Estimation of the Temperatures in an Asynchronous Machine Using Extended Kalman Filter

Authors: Yi Huang, Clemens Guehmann

Abstract:

In order to monitor the thermal behavior of an asynchronous machine with squirrel cage rotor, a 9th-order extended Kalman filter (EKF) algorithm is implemented to estimate the temperatures of the stator windings, the rotor cage and the stator core. The state-space equations of EKF are established based on the electrical, mechanical and the simplified thermal models of an asynchronous machine. The asynchronous machine with simplified thermal model in Dymola is compiled as DymolaBlock, a physical model in MATLAB/Simulink. The coolant air temperature, three-phase voltages and currents are exported from the physical model and are processed by EKF estimator as inputs. Compared to the temperatures exported from the physical model of the machine, three parts of temperatures can be estimated quite accurately by the EKF estimator. The online EKF estimator is independent from the machine control algorithm and can work under any speed and load condition if the stator current is nonzero current system.

Keywords: Asynchronous machine, extended Kalman filter, resistance, simulation, temperature estimation, thermal model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1131111

References:

[1] M. O. Sonnaillon, G. Bisheimer, C. D. Angelo, and G. O. Garca, “Online sensorless induction motor temperature monitoring,” IEEE Transactions on Energy Conversion, vol. 25, no. 2, pp. 273–280, June 2010.
[2] R. Beguenane and M. E. H. Benbouzid, “Induction motors thermal monitoring by means of rotor resistance identification,” IEEE Transactions on Energy Conversion, vol. 14, no. 3, pp. 566–570, Sep 1999.
[3] P. Tavner, Condition monitoring of rotating electrical machines. IET, 2008, vol. 56.
[4] S. Ben Brahim, R. Bouallegue, J. David, T. H. Vuong, and M. David, “A wireless on-line temperature monitoring system for rotating electrical machine,” Wireless Personal Communications, pp. 1–21, 2016. (Online). Available: http://dx.doi.org/10.1007/s11277-016-3808-5
[5] S. B. Brahim, R. Bouallegue, J. David, and T. H. Vuong, “Modelling and characterization of rotor temperature monitoring system,” in 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Sept 2016, pp. 735–740.
[6] G. Welch and G. Bishop, “An introduction to the kalman filter,” Chapel Hill, NC, USA, Tech. Rep., 1995.
[7] M. Ganchev, B. Kubicek, and H. Kappeler, “Rotor temperature monitoring system,” in Electrical Machines (ICEM), 2010 XIX International Conference on, Sept 2010, pp. 1–5.
[8] O. E. E, G. Metin, and B. Seta, “Simultaneous rotor and stator resistance estimation of squirrel cage induction machine with a single extended kalman filter,” Turk. J. Elec. Eng. & Comp. Sic., 2010.
[9] Y. Du, T. G. Habetler, and R. G. Harley, “Methods for thermal protection of medium voltage induction motors - a review,” in 2008 International Conference on Condition Monitoring and Diagnosis, April 2008, pp. 229–233.
[10] Z. Gao, T. G. Habetler, and R. G. Harley, “An online adaptive stator winding temperature estimator based on a hybrid thermal model for induction machines,” in IEEE International Conference on Electric Machines and Drives, 2005., May 2005, pp. 754–761.
[11] Z. Gao, T. G. Habetler, R. G. Harley, and R. S. Colby, “A novel online rotor temperature estimator for induction machines based on a cascading motor parameter estimation scheme,” in Diagnostics for Electric Machines, Power Electronics and Drives, 2005. SDEMPED 2005. 5th IEEE International Symposium on, Sept 2005, pp. 1–6.
[12] C. Kral, T. G. Habetler, R. G. Harley, F. Pirker, G. Pascoli, H. Oberguggenberger, and C. J. M. Fenz, “Rotor temperature estimation of squirrel-cage induction motors by means of a combined scheme of parameter estimation and a thermal equivalent model,” IEEE Transactions on Industry Applications, vol. 40, no. 4, pp. 1049–1057, July 2004.
[13] A. Haumer, C. Kral, V. Vukovic, A. David, C. Hettfleisch, and A. Huzsvar, “A parametrization scheme for high performance thermal models of electric machines using modelica,” 2012.
[14] D. Zeng, Advances in Computer Science and Engineering. Springer Publishing Company, Incorporated, 2012.
[15] A. Haumer, C. Kral, H. Kapeller, T. Buml, and J. V. Gragger, “The advancedmachines library: Loss models for electric machines,” in Proceedings of the 7th Modelica Conference, 2009, pp. 847–854.
[16] A. H. C. Kral, “Modelica libraries for dc machines, three phase and polyphase machines,” 4th International Modelica Conference, pp. 549–558, March 2005.
[17] G. Fish, “Dymola-simulink interface,” 2011. (Online). Available: http://www.claytex.com/blog/dymola-simulink-interface/