Abstract
The number of wind turbines is increasing each year, and with it, the need for methods of condition monitoring and predictive maintenance. During the last years, the number of techniques using 10 min SCADA data has grown and complex and efficient models are now available. Nevertheless, the non-stationary operating condition of wind turbines is still an issue. This paper will present a new approach using the similarity between the turbines behavior for fault detection. Then a more classical approach using data coming from a single turbine will be tested on two different generator failures to compare the results.
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References
Zaher A, McArthur SDJ, Infield DG (2009) Online wind turbine fault detection through automated SCADA data analysis. Wind Energy 12:574–593
Qui Y, Feng Y, Tavner P, Richardson P, Erdos G, Chen B (2012) Wind turbine SCADA alarm analysis for improving reliability. Wind Energy 15:951–966
Kusiak A, Li W (2011) The prediction and diagnosis of wind turbine faults. Renew Energy 36:16–23
Zimroz R, Bartelmus W, Barszcz T, Urbanek J (2014) Diagnostics of bearings in presence of strong operating conditions non-stationarity—a procedure of load-dependent features processing with application to wind turbine bearings. Mech Syst Signal Process 46(1):16–27
Yang W, Court R, Jiang J (2013) Wind turbine condition monitoring by the approach of SCADA data. Renew Energy 53:365–376
McLaughlin D (2009) Wind farm performance assessment: experience in the real world. In: Proceedings of the renewable energy world conference & expo Europe, 26–28 May 2009, KoelnMesse, Cologne
Zhang Z (2014) Comparison of data-driven and model based methodologies of wind turbine fault detection with SCADA data, EWEA March 2014
Guo P, Infield D (2012) Wind turbine generator condition-monitoring using temperature trend analysis. IEEE Trans Sustain Energy 3(1):124
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Lebranchu, A., Charbonnier, S., Berenguer, C., Prevost, F. (2016). Using SCADA Data for Fault Detection in Wind Turbines: Local Internal Model Versus Distance to a Wind Farm Reference. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_17
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DOI: https://doi.org/10.1007/978-3-319-20463-5_17
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Online ISBN: 978-3-319-20463-5
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