Parameter Estimation and Soft Computing Techniques

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)

Abstract

The paper presents an overview of parameter estimation of three phase induction motor using different techniques. The techniques mainly described in the paper are conventional techniques and soft computing techniques. The soft computing techniques considered in the paper are fuzzy system, artificial neural network (ANN), Neuro-Fuzzy, genetic algorithms (GA) and particle swarm optimization (PSO).

Keywords

Induction motor Parameter estimation Soft computing techniques ANN GA PSO 

References

  1. 1.
    Holtz J., Quan J.: Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification. In:Proceedings of the IEEE-IAS Annual Meeting, pp. 2614–2621 (2001)Google Scholar
  2. 2.
    Ribeiro, L.A. de S., Jacobina, C.B., Lima, A.M.N., Oliveira A.C.: Parameter sensitivity of MRAC models employed in ifo controlled ac motor drive. IEEE Trans. Ind. Electron. 44, 536–545 (1997)Google Scholar
  3. 3.
    Ponci F., Cristaldi L., Faifer M., Lazzaroni M.: Innovative approach to early fault detection for induction motors. In: Proceedings of the IEEE SDEMPED, pp. 283–288 (2007)Google Scholar
  4. 4.
    Benbouzid M.: A review of induction motors signature analysis as a medium for faults detection. IEEE Trans. Ind. Electron. 47(5), 984–993 (2000)Google Scholar
  5. 5.
    Barrera, P.M. de la, Bossio, G.R., Garcia, G.O., Solsona, J.A.: Stator core fault diagnosis for induction motors based on parameters adaptation. In: Proceedings of the IEEE SDEMPED, pp. 1–6 (2009)Google Scholar
  6. 6.
    Vélez-Reyes, M, Minami, K., Verghese, G.C.: Recursive speed and parameter estimation for induction machines. In: Proceedings of the IEEE-IAS Annual Meeting, pp. 607–611 (1989)Google Scholar
  7. 7.
    Stephan, J., Bodson, M., Chiasson, J.: Real-time estimation of the parameters and fluxes of induction motors. IEEE Trans. Ind. Appl. 30, 746–759 (1994)Google Scholar
  8. 8.
    Bose B.K., Simoes M.G., Crecelius D., Rajashekara K., Martin R.: Speed sensorless hybrid vector controlled induction motor drive. In: Proceedings of the IEEE-IAS Annual Meeting, pp. 137–143 (1995)Google Scholar
  9. 9.
    Cava, M.L., Picardi, C., Ranieri, F.: Application of the extended Kalman filter to parameter and state estimation of induction motors. Int. J. Model. Simul. 9(3), 85–89 (1989)Google Scholar
  10. 10.
    Atkinson, D.J., Acarney, P., Finch, J.: Observers for induction motor state and parameter estimation. IEEE Trans. Ind. Appl. 27(6), 1119–1127 (1991)Google Scholar
  11. 11.
    Wade, S., Dunnigan, M., Williams, B.: A new method of rotor resistance estimation for vector-controlled induction machines. IEEE Trans. Ind. Electron. 44(2), 247–257 (1997)Google Scholar
  12. 12.
    Levi E.: Impact of iron loss on behaviour of vector controlled induction machines. IEEE Trans. Ind. Applicat. 31, 1287–1296 (1995)Google Scholar
  13. 13.
    Globevnik, M.: Induction motor parameters measurement at standstill. In: Proceedings of the IEEE Industry Electronics Society Annual Meeting, pp. 280–285 (1998)Google Scholar
  14. 14.
    Ruff, M., Grotstollen H.: Identification of the saturated mutual inductance of an asynchronous motor at standstill by recursive least squares algorithm. In: Proceedings of the European Conference Power Electronics Applications vol. 5, pp. 103–108 (1993)Google Scholar
  15. 15.
    Moon, S.I., Keyhani, A.: Estimation of induction machine parameters from standstill time-domain data. IEEE Trans. Ind. Appl. 30, 1606–1615 (1994)Google Scholar
  16. 16.
    Consoli, A., Fortuna L., Gallo A.: Induction motor identification by a microcomputer-based structure. IEEE Trans. Ind. Electron. IE-34, 422–428 (1987)Google Scholar
  17. 17.
    Bünte A., Grotstollen H.: Offline parameter identification of an inverter- fed induction motor at standstill. In: Proceedings of the European Conference Power Electronics Applications, pp. 3.492–3.496 (1995)Google Scholar
  18. 18.
    Kwon, W.H., Lee, C.H., Youn, K.S., Cho, G.H.: Measurement of rotor time constant taking into account magnetizing flux in the induction motor. In: Proceedings of IEEE Industry Applications Society Annual Meeting, pp. 88–92 (1994)Google Scholar
  19. 19.
    Borgard, D.E., Olsson, G., Lorenz, R.D.: Accuracy issues for parameter estimation of field oriented induction machine drives. IEEE Trans. Ind. Applicat. 31, 795–801 (1995)Google Scholar
  20. 20.
    Bertoluzzo, M., Buja, G.S., Menis, R.: Inverter voltage drop-free recursive least-squares parameter identification of a PWM inverter-fed induction motor at standstill. In: Proceedings of the IEEE International Symposium on Industry Electronics, pp. 649–654 (1997)Google Scholar
  21. 21.
    Matsuo, T., Lipo, T.A.: A rotor parameter identification scheme for vector controlled induction motor drives”, IEEE Trans. Ind. Applicat. IA-21, 624–632 (1985)Google Scholar
  22. 22.
    Loron, L., Laliberté, G.: Application of the extended Kalman filter to parameters estimation of induction motors. In: Proceedings of the European Conference Power Electronics Applications, vol. 5, pp. 85–90 (1993)Google Scholar
  23. 23.
    Zai, C., Marco, C., Lipo, T.: An extended Kalman filter approach to rotor time constant measurement in PWM induction motor drives. IEEE Trans. Ind. Appl. 28(6), 96–104 (1992)Google Scholar
  24. 24.
    Finch, J.W., Atkinson, D.J., Acarnley, P.P.: Full-order estimator for induction motor states and parameters. Proc. IEEE Power Appl. 145(3), 169–179 (1998)CrossRefGoogle Scholar
  25. 25.
    Kataoka, T., Toda, S., Sato, Y.: On-line estimation of induction motor parameters by extended Kalman filter. In: Proceedings of the European Conference Power Electronics Applications vol. 4, pp. 325–329 (1993)Google Scholar
  26. 26.
    Krishnan, R., Pillay, P.: Sensitivity analysis and comparison of parameter compensation schemes in vector controlled induction motor drives. In: Proceedings of IEEE Industrial Applications Society Annual Meeting, pp. 155–161 (1986)Google Scholar
  27. 27.
    Toliyat, H., Arefeen M.S., Rahman, K.M., Ehsani, M.: Rotor time constant updating scheme for a rotor flux oriented induction motor drive. IEEE Trans. Power Electron. 14, 850–857 (1999)Google Scholar
  28. 28.
    Akin E., Ertan H. B., and Uctug M. Y., (1994), “A method for stator resistance measurement suitable for vector control”, Proc. IEEE Ind. Electron. Soc. Annu. Meeting, pp. 2122-–2126 Google Scholar
  29. 29.
    Umanand, L., Bhat, S.: Online estimation of stator resistance of an induction motor for speed control applications. IEE Proc. Electr. Power Appl. 142, 97–103 (1995)Google Scholar
  30. 30.
    Yassine, Koubaa: Recursive identification of induction motor parameters. Simul. Model. Pract. Theory 12(2004), 363–381 (2004)Google Scholar
  31. 31.
    Saravana Kumar, R., Vinoth Kumar, K., Ray, K.K.: Fuzzy Logic based fault detection in induction machines using Lab view. IJCSNS Int. J. Comput. Sci. Netw. Secur. 9(9), 226–243 (2009)Google Scholar
  32. 32.
    Toliyat, H.A., Levi, E., Raina, M.: A review of RFO induction motor parameter estimation techniques. IEEE Trans. Energy Convers. 18(2), 271–283 (2003)Google Scholar
  33. 33.
    Loser, F., Sattler, P.: Identification and compensation of the rotor temperature of AC drives by an observer. In: Conference Record IEEE IAS Annual Meeting, pp. 532–537 (1984)Google Scholar
  34. 34.
    Tzafestas, S.G., Zikidis, K.C.: Neuro FAST: On-line neuro-fuzzy ART-based structure and parameter learning TSK model. IEEE Trans. Syst. Man Cybern. B 31, 797–802 (2001)Google Scholar
  35. 35.
    Uddin, M.N., Radwan, T.S., Rahman, M.A.: Performances of fuzzy-logic-based indirect vector control for induction motor drive. IEEE Trans. Ind. Appl. 38(5), 1219–1225 (2002)Google Scholar
  36. 36.
    Uddin, M.N., Abido, M.A., Rahman, M.A.: Development and implementation of a hybrid intelligent controller for interior permanent magnet synchronous motor drive. IEEE Trans. Ind. Appl. 40(1), 68–76 (2004)Google Scholar
  37. 37.
    Consoli, A., Cerruto, E., Raciti, A., Testa, A.: Adaptive vector control of induction motor drives based on a neuro-fuzzy approach. In: Proceedings of IEEE PESC, pp. 225–232 (1994)Google Scholar
  38. 38.
    Treetrong, J.: Induction Motor Fault Detection Based on Parameter Identification Using Genetic Algorithm. J. KMUTNB. 20(3) (2010)Google Scholar
  39. 39.
    Phumiphak, T., Chat-uthai, C.: Estimation of Induction Motor Parameters Based on Field Test Coupled with Genetic Algorithm. Mahanakorn University of Technology, Thailand (1999)Google Scholar
  40. 40.
    Holtz J.: Sensorless control of induction machines—With or without signal injection? IEEE Trans. Ind. Electron. 53(1), 7–30 (2006)Google Scholar
  41. 41.
    Telford, D., Dunnigam, M.W., Williams, B.W.: Online identification of induction machine electrical parameters for vector control loop tuning. IEEE Trans. Ind. Electron. 50(2), 253–261 (2003)Google Scholar
  42. 42.
    Abdelhadi, B., Benoudjit, A., Nait-Said, N.: Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters. IEEE Trans. Energy Convers. 20(2), 284–291 (2005)Google Scholar
  43. 43.
    Levi, E., Sokola, M., Vukosavi, S.N.: A method for magnetizing curve identification in rotor flux oriented induction machines. IEEE Trans. Energy Convers. 15, 157–162 (2000)CrossRefGoogle Scholar
  44. 44.
    Levi, E., Vukosavic, S.N.: Identification of the magnetizing curve during commissioning of a rotor flux oriented induction machine. Proc. IEEE Power Appl. 146(6), 685–693 (1999)Google Scholar
  45. 45.
    Levi, E., Sokola, M., Boglietti, A., Pastorelli, M.: Iron loss in rotor flux oriented induction machine: identification, assessment of detuning and compensation. IEEE Trans. Power Electron. 11, 698–709 (1996)CrossRefGoogle Scholar
  46. 46.
    Toliyat H. A. and Hosseiny A. A. GH.,(1993), “Parameter estimation algorithm using spectral analysis for vector controlled induction motor drives,” in Proc. IEEE Int. Symp. Ind. Electron, pp. 9095 Google Scholar
  47. 47.
    Vukosavic, S.N., Stojic, M.R.: On-line tuning of the rotor time constant for vector-controlled induction motor in position control applications. IEEE Trans. Ind. Electron. 40, 130–138 (1993)CrossRefGoogle Scholar
  48. 48.
    Toliyat, H., Arefeen, M.S., Rahman, K.M., Ehsani, M.: Rotor time constant updating scheme for a rotor flux oriented induction motor drive. IEEE Trans. Power Electron. 14, 850–857 (1999)CrossRefGoogle Scholar
  49. 49.
    Nasir Uddin, M., Wen Hao.: Development of a self-tuned neuro-fuzzy controller for induction motor drives. IEEE Trans. Ind. Appl. 43(4), 1106–1115 (2007)Google Scholar
  50. 50.
    Nemec, M., Makuc, D., Ambrožiè, V., Fišer, R.: Simplified Model of Induction Machine with Electrical Rotor Asymmetry. ICEM, Rome, Italy (2010)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineeringAgraIndia

Personalised recommendations