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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29636

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High Accuracy ESPRIT-TLS Technique for Wind Turbine Fault Discrimination
ESPRIT-TLS method appears a good choice for high resolution fault detection in induction machines. It has a very high effectiveness in the frequency and amplitude identification. Contrariwise, it presents a high computation complexity which affects its implementation in real time fault diagnosis. To avoid this problem, a Fast-ESPRIT algorithm that combined the IIR band-pass filtering technique, the decimation technique and the original ESPRIT-TLS method was employed to enhance extracting accurately frequencies and their magnitudes from the wind stator current with less computation cost. The proposed algorithm has been applied to verify the wind turbine machine need in the implementation of an online, fast, and proactive condition monitoring. This type of remote and periodic maintenance provides an acceptable machine lifetime, minimize its downtimes and maximize its productivity. The developed technique has evaluated by computer simulations under many fault scenarios. Study results prove the performance of Fast- ESPRIT offering rapid and high resolution harmonics recognizing with minimum computation time and less memory cost.
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[1] Hamid A. Toliyat et al., “Electric Machines Modeling, Condition Monitoring, and Fault Diagnosis”, CRC Press Taylor & Francis Group NW 2013
[2] M. L. Sin, W. L. Soong and N. Ertugrul, “On-Line Condition Monitoring and Fault Diagnosis – A Survey” Australian Universities Power Engineering Conference, New Zealand, 2003
[3] K. K. Pandey et al, “Review on Fault Diagnosis in Three-Phase Induction Motor”, MEDHA – 2012, Proceedings published by International Journal of Computer Applications (IJCA)
[4] E. Al Ahmar et al, “Advanced Signal Processing Techniques for Fault Detection and Diagnosis in a Wind Turbine Induction Generator Drive Train: A Comparative Study”, IEEE Energy Conversion Congress and Exposition ECCE 2010, Atlanta United States 2010
[5] John L. Semmlow, “Biosignal and Biomedical Matlab-Based Applications”, Marcel Dekker, Inc New York 2004
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[8] Yassine Amirat et al, “Wind Turbine Bearing Failure Detection Using Generator Stator Current Homopolar Component Ensemble Empirical Mode Decomposition”, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society
[9] Elie Al-Ahmar et al, “Wind Energy Conversion Systems Fault Diagnosis Using Wavelet Analysis”, International Review of Electrical Engineering Volume 3, No 4 2008, pages: 646-652,
[10] El Houssin El Bouchikhi, Vincent Choqueuse, M.E.H. Benbouzid, “Non-stationary spectral estimation for wind turbine induction generator faults detection”, Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE 2013, pp 7376-738
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[13] Shahin Hedayati Kia et al, “A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection”, IEEE Transactions on Industrial Electronics, Vol. 54, No. 4, AUGUST 2007
[14] Saad Chakkor et al., “Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency Estimation Methods”, International Journal of Advanced Computer Science and Applications, SAI Publisher, Volume 5 No 4, May 2014, pages 139-148
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