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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 58-60Automatic Fault Detection for Wind Turbines Using...

Automatic Fault Detection for Wind Turbines Using Single-Class Machine Learning Methods

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Abstract:

This work adopts data related to the rotor efficiency of wind turbine to estimate the performance of wind turbine. To achieve this goal, two novel machine learning methods are adopted to build models for wind-turbine fault detection: one is the support vector data description (SVDD) and the other is the kernel principal component analysis (KPCA). The data collected from a normally-operating wind turbine are used to train models. In addition, we also build a health index using the KPCA reconstruction error, which can be used to predict the performance of a wind turbine when it operates online. The data used in our experiments were collected from a real wind turbine in Taiwan. Experiments results show that the model based on KPCA performs better than the one based on SVDD. The highest fault detection rate for KPCA model is higher than 98%. The results also indicate the validity of using rotor efficacy to predict the overall performance of a wind turbine.

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Periodical:

Applied Mechanics and Materials (Volumes 58-60)

Pages:

2602-2607

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.58-60.2602

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Online since:

June 2011

Authors:

Yi Hung Liu, Wei Zhi Lin, Jui Yiao Su, Yan Chen Liu

Keywords:

Fault Detection, Kernel Principal Component Analysis (KPCA), Machine Learning (ML), Support Vector Data Description, Wind Turbine (WT)

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] G. M. J. Herbert, S. Iniyan, E. Sreevalsan and S. Rajapandian: A Review of Wind Energy Technologies. Renewable and Sustainable Energy Reviews Vol. 11 (2007) p.1117–1145.

DOI: 10.1016/j.rser.2005.08.004

[2] B. Schölkopf, A. Smola and K. R. Müller: Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation Vol. 10 (1998) pp.1299-1319.

DOI: 10.1162/089976698300017467

[3] David M.J. Tax and P. W Duin: Support Vector Domain Description. Patten Recognition Letter Vol. 20 (1999) pp.1191-1199.

DOI: 10.1016/s0167-8655(99)00087-2

[4] David M.J. Tax and P. W Duin: Support Vector Data Description. Machine Learning Vol. 54 (2004) pp.45-66.

DOI: 10.1023/b:mach.0000008084.60811.49

[5] Y. H. Liu, S. H. Lin, Y. L. Hsueh and M. J. Lee: Automatic Target Defect Identification for TFT-LCD Array Process Inspection Using Kernel FCM-based Fuzzy SVDD Ensemble. Expert Systems with Applications Vol. 36 (2009) p.1978-(1998).

DOI: 10.1016/j.eswa.2007.12.015

[6] A. Kusiak, H. Zheng and Z. Song: Models for Monitoring Wind Farm Power. Renewable Energy Vol. 34 (2009) pp.583-590.

DOI: 10.1016/j.renene.2008.05.032

[7] H. Hoffmann: Kernel PCA for Novelty Detection. Pattern Recognition Vol. 40 (2007) p.863–874.

DOI: 10.1016/j.patcog.2006.07.009

[8] Y. H. Liu, Y. C. Liu and Y. Z. Chen: Fast Support Vector Data Descriptions for Novelty Detection. IEEE Trans. Neural Networks 21 (2010) pp.1296-1313.

DOI: 10.1109/tnn.2010.2053853

[9] Y. H. Liu, Y. C. Liu and Y. Z. Chen: High-speed Inline Defect Inspection for TFT-LCD Array Process Using a Novel Support Vector Data Description. Expert Systems with Applications Vol. 38 (2011) pp.6222-6231.

DOI: 10.1016/j.eswa.2010.11.046

[10] T. Bouno, T. Yuji, T. Hamada and T. Hideaki: Failure Forecast Diagnosis of Small Wind Turbine using Acoustic Emission Sensor. KIEE International Transactions on Electrical Machinery and Energy Conversion Systems Vol. 5-B (2005) pp.78-83.

[11] W.Q. Jeffries, J .A. Chambers and D.G. lnfield: Experience with Bicoherence of Electrical Power for Condition Monitoring of Wind Turbine Blades. IEE Proceedings of Vision, Image and Signal Processing Vol 145 (1998) pp.141-148.

DOI: 10.1049/ip-vis:19982013

[12] Z. Hameed, Y.S. Hong, Y.M. Cho, S.H. Ahn and C. K. Song: Condition Monitoring and Fault Detection of Wind Turbines and Related Algorithms: A Review. Renewable and Sustainable Energy Reviews Vol. 13 (2009) p.1–39.

DOI: 10.1016/j.rser.2007.05.008

[13] C.C. Ciang, J.R. Lee and H.J. Bang: Structural health monitoring for a wind turbine system: a review of damage detection methods. Meas. Sci. Technol. Vol. 19 (2008).

DOI: 10.1088/0957-0233/19/12/122001

[14] L. M. Popa, F. Blaabjerg and I. Boldea: Wind Turbine Generator Modeling and Simulation Where Rotational Speed is the Controlled Variable. IEEE Tran. Industry Applications Vol. 40 (2004).

DOI: 10.1109/tia.2003.821810

[15] J. B. Ekanayake, L. Holdsworth, X.G. Wu and N. Jenkins: Dynamic Modeling of Doubly Fed Induction Generator Wind Turbines. IEEE Transactions on Power System Vol. 18 (2003).

DOI: 10.1109/tpwrs.2003.811178

[16] Y. Amirat, M.E.H. Benbouzid, B. Bensaker and R. Wamkeue: Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems: A Review. Conference on Electric Machines and Drives (2007).

DOI: 10.1109/iemdc.2007.383639