Comparison of Auto-Associative Models Based Sensor Compensation Methods Applied for Fault Tolerant Operation in Motor Drives

Luigi Galotto; João O. P. Pinto; Luciana C. Leite; Luiz E. B. Silva; Burak Ozpineci; Bimal K. Bose

doi:10.18618/REP.20113.266274

Authors

Luigi Galotto Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil
João O. P. Pinto Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil
Luciana C. Leite Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil
Luiz E. B. Silva Federal University of Itajubá, Itajubá, MG, Brazil
Burak Ozpineci Oak Ridge National Lab, Oak Ridge, TN, USA
Bimal K. Bose The University of Tennessee, Knoxville – TN – USA

DOI:

https://doi.org/10.18618/REP.20113.266274

Keywords:

Auto-associative Models, Drive Systems, Induction Motor Control, Sensor drift compensation

Abstract

Several approaches related to fault tolerant motor control have already been proposed. However, most of them consider the sensors fault-free and work about faults in motors and actuators. Sensors are the fundamentals in any feedback control system. The bad calibration of sensors in motor drives may lead to degradation of performance and even to instability. The purpose of this work is to evaluate some models presented in recent publications to perform on-line sensor fault compensation. In a standard fault tolerant approach, the fault would be detected and the sensor would be isolated. The faulted sensor may have an off- set or scaling error and could still be used if its error is compensated. In this paper, different mathematical solution based on auto-associative models will be evaluated and compared. This technique is described and applied in indirect vector control of an induction motor. Simulated and experimental results are discussed.

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Author Biographies

Luigi Galotto, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil

was born in São Paulo, Brazil in 1981. Bachelors degree in 2003 of electrical engineering at the Federal University of Mato Grosso do Sul (UFMS), in Campo Grande, Brazil. The masters degree on Artificial Intelligent Applications was got in 2006 with the study of sensor fault tolerant operation in drive systems. Currently is finishing doctors at Paulista State University (UNESP) in power electronics. He has also been working as a researcher at an acknowledged laboratory at UFMS and developing projects in sprectrum analyzers, power converters, condition monitoring software and control systems.

João O. P. Pinto, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil

was Born in june 22, 1966 in Valparaíso, S.P., Brazil. Electrical Engineer by Paulista State University (UNESP), Ilha Solteira, Brazil, in 1990. Masters in electrical engineering by Federal University of Uberlândia, Uberlândia, Brazil, in 1993, and PhD. by The University of Tennessee, Knoxville, TN, EUA in 2001. Currently, he is professor of Federal University of Mato Grosso do Sul, Campo Grande, Brazil. Researcher of CNPq, your interest areas includes data mining, decision support systems, signal processing, artificial neural network application, fuzzy logic, genetic algorithms and wavelets in power electronics, PWM techniques, control and electrical machine drives.

Luciana C. Leite, Universidade Federal de Mato Grosso do Sul, Campo Grande, MS, Brazil

has bachelors degree in Electrical Engineering by the Júlio de Mesquita Filho Paulista State University (1993), masters in Electrical Engineering by Federal University of Uberlândia (1997) e Doctors in Electrical Engineering by State University of Campinas (2003). Currently, she is adjunct professor of the Department of Electrical Engeneering and Coordinator of the graduation program in Electrical Engeneering (PPGEE) of Federal University of Mato Grosso do Sul. Your acting areas are related with the following themes: electrical machine drives, application of artificial intelligence techniques in engineering and energetic efficiency.

Luiz E. B. Silva, Federal University of Itajubá, Itajubá, MG, Brazil

graduate at Electrical Engineering from Federal University of Itajubá (1977), master's at Electric Engineering from Federal University of Itajubá (1982) and PhD. in Electrical Engineering from Ecole Polytechnique of Montreal (1988). Has experience in Electric Engineering, focusing on Industrial Electronics, Electronic Systems and Controls, acting on the following subjects: intelligent control, power electronics, system operation, adaptive control and predictive maintenance.

Burak Ozpineci, Oak Ridge National Lab, Oak Ridge, TN, USA

received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1994, and the M.S. and Ph.D. degrees in electrical engineering from the University of Tennessee, Knoxville, in 1998 and 2002, respectively. He joined the Post-Masters Program with the Power Electronics and Electric Machinery Research Center, Oak Ridge National Laboratory (ORNL), Knoxville, TN, in 2001 and became a Full-Time Research and Development Staff Member in 2002 and the Group Leader of the Power and Energy Systems Group in 2008. Presently, he also has an Adjunct Faculty appointment with the University of Arkansas, Fayetteville. He is doing research on the system-level impact of SiC power devices, multilevel inverters, power converters for distributed energy resources and hybrid electric vehicles, and intelligent control applications for power converters.

Bimal K. Bose, The University of Tennessee, Knoxville – TN – USA

(Life Fellow, IEEE) held the Condra Chair of Excellence (Endowed Chair Professor) in Power Electronics at the University of Tennessee, Knoxville, since 1987, where he was responsible for teaching and the research program in power electronics and motor drives. Concurrently, he was the Distinguished Scientist (1989- 2000) and the Chief Scientist (1987-1989) of EPRI-Power Electronics Applications Center, Knoxville, TN. Prior to this, he was a Research Engineer in the General Electric Corporate Research and Development Center (now GE Global Research Center), Schenectady, NY, for 11 years (1976-1987), an Associate Professor of Electrical Engineering, Rensselaer Polytechnic Institute, Troy, NY for five years (1971-1976), and a faculty member at IIEST (formerly Bengal Engineering and Science University (BESU)) for 11 years (1960-1971).

References

L. Galotto, B.K. Bose, L.C. Leite, J.O.P. Pinto, L.E.B. da Silva, G.L. Torres, "Auto-Associative Neural Network Based Sensor Drift Compensation in IndirectVector Controlled Drive System", Proceedings on the Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE, pp. 1009-1014, nov, 2007. https://doi.org/10.1109/IECON.2007.4460357 DOI: https://doi.org/10.1109/IECON.2007.4460357

L. Galotto, J.O.P. Pinto, B. Ozpineci, L.C. Leite, L.E.B. da Silva, "Sensor Compensation in Motor Drives using Kernel Regression", Proceedings on the Electric Machines & Drives Conference, 2007. IEMDC '07. IEEE International, vol. 1, pp. 229-234, may, 2007. https://doi.org/10.1109/IEMDC.2007.383582 DOI: https://doi.org/10.1109/IEMDC.2007.383582

L. Galotto, Análise de Compensação De Falta Em Sensores Aplicada Em Controle De Motores, UFMS thesis, Brazil, 2006.

A. El-Antably, L. Xiaogang, R. Martin, "System simulation of fault conditions in the components of the electric drive system of an electric vehicle of an industrial drive", Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON '93., International Conference on, vol. 2, pp. 1146-1150, 1993. https://doi.org/10.1109/IECON.1993.339164 DOI: https://doi.org/10.1109/IECON.1993.339164

B.A. Welchko, T.M. Jahns, S. Hiti, "IPM synchronousmachine drive response to a single-phase open circuit fault", Power Electronics, IEEE Transactions on, vol. 17, pp. 764-771, 2002. https://doi.org/10.1109/TPEL.2002.802180 DOI: https://doi.org/10.1109/TPEL.2002.802180

N. Retiere, D. Roye, and P. Mannevy, "Vector-based investigation of induction motor drive under inverter fault operations", Power Electronics Specialists Conference, 1997. PESC '97 Record., 28th Annual IEEE, vol. 2, pp. 1288-1294, 1997. https://doi.org/10.1109/PESC.1997.616935 DOI: https://doi.org/10.1109/PESC.1997.616935

R.B. Sepe Jr., B. Fahimi, C. Morrison, J.M. Miller,"Fault-tolerant operation of induction motor drives with automatic controller reconfiguration", Electric Machines and Drives Conference, 2001. IEMDC 2001. IEEE International , pp. 156-162, 2001. https://doi.org/10.1109/IEMDC.2001.939291 DOI: https://doi.org/10.1109/IEMDC.2001.939291

D.W. Chung, S.K. Sul, "Analysis and Compensation ofCurrent Measurement Error in Vector-Controlled AC Motor Drives", Industry Applications, IEEE Transactions on, vol. 34, pp. 340-345, 1998. https://doi.org/10.1109/28.663477 DOI: https://doi.org/10.1109/28.663477

Y.S. Jeong, S.K. Sul, S.E. Schultz, N.R. Patel, "Fault Detection and Fault-Tolerant Control of Interior Permanent-Magnet Motor Drive System for Electric Vehicle", Industry Applications, IEEE Transactions on, vol. 41, pp. 46-51, 2005. https://doi.org/10.1109/TIA.2004.840947 DOI: https://doi.org/10.1109/TIA.2004.840947

S.M. Bennett, R.J. Patton, S. Daley and D.A. Newton, "Torque and Flux Estimation for a Rail Traction System in the Presence of Intermittent Sensor Faults", Control '96, UKACC International Conference on, vol. 1, 72-77, 1996. https://doi.org/10.1049/cp:19960529 DOI: https://doi.org/10.1049/cp:19960529

M.A. Kramer, "Auto-associative Neural Networks", Computers in Chemical Engineering, vol. 16, No. 4, pp. 313-328, 1992. https://doi.org/10.1016/0098-1354(92)80051-A DOI: https://doi.org/10.1016/0098-1354(92)80051-A

M. A. Kramer, "Nonlinear principles component analysis using auto-associative neural network", AIChE Journal, Vol. 37, No. 2, pp. 233-243, 1991. https://doi.org/10.1002/aic.690370209 DOI: https://doi.org/10.1002/aic.690370209

M.S. Ikbal, H. Misra, B. Yegnanarayana, "Analysis of autoassociative mapping neural networks", in International Joint Conference on Neural Network, 1999. https://doi.org/10.1109/IJCNN.1999.836037 DOI: https://doi.org/10.1109/IJCNN.1999.836037

C.G. Atkeson, A.W. Moorey, S. Schaalz. "Locally Weighted Learning", Artificial Intelligence Review, vol. 11, pp. 11-73, 1997. https://doi.org/10.1007/978-94-017-2053-3_2 DOI: https://doi.org/10.1007/978-94-017-2053-3_2

L. Galotto, J.O.P. Pinto, J.W. Hines, R.O. Sanches, B.N. Carrasco, G.S. Tatibana, "Improvement of Fault Detection with Partial Auto-Associative Models", COMADEM, pp. 357-366, 2006.

K.C. Gross, V. Bhardwaj, R. Bickford, "Proactive Detection of Software Aging Mechanisms in Performance Critical Computers", Software Engineering Workshop, 2002. Proceedings. 27th Annual NASA Goddard/IEEE, pp. 17 - 23, 2003. https://doi.org/10.1109/SEW.2002.1199445 DOI: https://doi.org/10.1109/SEW.2002.1199445

A.V. Gribok, A.M. Urmanov, J.W. Hines, "UncertaintyAnalysis of Memory Based Sensor Validation Techniques", Kluwer Academic Publishers, Real-Time Systems, vol. 27 Issue 1, may 2004. https://doi.org/10.1023/B:TIME.0000019124.24404.e9 DOI: https://doi.org/10.1023/B:TIME.0000019124.24404.e9