IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v158y2022ics1364032122001113.html
   My bibliography  Save this article

Review of robot-based damage assessment for offshore wind turbines

Author

Listed:
  • Liu, Y.
  • Hajj, M.
  • Bao, Y.

Abstract

Offshore wind turbines are subjected to highly-varying dynamic loadings and accelerated material degradation, resulting in the need for structural health monitoring, which increases the operation and maintenance cost and ultimately the levelized cost of electricity. Recent advances in robotics and intelligent algorithms offer new opportunities for automated damage assessment that would minimize these costs. This review aims to establish a holistic understanding of robot-based damage assessment technologies and to promote the development and application of these technologies for automated condition assessment of offshore wind turbines. It covers robots as potential carriers of inspection devices, damage inspection approaches, and intelligent algorithms for damage detection, classification, localization, and quantification for offshore wind turbines. The robots include climbing and underwater varieties, and unmanned aerial vehicles, which carry optical and infrared cameras, and X-ray equipment. Advanced machine learning algorithms for analysis of inspection data are evaluated. Challenges and opportunities of robot-based damage assessment technologies are discussed.

Suggested Citation

  • Liu, Y. & Hajj, M. & Bao, Y., 2022. "Review of robot-based damage assessment for offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:rensus:v:158:y:2022:i:c:s1364032122001113
    DOI: 10.1016/j.rser.2022.112187
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032122001113
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2022.112187?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. 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.
    2. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    3. Jensen, J.P. & Skelton, K., 2018. "Wind turbine blade recycling: Experiences, challenges and possibilities in a circular economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 165-176.
    4. ASM Shihavuddin & Xiao Chen & Vladimir Fedorov & Anders Nymark Christensen & Nicolai Andre Brogaard Riis & Kim Branner & Anders Bjorholm Dahl & Rasmus Reinhold Paulsen, 2019. "Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis," Energies, MDPI, vol. 12(4), pages 1-15, February.
    5. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    6. Tziavos, Nikolaos I. & Hemida, H. & Dirar, S. & Papaelias, M. & Metje, N. & Baniotopoulos, C., 2020. "Structural health monitoring of grouted connections for offshore wind turbines by means of acoustic emission: An experimental study," Renewable Energy, Elsevier, vol. 147(P1), pages 130-140.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Zountouridou, E.I. & Kiokes, G.C. & Chakalis, S. & Georgilakis, P.S. & Hatziargyriou, N.D., 2015. "Offshore floating wind parks in the deep waters of Mediterranean Sea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 433-448.
    12. Martinez-Luengo, Maria & Kolios, Athanasios & Wang, Lin, 2016. "Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 91-105.
    13. Josef Franko & Shengzhi Du & Stephan Kallweit & Enno Duelberg & Heiko Engemann, 2020. "Design of a Multi-Robot System for Wind Turbine Maintenance," Energies, MDPI, vol. 13(10), pages 1-18, May.
    14. Liu, W.Y. & Tang, B.P. & Han, J.G. & Lu, X.N. & Hu, N.N. & He, Z.Z., 2015. "The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 466-472.
    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. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.
    2. Verma, Amrit Shankar & Yan, Jiquan & Hu, Weifei & Jiang, Zhiyu & Shi, Wei & Teuwen, Julie J.E., 2023. "A review of impact loads on composite wind turbine blades: Impact threats and classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(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. 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.
    2. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. 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).
    4. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    5. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    6. 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).
    7. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    8. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    9. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    10. Njiri, Jackson G. & Beganovic, Nejra & Do, Manh H. & Söffker, Dirk, 2019. "Consideration of lifetime and fatigue load in wind turbine control," Renewable Energy, Elsevier, vol. 131(C), pages 818-828.
    11. Masoud Asgarpour & John Dalsgaard Sørensen, 2018. "Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms," Energies, MDPI, vol. 11(2), pages 1-17, January.
    12. Xiaowen Song & Zhitai Xing & Yan Jia & Xiaojuan Song & Chang Cai & Yinan Zhang & Zekun Wang & Jicai Guo & Qingan Li, 2022. "Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage," Energies, MDPI, vol. 15(20), pages 1-17, October.
    13. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
    14. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
    15. Wenjie Wang & Yu Xue & Chengkuan He & Yongnian Zhao, 2022. "Review of the Typical Damage and Damage-Detection Methods of Large Wind Turbine Blades," Energies, MDPI, vol. 15(15), pages 1-31, August.
    16. Chen, Xuejun & Yang, Yongming & Cui, Zhixin & Shen, Jun, 2019. "Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy," Energy, Elsevier, vol. 174(C), pages 1100-1109.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:158:y:2022:i:c:s1364032122001113. 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.