access icon free Aggregate reliability analysis of wind turbine generators

In North America, many utility-scale turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimise the life and value of their farm assets. In this study, data records from a wind farm have been used to estimate the reliability of wind turbine (WT) generators. For this study, non-parametric life data analysis, Weibull Standard Folio life data analysis, and ALTA Standard Folio life data analysis have been used to predict the reliability of the generators. The naive prediction interval procedure also has been used here to provide an approximate range for the remaining life of each generator. This study provides some insight into how reliable a subset of WT generators is and the lifetime distribution of individual generators. These outcomes may be leveraged further by the research community for companion applications like prognostic maintenance and investment decision support systems. This study also begins to investigate how electrical loads may influence turbine generator reliability. The work also illustrates a valuable example of how to estimate component remaining useful life based on truncated/limited data records.

Inspec keywords: maintenance engineering; power generation reliability; investment; turbogenerators; wind power plants; wind turbines; remaining life assessment; condition monitoring; Weibull distribution; data analysis; failure analysis

Other keywords: North America; utility-scale turbines; accurate estimation; originally anticipated lifespan; wind farm owners; remaining life; aggregate reliability analysis; investment decision support systems; electrical loads; Weibull Standard Folio life data analysis; farm assets; wind turbine generators; prognostic maintenance; lifetime distribution; data records; naive prediction interval procedure; WT generators; ALTA Standard Folio life data analysis; individual generators; nonparametric life data analysis; useful life; turbine generator reliability; turbine components

Subjects: Wind power plants; Plant engineering, maintenance and safety; Other topics in statistics; a.c. machines; Reliability

References

    1. 1)
      • 32. Gourdin, E., Hansen, P., Jaumard, B.: ‘Finding maximum likelihood estimators for the three-parameter Weibull distribution’, J. Global Optim., 1994, 5, (4), pp. 373397.
    2. 2)
      • 15. Yang, W., Tavner, P.J., Wilkinson, M.R.: ‘Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train’, IET Renew. Power Gener., 2009, 3, (1), pp. 111.
    3. 3)
      • 19. Sikorska, J.Z., Hodkiewicz, M., Ma, L.: ‘Prognostic modelling options for remaining useful life estimation by industry’, Mech. Syst. Signal Process., 2011, 25, (5), pp. 18031836.
    4. 4)
      • 13. Fitzgibbon, K., Barker, R., Clayton, T.: ‘A failure-forecast method based on Weibull and statistical-pattern analysis’. Annual Reliability and Maintainability Symp., Seattle, WA, USA, January 2002, pp. 516521.
    5. 5)
      • 5. Chen, N., Yu, R., Chen, Y., et al: ‘Hierarchical method for wind turbine prognosis using SCADA data’, IET Renew. Power Gener., 2016, 11, (4), pp. 403410.
    6. 6)
      • 2. ‘Global Wind Energy Outlook 2016: Wind Power to dominate power sector growth’, https://gwec.net/publications/global-wind-energy-outlook/global-wind-energy-outlook-2016/ March 2019.
    7. 7)
      • 6. Hahn, B., Durstewitz, M., Rohrig, K.: ‘Reliability of wind turbines: experiences of 15 years with 1,500 WTs’. Wind Energy Proc. Euromech Colloquium, Heidelberg, Germany, July 2007, pp. 329332.
    8. 8)
      • 23. Akdağ, S.A., Dinler, A.: ‘A new method to estimate Weibull parameters for wind energy applications’, Energy Convers. Manage., 2009, 50, (7), pp. 17611766.
    9. 9)
      • 28. Meeker, W.Q., Escobar, L.A.: ‘Statistical methods for reliability data using SAS software’, Technometrics., 1978, 20, (3), pp. 245247.
    10. 10)
      • 18. He, J., Xiao, X., Zhong, R., et al: ‘New AC-DC hybrid power supply system and its reliability analysis in data centre’, IET J. Eng., 2018, 16, pp. 28002803.
    11. 11)
      • 25. Wang, D., Miao, Q., Zhou, Q., et al: ‘An intelligent prognostic system for gear performance degradation assessment and remaining useful life estimation’, J. Vib. Acoust., 2015, 137, (2), pp. 149161.
    12. 12)
      • 1. Moeini, R., Tricoli, P., Hemida, H., et al: ‘Increasing the reliability of wind turbines using condition monitoring of semiconductor devices: a review’, IET Renew. Power Gener., 2017, 12, (2), pp. 182189.
    13. 13)
      • 11. O'Connor, P.D.T., Harris, L.N.: ‘Reliability prediction: a state-of-the-art review’, IEE Proc. A, Phys. Sci. Meas. Instrum. Manage. Educ. Rev., 1986, 133, (4), pp. 202216.
    14. 14)
      • 27. Chen, N., Yu, R., Chen, Y., et al: ‘Hierarchical method for wind turbine prognosis using SCADA data’, IET Renew. Power Gener., 2017, 11, (4), pp. 403410.
    15. 15)
      • 12. Giorsetto, P., Utsurogi, K.: ‘Development of a new procedure for reliability modeling of wind turbine generators’, IEEE Trans. Power Appar. Syst., 1983, PAS-102, (1), pp. 134143.
    16. 16)
      • 8. Sun, Y., Wang, P., Cheng, L., et al: ‘Operational reliability assessment of power systems considering condition-dependent failure rate’, IET Gener. Transm. Distrib., 2010, 4, (1), pp. 6072.
    17. 17)
      • 17. Hong, Y., Meeker, W.Q., McCalley, J.D.: ‘Prediction of remaining life of power transformers based on left truncated and right censored lifetime data’, Ann. Appl. Stat., 2009, 3, (2), pp. 857879.
    18. 18)
      • 4. Shafiee, M., Dinmohammadi, F.: ‘An FMEA-based risk assessment approach for wind turbine systems: a comparative study of onshore and offshore’, Energies, 2014, 7, (2), pp. 619642.
    19. 19)
      • 7. Stenberg, A., Holttinen, H.: ‘Analysing failure statistics of wind turbines in Finland’. European Wind Energy Conf., Warsaw, Poland, April 2010, pp. 2023.
    20. 20)
      • 21. Okoh, C., Roy, R., Mehnen, J., et al: ‘Overview of remaining useful life prediction techniques in through-life engineering services’, Proc. CIRP, 2014, 16, pp. 158163.
    21. 21)
      • 14. Batzel, T.D., Swanson, D.C.: ‘Prognostic health management of aircraft power generators’, IEEE Trans. Aerosp. Electron. Syst., 2009, 45, (2), pp. 473483.
    22. 22)
      • 10. Kumar, V., Singh, L., Tripathi, A. K.: ‘Reliability analysis of safety-critical and control systems: a state-of-the-art review’, IET Softw., 2017, 12, (1), pp. 118.
    23. 23)
      • 16. Guo, H., Watson, S., Tavner, P., et al: ‘Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation’, Reliab. Eng. Syst. Saf., 2009, 94, (6), pp. 10571063.
    24. 24)
      • 3. Gao, Q., Liu, C., Xie, B., et al: ‘Evaluation of the mainstream wind turbine concepts considering their reliabilities’, IET Renew. Power Gener., 2012, 6, (5), pp. 348357.
    25. 25)
      • 22. Sankararaman, S., Daigle, M.J., Goebel, K.: ‘Uncertainty quantification in remaining useful life prediction using first-order reliability methods’, IEEE Trans. Reliab., 2014, 63, (2), pp. 117.
    26. 26)
      • 24. Ben Ali, J., Chebel-Morello, B., Saidi, L., et al: ‘Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network’, Mech. Syst. Signal Process., 2015, 56, pp. 150172.
    27. 27)
      • 20. Infield, D., Wang, Y.: ‘Supervisory control and data acquisition data-based non-linear state estimation technique for wind turbine gearbox condition monitoring’, IET Renew. Power Gener., 2013, 7, (4), pp. 350358.
    28. 28)
      • 31. Coria, V.H., Maximov, S., Rivas-Dávalos, F., et al: ‘Perturbative method for maximum likelihood estimation of the Weibull distribution parameters’, SpringerPlus, 2016, 5, (1), p. 1802.
    29. 29)
      • 26. Matthews, P.C., Chen, B., Tavner, P.J.: ‘Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition’, IET Renew. Power Gener., 2015, 9, (5), pp. 503513.
    30. 30)
      • 9. Spinato, F., Tavner, P.J., van Bussel, G.J.W., et al: ‘Reliability of wind turbine sub-assemblies’, IET Renew. Power Gener., 2009, 3, (4), pp. 387401.
    31. 31)
      • 30. Kaplan, E.L., Meier, P.: ‘Nonparametric estimation from incomplete observations’, J. Am. Stat. Assoc., 1958, 53, pp. 457481.
    32. 32)
      • 29. Lee, E.T., Wang, J.W.: ‘Statistical methods for survival data analysis’ (John Wiley & Sons, New York, NY, USA, 2003, 3rd edn.).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2018.5909
Loading

Related content

content/journals/10.1049/iet-rpg.2018.5909
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading