Condition monitoring of wind turbines based on cointegration analysis of gearbox and generator temperature data
| 1 | AGH University of Science and Technology |
CORRESPONDING AUTHOR
Submission date: 2017-09-27
Final revision date: 2017-12-14
Acceptance date: 2017-12-19
Online publication date: 2017-12-18
Publication date: 2018-03-12
Diagnostyka 2018;19(1):63–71
KEYWORDS
TOPICS
ABSTRACT
This paper presents a cointegration-based method for condition monitoring of wind turbines. Analysis of cointegration residuals – obtained from cointegration process of wind turbine data – is used for operational condition monitoring and fault detection. The method has been employed for on-line condition monitoring of a wind turbine drivetrain with a nominal power of 2 MW under varying environmental and operational conditions using only the temperature data of gearbox bearing and generator winding, which were collected by the Supervisory Control And Data Acquisition (SCADA) system. The results show that the proposed method can effectively monitor the wind turbine and reliably detect the gearbox fault.
REFERENCES (25)
1.
Kusiak A, Li W. The prediction and diagnosis of wind turbine faults. Renewable Energy 2011; 36(1): 6–23.
2.
Hameed Z, Hong YS, Cho YM, Ahn SH, Song CK. Condition monitoring and fault detection of wind turbines and related algorithms: a review. Renewable and Sustainable Energy Reviews 2009; 13(1): 1–39.
3.
Garcia Marquez FP, Tobias AM, Pinar Perez JM, Papaelias M. Condition monitoring of wind turbines: techniques and methods. Renewable Energy 2012; 46: 169–178. https://doi.org/10.1016/j.rene....
4.
Zaher A, McArthur SDJ, Infield DG, Patel Y. Online wind turbine fault detection through automated SCADA data analysis. Wind Energy 2009; 12(6): 574–593.
5.
Qiu Y, Feng Y, Tavner P, Richardson P, Erdos G, Chen B. Wind turbine SCADA alarm analysis for improving reliability. Wind Energy 2012; 15(8): 951–966.
6.
Yang W, Court R, Jiang J. Wind turbine condition monitoring by the approach of SCADA data analysis. Renewable Energy 2013; 53: 365–376. https://doi.org/10.1016/j.rene....
7.
Schlechtingen M, Santos IF, Achiche S. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description. Applied Soft Computing 2013; 13(1): 259–270.
8.
Engle RF, Granger CWJ. Cointegration and error-correction: representation, estimation and testing. Econometrica 1987; 55: 251–276.
9.
Johansen S. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 1988; 12(2–3): 231–254.
10.
Chen Q, Kruger U, Leung AYT. Cointegration testing method for monitoring non-stationary processes. Industrial & Engineering Chemistry Research 2009; 48: 3533–3543.
11.
Cross EJ, Worden K, Chen Q. Cointegration: A novel approach for the removal of environmental trends in structural health monitoring data. Proceedings of the Royal Society A 2011; 467: 2712–2732.
12.
Dao PB, Staszewski WJ. Cointegration approach for temperature effect compensation in Lamb wave based damage detection. Smart Materials and Structures 2013; 22(9): 095002.
13.
Dao PB. Cointegration method for temperature effect removal in damage detection based on Lamb waves. Diagnostyka 2013; 14(3): 61–67.
14.
Dao PB, Staszewski WJ. Data normalisation for Lamb wave–based damage detection using cointegration: A case study with single- and multiple-temperature trends. Journal of Intelligent Material Systems and Structures 2014; 25(7): 845–857.
15.
Dao PB, Staszewski WJ. Lamb wave based structural damage detection using cointegration and fractal signal processing. Mechanical Systems and Signal Processing 2014; 49(1–2): 285–301. https://doi.org/10.1016/j.ymss....
16.
Dao PB, Klepka A, Pieczonka L, Aymerich F, Staszewski WJ. Impact damage detection in smart composites using nonlinear acoustics - cointegration analysis for removal of undesired load effect. Smart Materials and Structures 2017; 26(3): 035012.
17.
Dao PB, Staszewski WJ, Barszcz T, Uhl T. Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data. Renewable Energy 2017; 116(B): 107–122. https://doi.org/10.1016/j.rene....
18.
Dao PB, Staszewski WJ, Klepka A. Stationarity-based approach for the selection of lag length in cointegration analysis used for structural damage detection. Computer-Aided Civil and Infrastructure Engineering 2017; 32(2): 138–153.
19.
Cross EJ, Worden K. Approaches to nonlinear cointegration with a view towards applications in SHM. Journal of Physics: Conference Series 2011; 305: 012069.
20.
Zolna K, Dao PB, Staszewski WJ, Barszcz T. Nonlinear cointegration approach for condition monitoring of wind turbines. Mathematical Problems in Engineering 2015; vol. 2015 (Article ID 978156) 11 pages.
21.
Zolna K, Dao PB, Staszewski WJ, Barszcz T. Towards homoscedastic nonlinear cointegration for structural health monitoring. Mechanical Systems and Signal Processing 2016; 75: 94–108. https://doi.org/10.1016/j.ymss....
22.
Tsay RS. Analysis of financial time series (vol. Wiley series in probability and statistics). 2nd ed. New York: Wiley Interscience; 2005.
23.
Zivot E, Wang J. Modeling financial time series with S-PLUS. 2nd ed. New York: Springer; 2006.
24.
Dickey D, Fuller W. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 1981; 49(4): 1057–1072.
25.
Kullaa J. Damage detection of the Z24 bridge using control charts. Mechanical Systems and Signal Processing 2003; 17(1): 163–170.
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