsevier, vol. 132(C), pages 1076-1087.
    15. Siddiqui, Muhammad Omer & Feja, Paul Robert & Borowski, Philipp & Kyling, Hans & Nejad, Amir R. & Wenske, Jan, 2023. "Wind turbine nacelle testing: State-of-the-art and development trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    16. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    17. Shamshirband, Shahaboddin & Keivani, Afram & Mohammadi, Kasra & Lee, Malrey & Hamid, Siti Hafizah Abd & Petkovic, Dalibor, 2016. "Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 429-435.
    18. Yin, Xiuxing & Zhao, Xiaowei & Lin, Jin & Karcanias, Aris, 2020. "Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations," Energy, Elsevier, vol. 202(C).
    19. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    20. Chan, C.M. & Bai, H.L. & He, D.Q., 2018. "Blade shape optimization of the Savonius wind turbine using a genetic algorithm," Applied Energy, Elsevier, vol. 213(C), pages 148-157.

    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:appene:v:251:y:2019:i:c:4. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.