IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v41y2015icp189-201.html
   My bibliography  Save this article

Load monitoring for active control of wind turbines

Author

Listed:
  • Cooperman, Aubryn
  • Martinez, Marcias

Abstract

This review article examines the range of sensors that have been proposed for monitoring wind turbine blade loads for the purpose of active load control over the past decade. Wind turbine active load control requires sensors that are able to detect loads as they occur, in order to enable a prompt actuation of control devices. Loads may be detected based on structural effects or inferred from aerodynamic measurements. This paper is organized into the following sections: wind turbine control, requirements for load monitoring sensors, sensing technologies and field tests of load control. The types of sensors examined in this article include fiber optic sensors, inertial sensors, pressure measurements and remote optical sensing.

Suggested Citation

  • Cooperman, Aubryn & Martinez, Marcias, 2015. "Load monitoring for active control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 189-201.
    • Handle: RePEc:eee:rensus:v:41:y:2015:i:c:p:189-201
    DOI: 10.1016/j.rser.2014.08.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032114007059
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2014.08.029?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. Schubel, P.J. & Crossley, R.J. & Boateng, E.K.G. & Hutchinson, J.R., 2013. "Review of structural health and cure monitoring techniques for large wind turbine blades," Renewable Energy, Elsevier, vol. 51(C), pages 113-123.
    2. Ozbek, Muammer & Rixen, Daniel J. & Erne, Oliver & Sanow, Gunter, 2010. "Feasibility of monitoring large wind turbines using photogrammetry," Energy, Elsevier, vol. 35(12), pages 4802-4811.
    3. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    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. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Lucas, Tiago R. & Ferreira, Ana F. & Santos Pereira, R.B. & Alves, Marco, 2022. "Hydrogen production from the WindFloat Atlantic offshore wind farm: A techno-economic analysis," Applied Energy, Elsevier, vol. 310(C).
    3. Sidik, Muhammad Abu Bakar & Shahroom, Hamizah Binti & Salam, Zainal & Buntat, Zokafle & Nawawi, Zainuddin & Ahmad, Hussein & Jambak, Muhammad ’Irfan & Arief, Yanuar Zulardiansyah, 2015. "Lightning monitoring system for sustainable energy supply: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 710-725.
    4. Watson, Simon & Moro, Alberto & Reis, Vera & Baniotopoulos, Charalampos & Barth, Stephan & Bartoli, Gianni & Bauer, Florian & Boelman, Elisa & Bosse, Dennis & Cherubini, Antonello & Croce, Alessandro , 2019. "Future emerging technologies in the wind power sector: A European perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Zhang, Mingming & Tan, Bin & Xu, Jianzhong, 2016. "Smart fatigue load control on the large-scale wind turbine blades using different sensing signals," Renewable Energy, Elsevier, vol. 87(P1), pages 111-119.
    6. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.
    7. Stephanie Ordonez-Sanchez & Matthew Allmark & Kate Porter & Robert Ellis & Catherine Lloyd & Ivan Santic & Tim O’Doherty & Cameron Johnstone, 2019. "Analysis of a Horizontal-Axis Tidal Turbine Performance in the Presence of Regular and Irregular Waves Using Two Control Strategies," Energies, MDPI, vol. 12(3), pages 1-22, January.
    8. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    9. Bakdi, Azzeddine & Kouadri, Abdelmalek & Mekhilef, Saad, 2019. "A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 546-555.

    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. Beganovic, Nejra & Söffker, Dirk, 2016. "Structural health management utilization for lifetime prognosis and advanced control strategy deployment of wind turbines: An overview and outlook concerning actual methods, tools, and obtained result," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 68-83.
    2. Jijian Lian & Ou Cai & Xiaofeng Dong & Qi Jiang & Yue Zhao, 2019. "Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    3. Zhou, H.F. & Zheng, J.F. & Xie, Z.L. & Lu, L.J. & Ni, Y.Q. & Ko, J.M., 2017. "Temperature effects on vision measurement system in long-term continuous monitoring of displacement," Renewable Energy, Elsevier, vol. 114(PB), pages 968-983.
    4. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    5. Zengyi Zhang & Zhenru Shu, 2024. "Unmanned Aerial Vehicle (UAV)-Assisted Damage Detection of Wind Turbine Blades: A Review," Energies, MDPI, vol. 17(15), pages 1-31, July.
    6. Le Zhang & Qiang Yang, 2020. "Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox," Energies, MDPI, vol. 13(2), pages 1-13, January.
    7. Jin, Xin & Ju, Wenbin & Zhang, Zhaolong & Guo, Lianxin & Yang, Xiangang, 2016. "System safety analysis of large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1293-1307.
    8. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    9. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    10. Moynihan, Bridget & Moaveni, Babak & Liberatore, Sauro & Hines, Eric, 2022. "Estimation of blade forces in wind turbines using blade root strain measurements with OpenFAST verification," Renewable Energy, Elsevier, vol. 184(C), pages 662-676.
    11. Taylor, Josh A. & Dhople, Sairaj V. & Callaway, Duncan S., 2016. "Power systems without fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1322-1336.
    12. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
    13. Wenyi, Liu & Zhenfeng, Wang & Jiguang, Han & Guangfeng, Wang, 2013. "Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM," Renewable Energy, Elsevier, vol. 50(C), pages 1-6.
    14. Harrold, Magnus & Ouro, Pablo & O’Doherty, Tim, 2020. "Performance assessment of a tidal turbine using two flow references," Renewable Energy, Elsevier, vol. 153(C), pages 624-633.
    15. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    16. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    17. Jianjun Chen & Weihao Hu & Di Cao & Bin Zhang & Qi Huang & Zhe Chen & Frede Blaabjerg, 2019. "An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach," Energies, MDPI, vol. 12(14), pages 1-15, July.
    18. Guerine, A. & El Hami, A. & Walha, L. & Fakhfakh, T. & Haddar, M., 2017. "Dynamic response of wind turbine gear system with uncertain-but-bounded parameters using interval analysis method," Renewable Energy, Elsevier, vol. 113(C), pages 679-687.
    19. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    20. Teng, Wei & Ding, Xian & Zhang, Xiaolong & Liu, Yibing & Ma, Zhiyong, 2016. "Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform," Renewable Energy, Elsevier, vol. 93(C), pages 591-598.

    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:rensus:v:41:y:2015:i:c:p:189-201. 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/600126/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.