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Wind turbine performance assessment using multi-regime modeling approach

Author

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  • Lapira, Edzel
  • Brisset, Dustin
  • Davari Ardakani, Hossein
  • Siegel, David
  • Lee, Jay

Abstract

Prognostics & health management system is an integral component of any wind energy program to ensure high turbine availability and reliability. Traditional vibration-based condition monitoring practices have been proposed to be utilized with wind turbines as they have demonstrated varying degrees of success with other rotary machinery. However, high-frequency data such as vibration and acoustic emission signals, generally, are not collected and recorded due to limitations with data storage capacities. In addition, the highly dynamic operating conditions of a wind turbine pose a challenge to conventional frequency domain analysis tools. Thus, a systematic framework that utilizes multi-regime modeling approach is proposed to consider the dynamic working conditions of a wind turbine. Three methods were developed, and they were evaluated using SCADA (supervisory control and data acquisition) data only that have been collected from a large-scale on-shore wind turbine for 27 months. Empirical observations from the results of the three methods indicate the ability of the approach to trend and assess turbine degradation prior to known downtime occurrences.

Suggested Citation

  • Lapira, Edzel & Brisset, Dustin & Davari Ardakani, Hossein & Siegel, David & Lee, Jay, 2012. "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, Elsevier, vol. 45(C), pages 86-95.
    • Handle: RePEc:eee:renene:v:45:y:2012:i:c:p:86-95
    DOI: 10.1016/j.renene.2012.02.018
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    References listed on IDEAS

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    1. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "Models for monitoring wind farm power," Renewable Energy, Elsevier, vol. 34(3), pages 583-590.
    2. 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.
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    Cited by:

    1. 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.
    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. Raymond Byrne & Davide Astolfi & Francesco Castellani & Neil J. Hewitt, 2020. "A Study of Wind Turbine Performance Decline with Age through Operation Data Analysis," Energies, MDPI, vol. 13(8), pages 1-18, April.
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    5. John Thomas Lyons & Tuhfe Göçmen, 2021. "Applied Machine Learning Techniques for Performance Analysis in Large Wind Farms," Energies, MDPI, vol. 14(13), pages 1-28, June.
    6. Sequeira, C. & Pacheco, A. & Galego, P. & Gorbeña, E., 2019. "Analysis of the efficiency of wind turbine gearboxes using the temperature variable," Renewable Energy, Elsevier, vol. 135(C), pages 465-472.
    7. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
    8. 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.
    9. Hong Wang & Hongbin Wang & Guoqian Jiang & Jimeng Li & Yueling Wang, 2019. "Early Fault Detection of Wind Turbines Based on Operational Condition Clustering and Optimized Deep Belief Network Modeling," Energies, MDPI, vol. 12(6), pages 1-22, March.
    10. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    11. Jia, Xiaodong & Jin, Chao & Buzza, Matt & Wang, Wei & Lee, Jay, 2016. "Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves," Renewable Energy, Elsevier, vol. 99(C), pages 1191-1201.
    12. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    13. Hu, Yaogang & Li, Hui & Shi, Pingping & Chai, Zhaosen & Wang, Kun & Xie, Xiangjie & Chen, Zhe, 2018. "A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process," Renewable Energy, Elsevier, vol. 127(C), pages 452-460.
    14. 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.
    15. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
    16. 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.
    17. Mian Du & Jun Yi & Peyman Mazidi & Lin Cheng & Jianbo Guo, 2017. "A Parameter Selection Method for Wind Turbine Health Management through SCADA Data," Energies, MDPI, vol. 10(2), pages 1-14, February.
    18. Mérigaud, Alexis & Ringwood, John V., 2016. "Condition-based maintenance methods for marine renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 53-78.
    19. Jeong, Haedong & Park, Bumsoo & Park, Seungtae & Min, Hyungcheol & Lee, Seungchul, 2019. "Fault detection and identification method using observer-based residuals," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 27-40.
    20. Quan Zhou & Taotao Xiong & Mubin Wang & Chenmeng Xiang & Qingpeng Xu, 2017. "Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS," Energies, MDPI, vol. 10(7), pages 1-15, July.
    21. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.

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