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Correlation analysis for wind speed and failure rate of wind turbines using time series approach
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1. K. G. Xie and B. Roy, Electr. Power Syst. Res. 79, 687 (2009).
2. A. Kusiak, and W. Y. Li, Renewable Energy 36, 16 (2011).
3. A. Underbrink, J. Hanson, A. Osterholt, and W. Zimmermann, “Probabilistic reliability calculations for the grid connection of an offshore wind farm,” in 9th International Conference on Probabilistic Methods Applied to Power Systems, Stockholm, Swedish, 2006, pp.15.
4. Z. G. Tian, T. D. Jin, B. R. Wu, and F. F. Ding, Renewable Energy 36, 1502 (2011).
5. C. A. Walford, “Wind turbine reliability: Understanding and minimizing wind turbine operation and maintenance costs,” Sandia National Laboratories, Report No. SAND2006-1100, 2006.
6. H. T. Guo, S. Watson, P. Tavner, and J. P. Xiang, Reliab. Eng. Syst. Saf. 94, 1057(2009).
7. F. Spinato, P. J. Tavner, G. J. W. Van Bussel, and E. Koutoulakos, IET Renewable Power Gener. 3, 387 (2009).
8. P. J. Tavner, J. Xiang, and F. Spinato, Wind Energy 10, 1 (2007).
9. D. O. Leonardo, and B. Biswajit, Eng. Struct. 30, 885 (2008).
10. P. J. Tavner, C. Edwards, A. Brinkman, and F. Spinato, Wind Eng. 30, 55 (2006).
11. P. J. Tavner, B. Hahn, and R. Gindele, M. W. G. Whittle, S. Faulstich, and D. M. Greenwood, “Study of effects of weather & location on wind turbine failure rates,” in European Wind Energy Conference (EWEC 2010) Wind Energy, Warsaw, Poland, 2010.
12. N. B. Negra, O. Holmstrom, B. Bak-Jensen, and P. Sorensen, IEEE Trans. Energy Convers. 22, 159 (2007).
13. A. Balouktsis, D. Chassapis, and T. D. Karapantsios, Sol. Energy 72, 251 (2002).
14. Y. F. L. Isaac and C. L. Joseph, Renewable Energy 20, 145 (2000).
15. M. Engelhardt, and L. J. Bain, IEEE Trans. Reliab. R–36, 392 (1987).
16. P. J. Tavner, J. Xiang, and F. Spinato, “Improving the reliability of wind turbine generation and its impact on overall distribution network reliability,” in 18th International Conference on Electricity Distribution, Turin, Italy, 2005.
17. R. H. Shumway and D. S. Stoffer, Time Series Analysis and its Applications: With R Examples, 2nd ed. (Springer, New York, 2006).
18. Y. H. Jiang, B. Q. Tang, Y. Qin, and W. Y. Liu, Renewable Energy 36, 2146 (2011).
19. K. Hadad, M. Pourahmadi, and H. Majidi-Maraghi, Prog. Nucl. Energy 53, 41 (2011).
20. M. Rainer, IEEE Trans. Speech Audio Process. 9, 504 (2001).
21.See for Windstats (WS).
22.See for EMD on-line.

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The correlation between wind speed and failure rate (FR) of wind turbines is analyzed with time series approach. The time series of power index (PI) and FR of wind turbines are established based on historical data, which are pretreated by singularity processing, stationarity processing, and wavelet de-noising. The trend variations of the time series are analyzed from both time domain and frequency domain by extracting the indicator functions, including auto-correlation function, cross-correlation function, and spectral density function. A case study is given out to verify the validity of the model and the method, which is based on the wind speed and failure data from January 1995 to December of 2002 in Nordjylland, Denmark. Auto-correlation function and spectral density function show that time series of PI and FR have strong seasonal characteristics and quite similar periodicity, while the cross-correlation function shows they keep high consistency and strong correlation. The results indicate that by calculating and monitoring PI, the failure rule of wind turbines can be forecast, which provides theoretical basis for preventive maintenance of wind turbines.


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Scitation: Correlation analysis for wind speed and failure rate of wind turbines using time series approach