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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

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


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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  4/2014 - 8

Detection of Inter-turn Faults in Five-Phase Permanent Magnet Synchronous Motors

SAAVEDRA, H. See more information about SAAVEDRA, H. on SCOPUS See more information about SAAVEDRA, H. on IEEExplore See more information about SAAVEDRA, H. on Web of Science, RIBA, J.-R. See more information about  RIBA, J.-R. on SCOPUS See more information about  RIBA, J.-R. on SCOPUS See more information about RIBA, J.-R. on Web of Science, ROMERAL, L. See more information about ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on Web of Science
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Download PDF pdficon (890 KB) | Citation | Downloads: 409 | Views: 2,174

Author keywords
permanent magnet motors, fault diagnosis, fault detection, fault tolerance, harmonic analysis

References keywords
magnet(14), permanent(12), machines(12), faults(11), phase(10), stator(8), induction(8), fault(8), winding(7), turn(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 49 - 54
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04008
Web of Science Accession Number: 000348772500008
SCOPUS ID: 84921628550

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Five-phase permanent magnet synchronous motors (PMSMs) have inherent fault-tolerant capabilities. This paper analyzes the detection of inter-turn short circuit faults in five-phase PMSMs in their early stage, i.e. with only one turn in short circuit by means of the analysis of the stator currents and the zero-sequence voltage component (ZSVC) spectra. For this purpose, a parametric model of five-phase PMSMs which accounts for the effects of inter-turn short circuits is developed to determine the most suitable harmonic frequencies to be analyzed to detect such faults. The amplitudes of these fault harmonic are analyzed in detail by means of finite-elements method (FEM) simulations, which corroborate the predictions of the parametric model. A low-speed five-phase PMSM for in-wheel applications is studied and modeled. This paper shows that the ZSVC-based method provides better sensitivity to diagnose inter-turn faults in the analyzed low-speed application. Results presented under a wide speed range and different load levels show that it is feasible to diagnose such faults in their early stage, thus allowing applying a post-fault strategy to minimize their effects while ensuring a safe operation.

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

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[CrossRef] [SCOPUS Times Cited 9]

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[CrossRef] [SCOPUS Times Cited 183]

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[CrossRef] [SCOPUS Times Cited 187]

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[CrossRef] [SCOPUS Times Cited 135]

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[CrossRef] [SCOPUS Times Cited 38]

[8] J.-C. Urresty, J.-R. Riba, M. Delgado, L. Romeral, "Detection of demagnetization faults in surface-mounted permanent magnet synchronous motors by means of the zero-sequence voltage component," IEEE Trans. Energy Convers., vol. 27, no. 1, pp. 42-51, Mar. 2012.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 67]

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[CrossRef] [Full Text] [SCOPUS Times Cited 5]

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[14] J.-C. Urresty, J.-R. Riba, L. Romeral, "A Back-EMF Based Method to Detect Magnet Failures in PMSMs," IEEE Trans. Magn., vol. 49, no. 1, pp. 591-598, Jan. 2013.
[CrossRef] [SCOPUS Times Cited 51]

[15] J.-H. Choi, B.-G. Gu, C.-Y. Won, "Modeling and Analysis of PMSMs under Inter Turn Short Faults," Electr. Eng. Technol., vol. 8, no. 5, pp. 1243-1250, 2013.
[CrossRef] [SCOPUS Times Cited 6]

[16] W. Tang, G. Liu, J. Ji, "Winding Turn-to-Turn Faults Detection of Five-Phase Fault-Tolerant Permanent-Magnet Machine Based on Parametric Model," in Proc. 15th International Conference on Electrical Machines and Systems (ICEMS), 2012, pp. 1-6.

[17] D. Casadei, F. Filippetti, M. Mengoni, Y. Gritli, G. Serra, A. Tani, L. Zarri, "Detection of Magnet Demagnetization in Five-Phase Surface-Mounted Permanent Magnet Generators," in Proc. 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2012, pp. 841-848.
[CrossRef] [SCOPUS Times Cited 13]

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[20] J.-C. Urresty, J.-R. Riba, L. Romeral, "Diagnosis of Interturn Faults in PMSMs Operating Under Nonstationary Conditions by Applying Order Tracking Filtering," IEEE Trans. Power Electron., vol. 28, no. 1, pp. 507-515, Jan. 2013.
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[21] B. M. Ebrahimi, J. Faiz, "Feature Extraction for Short-Circuit Fault Detection in Permanent-Magnet Synchronous Motors Using Stator-Current Monitoring," IEEE Trans. Power Electronics, vol. 25, no. 10, pp. 2673-2682, Oct. 2010.
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[22] M. A. Awadallah, M. M. Morcos, S. Gopalakrishnan, T. W. Nehl, "Detection of Stator Short Circuits in VSI-Fed Brushless DC Motors Using Wavelet Transform," IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 1-8, March 2006.
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[27] L. A. Pereira, C. C. Scharlau, L.F. Fernando Alves, S. Haffner, "Influence of Saturation on the Airgap Induction Waveform of Five-Phase Induction Machines," IEEE Trans. Energy Convers., vol. 27, no. 1, pp. 29-41, March 2012.
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References Weight

Web of Science® Citations for all references: 607 TCR
SCOPUS® Citations for all references: 2,477 TCR

Web of Science® Average Citations per reference: 20 ACR
SCOPUS® Average Citations per reference: 80 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 2018-10-20 05:32 in 205 seconds.

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