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SCADA-Based Offshore Wind Turbine Monitoring: A Review of Methods of Addressing Marine Environmental Challenges

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Offshore wind turbines could be an important aspect of the global green energy transition, but their implementation is challenging due to the harshness of marine environments. Compared to onshore structures, offshore wind turbines are exposed to stronger loads from waves and more turbulent atmospheric conditions, while salt-laden air accelerates structural degradation. These variable environmental conditions also make diagnostics difficult. Supervisory control and data acquisition (SCADA) systems, which are already embedded in all turbines, provide a cost-effective source of operational data for performance assessment and condition monitoring. Although experience of SCADA-based onshore testing over recent decades has provided valuable knowledge, these insights cannot be directly applied to offshore monitoring. This review summarises the current state of knowledge of SCADA-based monitoring for offshore wind turbines, compares it with onshore approaches, and highlights offshore-specific challenges arising from the marine environment.
Słowa kluczowe
Rocznik
Tom
Strony
187--194
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Gdansk University of Technology, Poland
  • Gdansk University of Technology, Poland
autor
  • Gdansk University of Technology, Poland
autor
  • Gdansk University of Technology, Poland
  • Gdansk University of Technology, Poland
  • Gdansk University of Technology, Poland
Bibliografia
  • 1. WindEurope. Europe doubles down on offshore wind to drive industrial competitiveness. In WindEurope. 2024. Retrieved from https://windeurope.org/news/europe-doubles-down-on-offshore-wind-to-drive-industrial-competitiveness/.
  • 2. McMillan D, Ault GW. Quantification of condition monitoring benefit for offshore wind turbines. Wind Eng 2007, vol. 31, no. 4, pp. 267–285. https://doi.org/10.1260/030952407783123060.
  • 3. Pandit R, Wang J. A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights. IET Renew Power Gener 2024, vol. 18, no. 4, pp. 722–742. https://doi.org/10.1049/rpg2.12920.
  • 4. Cevasco D, Koukoura S, Kolios AJ. Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications. Renew Sustain Energy Rev 2021, vol. 136, p. 110414. https://doi.org/10.1016/j.rser.2020.110414.
  • 5. Larsén XG, Fischereit J, Hamzeloo S, Bärfuss K, Lampert A. Investigation of wind farm impacts on surface waves using coupled numerical simulations. Renew Energy 2024, vol. 237, p. 121671. https://doi.org/10.1016/j.renene.2024.121671.
  • 6. Niklas K. Strength analysis of a large-size supporting structure for an offshore wind turbine. Pol Marit Res 2017, vol. 24, no. s1, pp. 156–165. https://doi.org/10.1515/pomr-2017-0034.
  • 7. Agarwal D, Kishor N. A fuzzy inference-based fault detection scheme using adaptive thresholds for health monitoring of offshore wind-farms. IEEE Sens J 2014, vol. 14, no. 11, pp. 3851–3861. https://doi.org/10.1109/JSEN.2014.2347700.
  • 8. Zhu C, Zhang T. A review on the realization methods of dynamic fault tree. Qual Reliab Eng Int 2022, vol. 38, no. 6, pp. 3233–3251. https://doi.org/10.1002/qre.3139.
  • 9. Dinmohammadi F, Shafiee M. A fuzzy-FMEA risk assessment approach for offshore wind turbines. Int J Progn Health Manag 2013, vol. 4, no. 13, pp. 59–68. https://doi.org/10.36001/ijphm.2013.v4i3.2143.
  • 10. Li H, Díaz H, Soares CG. A failure analysis of floating offshore wind turbines using AHP-FMEA methodology. Ocean Eng 2021, vol. 234, p. 109261. https://doi.org/10.1016/j.oceaneng.2021.109261.
  • 11. Zhang J, Kang J, Sun L, Bai X. Risk assessment of floating offshore wind turbines based on fuzzy fault tree analysis. Ocean Eng 2021, vol. 239, p. 109859. https://doi.org/10.1016/j.oceaneng.2021.109859.
  • 12. Dienst S, Beseler J. Automatic anomaly detection in offshore wind SCADA data. Proc. WindEurope Summit 2016.
  • 13. Tautz-Weinert J, Watson SJ. Using SCADA data for wind turbine condition monitoring—A review. IET Renew Power Gener 2017, vol. 11, no. 4, pp. 382–394. https://doi.org/10.1049/iet-rpg.2016.0248.
  • 14. Miraftabzadeh SM, Colombo CG, Longo M, Foiadelli F. K-means and alternative clustering methods in modern power systems. IEEE Access 2023, vol. 11, pp. 119596–119633.
  • 15. Li YF, Hu ZA, Gao JW, Zhang YS, Li PF, Du HZ. Efficient anomaly detection method for offshore wind turbines. J Electron Sci Technol 2024, vol. 22, no. 4, p. 100285. https://doi.org/10.1016/j.jnlest.2024.100285.
  • 16. Suliaman S, Salam N. An adaptive anomaly detection and fault diagnosis in wind turbine. Proc. IEEE Int Women Eng Conf Electr Comput Eng (WIECON-ECE) 2021, pp. 172–175.
  • 17. Pandit RK, Infield D. SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes. IET Renew Power Gener 2018, vol. 12, no. 11, pp. 1249–1255. https://doi.org/10.1049/iet-rpg.2018.0156.
  • 18. Leahy K, Hu RL, Konstantakopoulos IC, Spanos CJ, Agogino AM. Diagnosing wind turbine faults using machine learning techniques applied to operational data. Proc. IEEE Int Conf Progn Health Manag (ICPHM) 2016, pp. 1–8. https://doi.org/10.1109/ICPHM.2016.7542860.
  • 19. Helbing G, Ritter M. Deep learning for fault detection in wind turbines. Renew Sustain Energy Rev 2018, vol. 98, pp. 189–198. https://doi.org/10.1016/j.rser.2018.09.012.
  • 20. Schlechtingen M, Santos IF, Achiche S. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Appl Soft Comput 2013, vol. 13, no. 1, pp. 259–270. https://doi.org/10.1016/j.asoc.2012.08.033.
  • 21. Schlechtingen M, Santos IF. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples. Appl Soft Comput 2014, vol. 14, pp. 447–460. https://doi.org/10.1016/j.asoc.2013.09.016.
  • 22. Noppe N, Iliopoulos A, Weijtjens W, Devriendt C. Full load estimation of an offshore wind turbine based on SCADA and accelerometer data. J Phys Conf Ser 2016, vol. 753, no. 7, p. 072025. https://doi.org/10.1088/1742-6596/753/7/072025.
  • 23. Branlard E, Jonkman J, Brown C, Zhang J. A digital twin solution for floating offshore wind turbines validated using a full-scale prototype. Wind Energy Sci 2024, vol. 9, no. 1, pp. 1–24. https://doi.org/10.5194/wes-9-1-2024.
  • 24. Jamil F, Peeters C, Verstraeten T, Helsen J. Leveraging signal processing and machine learning for automated fault detection in wind turbine drivetrains. Wind Energy Sci Discuss 2024, vol. 10, pp. 1963–1978. https://doi.org/10.5194/wes-10-1963-2025.
  • 25. Ziegler L, Gonzalez E, Rubert T, Smolka U, Melero JJ. Lifetime extension of onshore wind turbines: A review covering Germany, Spain, Denmark, and the UK. Renew Sustain Energy Rev 2018, vol. 82, pp. 1261–1271. https://doi.org/10.1016/j.rser.2017.09.100.
  • 26. Liang J, Fu Y, Wang Y, Ou J. Identification of equivalent wind and wave loads for monopile-supported offshore wind turbines in operating condition. Renew Energy 2024, vol. 237, p. 121525. https://doi.org/10.1016/j.renene.2024.121525.
  • 27. Gebel J, Rezaei A, Vemuri A, Liverud Krathe V, Daems PJ, Matthys JJ, Helsen J. System identification of offshore wind turbines for model updating and validation using field measurements. Wind Energy Sci Discuss 2025, pp. 1–25.
  • 28. Ambarita EE, Karlsen A, Scibilia F, Hasan A. Industrial digital twins in offshore wind farms. Energy Inform 2024, vol. 7, no. 1, p. 5. https://doi.org/10.1186/s42162-024-00306-6.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5db5cfef-1e99-4b27-b392-b61148031abb
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