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A survey of health monitoring systems for wind turbines

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  • Wymore, Mathew L.
  • Van Dam, Jeremy E.
  • Ceylan, Halil
  • Qiao, Daji

Abstract

Wind energy has played an increasingly vital role in renewable power generation, driving the need for more cost-effective wind energy solutions. Health monitoring of turbines could provide a variety of economic and other benefits to aid in wind growth. A number of commercial and research health monitoring systems have been implemented for wind turbines. This paper surveys these systems, providing an analysis of the current state of turbine health monitoring and the challenges associated with monitoring each of the major turbine components. This paper also contextualizes the survey with the various potential benefits of health monitoring for turbines.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:52:y:2015:i:c:p:976-990
    DOI: 10.1016/j.rser.2015.07.110
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    Cited by:

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    2. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    3. Andreas Baumann-Ouyang & Jemil Avers Butt & Matej Varga & Andreas Wieser, 2023. "MIMO-SAR Interferometric Measurements for Wind Turbine Tower Deformation Monitoring," Energies, MDPI, vol. 16(3), pages 1-20, February.
    4. 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.
    5. Paula Helming & Axel von Freyberg & Michael Sorg & Andreas Fischer, 2021. "Wind Turbine Tower Deformation Measurement Using Terrestrial Laser Scanning on a 3.4 MW Wind Turbine," Energies, MDPI, vol. 14(11), pages 1-14, June.
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    7. 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).
    8. 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.
    9. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    10. 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.
    11. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    12. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    13. Yang, Zhimin & Chai, Yi, 2016. "A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 345-359.
    14. 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.
    15. Rubert, T. & Zorzi, G. & Fusiek, G. & Niewczas, P. & McMillan, D. & McAlorum, J. & Perry, M., 2019. "Wind turbine lifetime extension decision-making based on structural health monitoring," Renewable Energy, Elsevier, vol. 143(C), pages 611-621.
    16. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
    17. 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.
    18. Mario Vieira & Brian Snyder & Elsa Henriques & Craig White & Luis Reis, 2023. "Economic Viability of Implementing Structural Health Monitoring Systems on the Support Structures of Bottom-Fixed Offshore Wind," Energies, MDPI, vol. 16(13), pages 1-20, June.
    19. Igliński, Bartłomiej & Iglińska, Anna & Koziński, Grzegorz & Skrzatek, Mateusz & Buczkowski, Roman, 2016. "Wind energy in Poland – History, current state, surveys, Renewable Energy Sources Act, SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 19-33.
    20. 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).
    21. Sakaris, Christos S. & Yang, Yang & Bashir, Musa & Michailides, Constantine & Wang, Jin & Sakellariou, John S. & Li, Chun, 2021. "Structural health monitoring of tendons in a multibody floating offshore wind turbine under varying environmental and operating conditions," Renewable Energy, Elsevier, vol. 179(C), pages 1897-1914.
    22. Castellano-Aldave, Carlos & Carlosena, Alfonso & Iriarte, Xabier & Plaza, Aitor, 2023. "Ultra-low frequency multidirectional harvester for wind turbines," Applied Energy, Elsevier, vol. 334(C).
    23. Burgaç, Alper & Yavuz, Hakan, 2019. "Fuzzy Logic based hybrid type control implementation of a heaving wave energy converter," Energy, Elsevier, vol. 170(C), pages 1202-1214.
    24. 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).
    25. 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.

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