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Wind turbine performance degradation assessment based on a novel similarity metric for machine performance curves

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  • Jia, Xiaodong
  • Jin, Chao
  • Buzza, Matt
  • Wang, Wei
  • Lee, Jay

Abstract

Prognostics and Health Management (PHM) can offer substantial improvements in reliability and availability of the wind turbine asset. Driven by reducing the Operation and Maintenance (O&M) cost of wind turbines, many research efforts have been conducted to realize reliable wind turbine performance degradation assessment. Despite these efforts, it is still challenging to assess the actual degradation trend of wind turbine which will be suitable for prediction analysis. In this study, a novel similarity metric for machine performance curves is proposed and a framework of wind turbine performance assessment methodology is presented. The proposed algorithm evaluates the health condition of wind turbine by performing principal component analysis on the quasi-linear region of the power curve. The proposed methodology has been validated on a dataset collected from a large scale onshore wind turbine for a period of two years. The result exhibits a gradual degradation trend of wind turbine and indicates the ability of proposed approach to trend and assess the turbine degradation before downtime happens. The result from the proposed method also reveals its robustness to wind resolution in the power curve, which still exhibits a very similar degradation trend when the wind resolution of power curve has been down sampled.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:1191-1201
    DOI: 10.1016/j.renene.2016.08.018
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    References listed on IDEAS

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    Cited by:

    1. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    2. Suárez-Cetrulo, Andrés L. & Burnham-King, Lauren & Haughton, David & Carbajo, Ricardo Simón, 2022. "Wind power forecasting using ensemble learning for day-ahead energy trading," Renewable Energy, Elsevier, vol. 191(C), pages 685-698.
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    4. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.
    5. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
    6. 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.
    7. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    8. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Yang, Qibo & Hsu, Yuan-Ming & Chen, Yudi & Lee, Jay, 2019. "A combined filtering strategy for short term and long term wind speed prediction with improved accuracy," Renewable Energy, Elsevier, vol. 136(C), pages 1082-1090.
    9. Giani, Paolo & Tagle, Felipe & Genton, Marc G. & Castruccio, Stefano & Crippa, Paola, 2020. "Closing the gap between wind energy targets and implementation for emerging countries," Applied Energy, Elsevier, vol. 269(C).
    10. Rubert, T. & McMillan, D. & Niewczas, P., 2018. "A decision support tool to assist with lifetime extension of wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 423-433.
    11. Junxun Chen & Longsheng Cheng & Hui Yu & Shaolin Hu, 2018. "Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(1), pages 147-159, January.
    12. Tucker, Swatara & Indrawan, Natarianto & Shadle, Lawrence J. & Harun, Nor Farida & Tucker, David, 2024. "Performance degradation in an advanced power system by analyzing process dynamics," Applied Energy, Elsevier, vol. 369(C).
    13. Barlow, E. & Bedford, T. & Revie, M. & Tan, J. & Walls, L., 2021. "A performance-centred approach to optimising maintenance of complex systems," European Journal of Operational Research, Elsevier, vol. 292(2), pages 579-595.
    14. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    15. Zheng, Yi & You, Shi & Huang, Chunjun & Jin, Xin, 2023. "Model-based economic analysis of off-grid wind/hydrogen systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    16. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    17. Soszyńska-Budny Joanna & Chmielewski Mariusz & Pioch Joanna, 2023. "Reliability of Renewable Power Generation using the Example of Offshore Wind Farms," Folia Oeconomica Stetinensia, Sciendo, vol. 23(1), pages 228-245, June.
    18. Aziz, Usama & Charbonnier, Sylvie & Bérenguer, Christophe & Lebranchu, Alexis & Prevost, Frederic, 2021. "Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    19. Li, Yanting & Wang, Peng & Wu, Zhenyu & Su, Yan, 2024. "Collaborative monitoring of wind turbine performance based on probabilistic power curve comparison," Renewable Energy, Elsevier, vol. 231(C).

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