IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v22y2013icp515-526.html
   My bibliography  Save this article

Testing, inspecting and monitoring technologies for wind turbine blades: A survey

Author

Listed:
  • Yang, Bin
  • Sun, Dongbai

Abstract

Renewable wind energy is one of the most efficient and effective ways to deal with global warming and energy crisis. Recently, wind energy has grown at an impressive rate in entire world. As we know, the blades are the most important components of wind turbine. In order to increase the energy conversion efficiency, the size of wind turbine blades becomes more and more big which blade diameter ranges from about 20m to about 100m or even. However, wind turbine blades are facing increasingly harsh and complexity service environment. It is necessary to testing, inspecting and monitoring of wind turbine blades in order to guarantee the service safety of wind turbine blades. This paper surveys the testing, inspecting and monitoring technologies for wind turbine blades, including mechanical property testing, non-destructive testing/inspecting, full-scale testing, structural health monitoring and condition monitoring. And then, the development trends and some suggestions of testing, inspecting and monitoring technologies for wind turbine blades are discussed.

Suggested Citation

  • Yang, Bin & Sun, Dongbai, 2013. "Testing, inspecting and monitoring technologies for wind turbine blades: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 515-526.
    • Handle: RePEc:eee:rensus:v:22:y:2013:i:c:p:515-526
    DOI: 10.1016/j.rser.2012.12.056
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032113000129
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2012.12.056?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. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    2. Yang, Jinshui & Peng, Chaoyi & Xiao, Jiayu & Zeng, Jingcheng & Yuan, Yun, 2012. "Application of videometric technique to deformation measurement for large-scale composite wind turbine blade," Applied Energy, Elsevier, vol. 98(C), pages 292-300.
    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.
    4. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    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. 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. 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.
    3. Chandrasekhar, Kartik & Stevanovic, Nevena & Cross, Elizabeth J. & Dervilis, Nikolaos & Worden, Keith, 2021. "Damage detection in operational wind turbine blades using a new approach based on machine learning," Renewable Energy, Elsevier, vol. 168(C), pages 1249-1264.
    4. Lenci, Stefano, 2023. "Along-wind and cross-wind coupled nonlinear oscillations of wind turbine towers close to 1:1 internal resonance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    5. Howlader, Abdul Motin & Senjyu, Tomonobu, 2016. "A comprehensive review of low voltage ride through capability strategies for the wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 643-658.
    6. Wymore, Mathew L. & Van Dam, Jeremy E. & Ceylan, Halil & Qiao, Daji, 2015. "A survey of health monitoring systems for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 976-990.
    7. 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.
    8. Guo, Jihong & Liu, Chao & Cao, Jinfeng & Jiang, Dongxiang, 2021. "Damage identification of wind turbine blades with deep convolutional neural networks," Renewable Energy, Elsevier, vol. 174(C), pages 122-133.
    9. Yang, Ruizhen & He, Yunze & Zhang, Hong, 2016. "Progress and trends in nondestructive testing and evaluation for wind turbine composite blade," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1225-1250.
    10. Feng Gao & Xiaojiang Wu & Qiang Liu & Juncheng Liu & Xiyun Yang, 2019. "Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines," Energies, MDPI, vol. 12(3), pages 1-16, February.
    11. Esu, O.O. & Lloyd, S.D. & Flint, J.A. & Watson, S.J., 2016. "Feasibility of a fully autonomous wireless monitoring system for a wind turbine blade," Renewable Energy, Elsevier, vol. 97(C), pages 89-96.
    12. de Oliveira Nogueira, Tiago & Palacio, Gilderlânio Barbosa Alves & Braga, Fabrício Damasceno & Maia, Pedro Paulo Nunes & de Moura, Elineudo Pinho & de Andrade, Carla Freitas & Rocha, Paulo Alexandre C, 2022. "Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines," Energy, Elsevier, vol. 238(PC).
    13. 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).
    14. Sungmok Hwang & Cheol Yoo, 2021. "Health Monitoring and Diagnosis System for a Small H-Type Darrieus Vertical-Axis Wind Turbine," Energies, MDPI, vol. 14(21), pages 1-18, November.
    15. Ossai, Chinedu I., 2017. "Optimal renewable energy generation – Approaches for managing ageing assets mechanisms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 269-280.
    16. Melo Junior, Francisco Erivan de Abreu & de Moura, Elineudo Pinho & Costa Rocha, Paulo Alexandre & de Andrade, Carla Freitas, 2019. "Unbalance evaluation of a scaled wind turbine under different rotational regimes via detrended fluctuation analysis of vibration signals combined with pattern recognition techniques," Energy, Elsevier, vol. 171(C), pages 556-565.
    17. José Gibergans-Báguena & Pablo Buenestado & Gisela Pujol-Vázquez & Leonardo Acho, 2022. "A Proportional Digital Controller to Monitor Load Variation in Wind Turbine Systems," Energies, MDPI, vol. 15(2), pages 1-27, January.
    18. Kaewniam, Panida & Cao, Maosen & Alkayem, Nizar Faisal & Li, Dayang & Manoach, Emil, 2022. "Recent advances in damage detection of wind turbine blades: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    19. Luo, Kai & Chen, Liang & Liang, Wei, 2022. "Structural health monitoring of carbon fiber reinforced polymer composite laminates for offshore wind turbine blades based on dual maximum correlation coefficient method," Renewable Energy, Elsevier, vol. 201(P1), pages 1163-1175.
    20. 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.
    21. Ruiz de la Hermosa González-Carrato, Raúl, 2017. "Sound and vibration-based pattern recognition for wind turbines driving mechanisms," Renewable Energy, Elsevier, vol. 109(C), pages 262-274.

    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. Colak, Ilhami & Fulli, Gianluca & Bayhan, Sertac & Chondrogiannis, Stamatios & Demirbas, Sevki, 2015. "Critical aspects of wind energy systems in smart grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 155-171.
    2. Cristina Vázquez-Hernández & Javier Serrano-González & Gabriel Centeno, 2017. "A Market-Based Analysis on the Main Characteristics of Gearboxes Used in Onshore Wind Turbines," Energies, MDPI, vol. 10(11), pages 1-17, October.
    3. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    4. Kaldellis, John K. & Zafirakis, D., 2011. "The wind energy (r)evolution: A short review of a long history," Renewable Energy, Elsevier, vol. 36(7), pages 1887-1901.
    5. Wenna Zhang & Xiandong Ma, 2016. "Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines," Energies, MDPI, vol. 9(4), pages 1-15, April.
    6. Márquez, Fausto Pedro García & Pérez, Jesús María Pinar & Marugán, Alberto Pliego & Papaelias, Mayorkinos, 2016. "Identification of critical components of wind turbines using FTA over the time," Renewable Energy, Elsevier, vol. 87(P2), pages 869-883.
    7. 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.
    8. Bakir, I. & Yildirim, M. & Ursavas, E., 2021. "An integrated optimization framework for multi-component predictive analytics in wind farm operations & maintenance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    9. Mohd Zin, Abdullah Asuhaimi B. & Pesaran H.A., Mahmoud & Khairuddin, Azhar B. & Jahanshaloo, Leila & Shariati, Omid, 2013. "An overview on doubly fed induction generators′ controls and contributions to wind based electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 692-708.
    10. 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.
    11. Bhutto, Abdul Waheed & Bazmi, Aqeel Ahmed & Zahedi, Gholamreza, 2013. "Greener energy: Issues and challenges for Pakistan—wind power prospective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 519-538.
    12. Pedro Santos & Jesús Maudes & Andres Bustillo, 2018. "Identifying maximum imbalance in datasets for fault diagnosis of gearboxes," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 333-351, February.
    13. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
    14. Fallahi, F. & Bakir, I. & Yildirim, M. & Ye, Z., 2022. "A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. Ghasemi, Hosein & Gharehpetian, G.B. & Nabavi-Niaki, Seyed Ali & Aghaei, Jamshid, 2013. "Overview of subsynchronous resonance analysis and control in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 234-243.
    16. Ertürk, Mehmet, 2012. "The evaluation of feed-in tariff regulation of Turkey for onshore wind energy based on the economic analysis," Energy Policy, Elsevier, vol. 45(C), pages 359-367.
    17. Sebestyén, Viktor, 2021. "Renewable and Sustainable Energy Reviews: Environmental impact networks of renewable energy power plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    18. Hung-Ta Wen & Jau-Huai Lu & Mai-Xuan Phuc, 2021. "Applying Artificial Intelligence to Predict the Composition of Syngas Using Rice Husks: A Comparison of Artificial Neural Networks and Gradient Boosting Regression," Energies, MDPI, vol. 14(10), pages 1-18, May.
    19. 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.
    20. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.

    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:22:y:2013:i:c:p:515-526. 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.