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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2012 - 5

 HIGHLY CITED PAPER 

A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

AKTAS, M. See more information about AKTAS, M. on SCOPUS See more information about AKTAS, M. on IEEExplore See more information about AKTAS, M. on Web of Science
 
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Download PDF pdficon (1,037 KB) | Citation | Downloads: 858 | Views: 4,117

Author keywords
discrete wavelet transforms, wavelet packets, fault diagnosis, electric vehicles, permanent magnet motors

References keywords
motor(22), power(18), synchronous(14), permanent(14), control(14), magnet(13), electric(13), fault(12), wavelet(11), system(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-11-30
Volume 12, Issue 4, Year 2012, On page(s): 33 - 38
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.04005
Web of Science Accession Number: 000312128400005
SCOPUS ID: 84872785446

Abstract
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Full text preview
The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits) in the driving systems with Field Oriented Controlled Permanent Magnet Synchronous Motors (PMSM) of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


[15] Fernandez, R. C., Rojas, H.D., An Overview of Wavelet Transforms Application in Power Systems 14th Power System Computation Conf., PSCC'02, Sevilla, June 2002.
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[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 5]


[18] Charfi F., Al-Haddad K., Sellami F., Analysis and identification of embedded power system faults using wavelet transform, Proc. Electric Vehicle Symp. EVS, Monaco, 21 April 2005.

[19] Thuillard M., A review of wavelet networks, wavelets, fuzzy wavelets and their applications, Proc. ESIT 2000, Aachen, Germany, 14-15 September 2000.

[20] Zeraoulia M., Mohamed E-H., Benbouzid, and Diallo D., Electric Motor Drive Selection Issues for HEV Propulsion Systems: A Comparative Study, IEEE Transaction on Vehicular Technology, Vol. 55, No.6, November 2006, pp.1756-1764
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[CrossRef] [SCOPUS Times Cited 17]


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[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 86]


[26] Trabelsi M., Boussak M., Gossa M., Multiple IGBTs Open Circuit Faults Diagnosis in Voltage Source Inverter Fed Induction Motor Using Modified Slope Method, XIX International Conf. on Electrical Machines-ICEM 2010, 6-8 Sept. 2010, p.1-6.
[CrossRef] [SCOPUS Times Cited 71]


[27] Akin B., Ozturk S.B., Toliyat H.A., and Rayner M., DSP-Based Sensorless Electric Motor Fault Diagnosis Tools for Electric and Hybrid Electric Vehicle Powertrain Applications, IEEE Transaction on Vehicular Technology, Vol. 58, No. 5, June 2009, p.2150-2159.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 18]


[28] Schoen R., Habetler T., Kamran F., and Bartfield R., Motor bearing damage detection using stator current monitoring, IEEE Trans. Ind. Appl., vol. 31, no. 6, Nov./Dec. 1995, p.1274-1279.
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[29] Benbouzid M.E.H., A review of induction motors signature analysis as a medium for faults detection, IEEE Transaction Industrial Electronic, vol. 47, no. 5, Oct. 2000, pp.984-993.
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[30] Nandi S., Bharadwaj M., and Toliyat H.A., Performance analysis of a three-phase induction motor under mixed eccentricity condition, IEEE Transaction Energy Conversion, vol. 17, no. 3, Sep. 2002, pp.392-399.
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[31] Ngaopitakkul, A., and Kunakorn, A., Internal Fault Classification in Transformer Windings using Combination of Discrete Wavelet Transforms and Back-propagation Neural Networks, International Journal of Control, Automation, and Systems, vol. 4, no. 3, June 2006, pp. 365-371.

[32] Yan C., Zhang Y., Wu L., A Novel Real-Time Fault Diagnostic System by Using Strata Hierarchical Artificial Neural Network, Power and Energy Engineering Conf., APPEEC 2009, Asia-Pacific, 2009, pp.1-4.
[CrossRef] [SCOPUS Times Cited 1]


[33] Charfi F., Sellami F., Al-Haddad K., Fault diagnostic in power system using wavelet transforms and neural Networks, IEEE ISIE 2006, Montreal, Quebec, Canada, 9-12 July 2006.
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[38] Ozgen, S.P., Graphical User Interface Aided Online Fault Diagnosis of Electric Motor - DC Motor Case Study, Advances in Electrical and Computer Engineering, Volume 9, Issue 3, 2009, pp.12-17.
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[39] Sedighizadeh M., Rezazadeh A., A modified Adaptive Wavelet PID Control Based on Reinforcement Learning for Wind Energy Conversion System Control, Advances in Electrical and Computer Engineering, Volume 10, Issue 2, 2010, pp.153-159.
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[40] Maraba V.A., Kuzucuoglu A.E., PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller, Advances in Electrical and Computer Engineering, Volume 11, Issue 4, 2011, pp.23-28.
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References Weight

Web of Science® Citations for all references: 2,589 TCR
SCOPUS® Citations for all references: 3,763 TCR

Web of Science® Average Citations per reference: 63 ACR
SCOPUS® Average Citations per reference: 92 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2020-08-05 10:14 in 221 seconds.




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