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Open Access

Modeling Common-Mode Current Due to Asymmetric Aging of Machine Winding Insulation

Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
PC Krause and Associates, West Lafayette, IN 47906, USA
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Abstract

Machine stator winding insulation degradation is one of the main results of machine aging. It is non-negligible once this degradation process becomes asymmetric between phases. The traditional way to determine the insulation state of health is a partial discharge test. However, this method requires the system offline, which causes production loss and extra administrative burden. This paper presents an idea for better characterizing the insulation machine’s state of health using common-mode (CM) behavior in the machine-drive system. With the help of circuit decomposition methods and modeling tools, the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode (DM) quantities in the equivalent circuit. It is shown effective to represent the asymmetric aging effect from the detection of system leakage current. This paper provides an analytical method to quantify this characteristic from mathematical approaches, and a proper approximation has been made on the CM equivalent model (CEM) such that the CM behavior is accurately characterized. The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit. Researchers can adapt this method to quantify and characterize the machine insulation state of health (SOH).

Keywords

equivalent circuitcommon-modemachine decompositionmachine stator windinginsulation healthmodel approximation

References

[1]

M. Kaufhold, G. Borner, M. Eberhardt, and J. Speck, Failure mechanism of the interturn insulation of low voltage electric machines fed by pulse-controlled inverters, IEEE Electrical Insulation Magazine, vol. 12, no. 5, pp. 9–16, 1996.
Crossref Google Scholar
[2]

M. Kaufhold, H. Aninger, M. Berth, J. Speck, and M. Eberhardt, Electrical stress and failure mechanism of the winding insulation in PWM-inverter-fed low-voltage induction motors, IEEE Transactions on Industrial Electronics, vol. 47, no. 2, pp. 396–402, 2000.
Crossref Google Scholar
[3]

N. J. Jameson, M. H. Azarian, and M. Pecht, Improved electromagnetic coil insulation health monitoring using equivalent circuit model analysis, International Journal of Electrical Power &Energy Systems, vol. 119, p. 105829, 2020.
Crossref Google Scholar
[4]

J. Yang, S. B. Lee, J. Yoo, S. Lee, Y. Oh, and C. Choi, A stator winding insulation condition monitoring technique for inverter-fed machines, IEEE Transactions on Power Electronics, vol. 22, no. 5, pp. 2026–2033, 2007.
Crossref Google Scholar
[5]

K. Younsi, P. Neti, M. Shah, J. Y. Zhou, J. Krahn, K. Weeber, and C. D. Whitefield, On-line capacitance and dissipation factor monitoring of AC stator insulation, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 17, no. 5, pp. 1441–1452, 2010.
Crossref Google Scholar
[6]

D. Zheng, G. Lu, and P. Zhang, An improved online stator insulation monitoring method based on common-mode impedance spectrum considering the effect of aging position, IEEE Transactions on Industry Applications, vol. 58, no. 3, pp. 3558–3566, 2022.
Crossref Google Scholar
[7]

M. Karimi, M. Majidi, H. MirSaeedi, M. M. Arefi, and M. Oskuoee, A novel application of deep belief networks in learning partial discharge patterns for classifying corona, surface, and internal discharges, IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 3277–3287, 2019.
Crossref Google Scholar
[8]

Y. Xie, J. Zhang, F. Leonardi, A. R. Munoz, M. W. Degner, and F. Liang, Voltage stress modeling and measurement for random-wound machine windings driven by inverters, IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 3536–3548, 2020.
Crossref Google Scholar
[9]

F. Perisse, P. Werynski, and D. Roger, A new method for AC machine turn insulation diagnostic based on high frequency resonances, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 14, no. 5, pp. 1308–1315, 2007.
Crossref Google Scholar
[10]
P. Nussbaumer, C. Zoeller, T. M. Wolbank, and M. A. Vogelsberger, Transient distribution of voltages in induction machine stator windings resulting from switching of power electronics, in Proc. IECON 2013-39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria, 2013, pp. 3189–3194.
Crossref
[11]
I. Tsyokhla, A. Griffo, and J. Wang, On-line monitoring of winding insulation health using high frequency common mode voltage from PWM, in Proc. 2015 IEEE International Electric Machines & Drives Conference (IEMDC), Coeur d’Alene, ID, USA, 2015, pp. 1433–1439.
Crossref
[12]
W. R. Jensen, E. G. Strangas, and S. N. Foster, Online estimation of remaining useful life of stator insulation, in Proc. 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Tinos, Greece, 2017, pp. 635–641.
Crossref
[13]

D. Xiang, H. Li, H. Yan, Y. Zheng, N. Zhao, and B. Liu, Online monitoring of incipient turn insulation degradation for inverter-fed machine using sensitive tail component in PWM switching oscillations, IEEE Transactions on Power Electronics, vol. 36, no. 8, pp. 8730–8742, 2021.
Crossref Google Scholar
[14]

G. Zanuso and L. Peretti, Evaluation of high-frequency current ringing measurements for insulation health monitoring in electrical machines, IEEE Transactions on Energy Conversion, vol. 37, no. 4, pp. 2637–2644, 2022.
Crossref Google Scholar
[15]

H. Li, Y. Gu, D. Xiang, P. Zhang, P. Yue, and Y. Cui, Online condition monitoring of line-end coil insulation for inverter-fed machine by switching oscillation mode decomposition, IEEE Transactions on Industrial Electronics, vol. 69, no. 11, pp. 11697–11708, 2021.
Crossref Google Scholar
[16]

F. Niu, Y. Wang, S. Huang, L. Wu, X. Huang, Y. Fang, and T. Yang, An online groundwall insulation monitoring method based on transient characteristics of leakage current for inverter-fed motors, IEEE Transactions on Power Electronics, vol. 37, no. 8, pp. 9745–9753, 2022.
Crossref Google Scholar
[17]
J. E. Antón, J. M. Guerrero, and C. A. Platero, A frequency response analysis and machine learning combined method for interturn faults identification in synchronous generators, in Proc. 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Bari, Italy, 2021, pp. 1–6.
Crossref
[18]

Z. Zhao, Y. Chen, Y. Yu, M. Han, C. Tang, and C. Yao, Equivalent broadband electrical circuit of synchronous machine winding for frequency response analysis based on gray box model, IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3512–3521, 2021.
Crossref Google Scholar
[19]
F. Babaa, O. Bennis, F. Kratz, and A. Hadri-Hamida, Combined electrical faults detection and diagnosis using current signature analysis, in Proc. 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Dallas, TX, USA, 2021, pp. 52–57.
Crossref
[20]
K. K. Swarnkar, J. Rai, and S. Wadhwani, Stator inter-turn fault diagnosis by motor current signature approach, in Proc. 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2022, pp. 584–588.
Crossref
[21]
H. W. Penrose, Evaluation of asynchronous wind generator stator magnetic slot wedge and coil movement using electrical signature analysis, in Proc. 2021 IEEE Electrical Insulation Conference (EIC), Denver, CO, USA, 2021, pp. 1–4.
Crossref
[22]

P. Nussbaumer, M. Vogelsberger, and T. Wolbak, Induction machine insulation health state monitoring based on online switching transient exploitation, IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1835–1845, 2015.
Crossref Google Scholar
[23]
P. Zhang, K. Younsi, and P. Neti, A novel online stator ground-wall insulation monitoring scheme for inverter-fed AC motors, in Proc. 2013 IEEE Energy Conversion Congress and Exposition, Denver, CO, USA, 2013, pp. 3541–3547.
Crossref
[24]

F. Hassani, N. H. Faisal, R. Nish, S. Rothnie, and J. Njuguna, The impact of thermal ageing on sealing performance of HNBR packing elements in downhole installations in oilfield wellhead applications, Journal of Petroleum Science and Engineering, vol. 208, p. 109200, 2022.
Crossref Google Scholar
[25]
S. B. Lee and J. Yang, An on-line groundwall and phase to phase insulation quality assessment technique for AC machine stator windings, in Proc. 2005 40th Industry Applications Conference, Hong Kong, China, 2005, pp. 10–19.
[26]

A. Brovont, Generalized differential-common-mode decomposition for modeling conducted emissions in asymmetric power electronic systems, IEEE Transactions on Power Electronics, vol. 33, no. 8, pp. 6461–6466, 2018.
Crossref Google Scholar
[27]
J. Jatskevich and O. Wasynczuk, Automated state model generator, https://pcka.com/?page_id=80, 2023.
[28]

L. Wang, C. N. -M. Ho, F. Canales, and J. Jatskevich, High-frequency modeling of the long-cable-fed induction motor drive system using TLM approach for predicting overvoltage transients, IEEE Transactions on Power Electronics, vol. 25, no. 10, pp. 2653–2664, 2010.
Crossref Google Scholar
[29]
B. Wang, J. Wang, A. Griffo, A. I. Patel, Z. Sun, E. Chong, and R. Smitham, Permanent magnet generator turn fault detection using kalman filter technique, in Proc. 2016 IEEE Energy Conversion Congress and Exposition (ECCE), Milwaukee, WI, USA, 2016, pp. 1–8.
Crossref
[30]

A. Bellini, F. Filippetti, C. Tassoni, and G. -A. Capolino, Advances in diagnostic techniques for induction machines, IEEE Transactions on Industrial Electronics, vol. 55, no. 12, pp. 4109–4126, 2008.
Crossref Google Scholar
[31]

S. Ogasawara and H. Akagi, Modeling and damping of high-frequency leakage currents in PWM inverter-fed AC motor drive systems, IEEE Transactions on Industry Applications, vol. 32, no. 5, pp. 1105–1114, 1996.
Crossref Google Scholar
Complex System Modeling and Simulation
Volume 3 Issue 2,
June 2023
Pages 118-128
DOI: 10.23919/CSMS.2023.0004
Cite this article:
Zhao J, Brovont AD. Modeling Common-Mode Current Due to Asymmetric Aging of Machine Winding Insulation. Complex System Modeling and Simulation, 2023, 3(2): 118-128. https://doi.org/10.23919/CSMS.2023.0004

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Received: 26 September 2022
Revised: 16 January 2023
Accepted: 26 February 2023
Published: 20 June 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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