Experimental evaluation for fluxes, currents and speed estimation of induction motor

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.252968

Keywords:

induction motor, extended Kalman filter, speed estimation, rotor flux estimation, sensorless drive

Abstract

Induction motor drive control and estimation is a wide subject. The market for variable speed drives has grown dramatically in the last few years. Manufacturers have recognized the importance of not only managing the speed or torque range, but also reducing power consumption. This necessitates the development of new control algorithms and schemes to include these solutions. Indeed, the speed estimate must be employed in one or more regions of the control scheme, depending on the control objective. This concept, as well as the most common speed estimation methodologies, is investigated.

Currently, many tools can be used for the evaluation of the rotor speed without a speed sensor. By modern signal processing methods, it is possible to implement an estimation scheme with the possibility of monitoring currents and voltages. Therefore, in this paper, the concept of currents, speed and fluxes estimation based on the extended Kalman filter is proposed. By monitoring the ratio of the theoretical residual to the actual residual, the measured noise covariance matrix is recursively corrected online to make it gradually approach the real noise level. So that the filter performs the optimal estimation, improves the accuracy of the speed estimation. The effect of the load change on the currents, fluxes and speed estimation was also studied. Simulation and experimental results show that the proposed improved adaptive extended Kalman estimator has a strong ability to suppress random measurement noise. The experimental and simulation results prove the accuracy of the proposed scheme towards the state estimation of an induction motor at different load levels. It can accurately estimate the speed of the motor and has a good anti-error ability to meet the actual needs of the project.

Supporting Agency

  • The authors would like to express their gratitude to the, University of Kirkuk for their provided facilities, which helped to improve the quality of this work.

Author Biographies

Najimaldin M. Abbas, University of Kirkuk

Assistant Professor

Department of Electrical Engineering

College of Engineering

Ali Merwan Shakor, University of Kirkuk

Assistant Lecturer

Department of Electrical Engineering

College of Engineering

References

  1. Vas, P. (1992). Electrical machines and drives: a space-vector theory approach. New York: Oxford University Press, 808.
  2. Vas, P. (1990). Vector Control of AC Machines. New York: Oxford University Press.
  3. Alonge, F., D’Ippolito, F., Sferlazza, A. (2014). Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation. IEEE Transactions on Industrial Electronics, 61 (3), 1444–1453. doi: https://doi.org/10.1109/tie.2013.2257142
  4. Zhou, L., Wang, Y. (2016). Speed sensorless state estimation for induction motors: A moving horizon approach. 2016 American Control Conference (ACC). doi: https://doi.org/10.1109/acc.2016.7525249
  5. Horvath, K., Kuslits, M. (2017). Optimization-based parameter tuning of unscented Kalman filter for speed sensorless state estimation of induction machines. 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE). doi: https://doi.org/10.1109/iseee.2017.8170649
  6. Bazylev, D. N., Doria-Cerezo, A., Pyrkin, A. A., Bobtsov, A. A., Ortega, R. (2017). A new Approach For flux and rotor resistance estimation of induction motors. IFAC-PapersOnLine, 50 (1), 1885–1890. doi: https://doi.org/10.1016/j.ifacol.2017.08.259
  7. Alonge, F., D’Ippolito, F., Fagiolini, A., Garraffa, G., Raimondi, F. M., Sferlazza, A. (2019). Tuning of Extended Kalman Filters for Sensorless Motion Control with Induction Motor. 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE). doi: https://doi.org/10.23919/eeta.2019.8804540
  8. Wang, Y., Deng, Z. (2012). An Integration Algorithm for Stator Flux Estimation of a Direct-Torque-Controlled Electrical Excitation Flux-Switching Generator. IEEE Transactions on Energy Conversion, 27 (2), 411–420. doi: https://doi.org/10.1109/tec.2012.2188139
  9. Wang, K., Chen, B., Shen, G., Yao, W., Lee, K., Lu, Z. (2014). Online Updating of Rotor Time Constant Based on Combined Voltage and Current Mode Flux Observer for Speed-Sensorless AC Drives. IEEE Transactions on Industrial Electronics, 61 (9), 4583–4593. doi: https://doi.org/10.1109/tie.2013.2288227
  10. Holtz, J., Quan, J. (2002). Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification. IEEE Transactions on Industry Applications, 38 (4), 1087–1095. doi: https://doi.org/10.1109/tia.2002.800779
  11. Staines, C. S., Asher, G. M., Sumner, M. (2006). Rotor-position estimation for induction machines at zero and low frequency utilizing zero-sequence currents. IEEE Transactions on Industry Applications, 42 (1), 105–112. doi: https://doi.org/10.1109/tia.2005.861367
  12. Rashed, M., Stronach, A. F. (2004). A stable back-EMF MRAS-based sensorless low-speed induction motor drive insensitive to stator resistance variation. IEE Proceedings - Electric Power Applications, 151 (6), 685. doi: https://doi.org/10.1049/ip-epa:20040609
  13. Park, C.-W., Kwon, W.-H. (2004). Simple and robust speed sensorless vector control of induction motor using stator current based MRAC. Electric Power Systems Research, 71 (3), 257–266. doi: https://doi.org/10.1049/ip-epa:20040609
  14. Lascu, C., Boldea, I., Blaabjerg, F. (2006). Comparative study of adaptive and inherently sensorless observers for variable-speed induction-motor drives. IEEE Transactions on Industrial Electronics, 53 (1), 57–65. doi: https://doi.org/10.1109/tie.2005.862314
  15. Krause, P. C., Wasynczuk, O., Sudhoff, S. D. (2002). Analysis of Electric Machinery and Drive Systems. Wiley-IEEE Press, 632. doi: https://doi.org/10.1109/9780470544167
  16. Manias, S. N. (2017). Power Electronics and Motor Drive Systems. Academic Press.
  17. Håland, D. (2017). Sensorless Control of Induction Motors Using an Extended Kalman Filter and Linear Quadratic Tracking. University of Agder. Available at: https://uia.brage.unit.no/uia-xmlui/handle/11250/2454405?locale-attribute=en
  18. Ouhrouche, M. A. (2002). Estimation Of Speed, Rotor Flux, And Rotor Resistance In Cage Induction Motor Using The EKF Algorithm. International Journal of Power and Energy Systems, 22, 103–109.
  19. Blaschke, F. (1972). The Principle of Field Orientation as Applied to the New Transvector Closed-Loop System for Rotating-Field Machines. Siemens Review, 5, 217–219.
  20. Li, L., Zhang, Z., Wang, C. (2019). A flexible current tracking control of sensorless induction motors via adaptive observer. ISA Transactions, 93, 180–188. doi: https://doi.org/10.1016/j.isatra.2019.02.032

Downloads

Published

2022-02-25

How to Cite

Abbas, N. M., & Shakor, A. M. (2022). Experimental evaluation for fluxes, currents and speed estimation of induction motor. Eastern-European Journal of Enterprise Technologies, 1(2(115), 85–95. https://doi.org/10.15587/1729-4061.2022.252968