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.
[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
[6] Neelam Mehala et al, “Condition monitoring methods, failure identification and analysis for Induction machines”, International Journal of Circuits, Systems and Signal Processing, Issue 1, Volume 3, 2009, pages 10-17
[7] Gérard Blanchet and Maurice Charbit, “Digital Signal and Image Processing using Matlab”, ISTE USA 2006
[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, http://hal.univ-brest.fr/docs/00/52/65/07/PDF/IREE_2008_AL-AHMAR.pdf
[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
[11] Ioannis Tsoumas et al, “A Comparative Study of Induction Motor Current Signature Analysis Techniques for Mechanical Faults Detection, SDEMPED 2005 - International Symposium on Diagnostics for Electric Machines”, Power Electronics and Drives Vienna, Austria, 7-9, September 2005
[12] Yong-Hwa Kim, “High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors, IEEE Transactions on Industrial Electronics, Vol. 60, Issue 9, pages 4103 – 4117, September 2013
[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
[15] Jian Zhang et al, “Rank Reduced ESPRIT Techniques in the Estimation of Principle Signal Components”, Proceedings 5th Australian Communications Theory Workshop, Australian National University, 2004
[16] Shawn Sheng and Jon Keller et al, “Gearbox Reliability Collaborative Update”, NREL U.S. Department of Energy, http://www.nrel.gov/docs/fy14osti/60141.pdf
[17] J. Proakis and D. Manolakis, “Digital Signal Processing : Principles, Algorithms, and Applications”, New York: Macmillan Publishing Company, 1992
[18] André Quinquis, “Digital Signal Processing using MATLAB”, ISTE Ltd, London UK, 2008
[19] Monson H. Hayes, “Statistical Digital signal processing and modeling”, John Wiley & Sons, New York, 1996
[20] R. Roy and T. Kailath, “ESPRIT-estimation of signal parameters via rotational invariance techniques,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37(7), pp. 984 –995, July 1989
[21] Fredric J. Harris, “Multirate Signal Processing for Communication Systems”, Prentice Hall, Mai 2004
[22] Joao Paulo C. L. da Costa et al, “Comparison of Model Order Selection Techniques For High-Resolution Parameter Estimation Algorithms”, 54th Internationales Wissenschaftliches Kolloquium, Technische Universität Ilmenau, Germany, 2009
[23] Janos J. Gertler, “Fault Detection and Diagnosis in Engineering Systems Basic concepts with simple examples”, Marcel Dekker Inc., New York, 1998
[24] Saad Chakkor et al., “Wind Turbine Fault Detection System in Real Time Remote Monitoring”, International Journal of Electrical and Computer Engineering (IJECE), IAES Publisher, Volume 4 No 6, December 2014
[25] H. Vincent Poor, “An Introduction to Signal Detection and Estimation”, Second Edition, Springer-Verlag texts in electrical engineering, Virginia USA 1994
[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
[6] Neelam Mehala et al, “Condition monitoring methods, failure identification and analysis for Induction machines”, International Journal of Circuits, Systems and Signal Processing, Issue 1, Volume 3, 2009, pages 10-17
[7] Gérard Blanchet and Maurice Charbit, “Digital Signal and Image Processing using Matlab”, ISTE USA 2006
[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, http://hal.univ-brest.fr/docs/00/52/65/07/PDF/IREE_2008_AL-AHMAR.pdf
[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
[11] Ioannis Tsoumas et al, “A Comparative Study of Induction Motor Current Signature Analysis Techniques for Mechanical Faults Detection, SDEMPED 2005 - International Symposium on Diagnostics for Electric Machines”, Power Electronics and Drives Vienna, Austria, 7-9, September 2005
[12] Yong-Hwa Kim, “High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors, IEEE Transactions on Industrial Electronics, Vol. 60, Issue 9, pages 4103 – 4117, September 2013
[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
[15] Jian Zhang et al, “Rank Reduced ESPRIT Techniques in the Estimation of Principle Signal Components”, Proceedings 5th Australian Communications Theory Workshop, Australian National University, 2004
[16] Shawn Sheng and Jon Keller et al, “Gearbox Reliability Collaborative Update”, NREL U.S. Department of Energy, http://www.nrel.gov/docs/fy14osti/60141.pdf
[17] J. Proakis and D. Manolakis, “Digital Signal Processing : Principles, Algorithms, and Applications”, New York: Macmillan Publishing Company, 1992
[18] André Quinquis, “Digital Signal Processing using MATLAB”, ISTE Ltd, London UK, 2008
[19] Monson H. Hayes, “Statistical Digital signal processing and modeling”, John Wiley & Sons, New York, 1996
[20] R. Roy and T. Kailath, “ESPRIT-estimation of signal parameters via rotational invariance techniques,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37(7), pp. 984 –995, July 1989
[21] Fredric J. Harris, “Multirate Signal Processing for Communication Systems”, Prentice Hall, Mai 2004
[22] Joao Paulo C. L. da Costa et al, “Comparison of Model Order Selection Techniques For High-Resolution Parameter Estimation Algorithms”, 54th Internationales Wissenschaftliches Kolloquium, Technische Universität Ilmenau, Germany, 2009
[23] Janos J. Gertler, “Fault Detection and Diagnosis in Engineering Systems Basic concepts with simple examples”, Marcel Dekker Inc., New York, 1998
[24] Saad Chakkor et al., “Wind Turbine Fault Detection System in Real Time Remote Monitoring”, International Journal of Electrical and Computer Engineering (IJECE), IAES Publisher, Volume 4 No 6, December 2014
[25] H. Vincent Poor, “An Introduction to Signal Detection and Estimation”, Second Edition, Springer-Verlag texts in electrical engineering, Virginia USA 1994
@article{"International Journal of Electrical, Electronic and Communication Sciences:69283", author = "Saad Chakkor and Mostafa Baghouri and Abderrahmane Hajraoui", title = "High Accuracy ESPRIT-TLS Technique for Wind Turbine Fault Discrimination", abstract = "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.
", keywords = "Spectral Estimation, ESPRIT-TLS, Real Time,
Diagnosis, Wind Turbine Faults, Band-Pass Filtering, Decimation.", volume = "9", number = "1", pages = "122-10", }
