Research Article
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Year 2018, Volume: 19 Issue: 2, 293 - 302, 31.03.2018
https://doi.org/10.18038/aubtda.333118

Abstract

References

  • [1] J. D. Kueck, M. Olszewski, D. A. Casada, J. Hsu, P. J. Otaduy, and L. M. Tolbert, “Assessment of Methods for Estimating Motor Efficiency, Load Under Field Conditions,” Oak Ridge Nat. Lab., Oak Ridge, TN, Rep. ORNL/ TM-13165, 1996.
  • [2] “In-plant electric motor loading and efficiency techniques,” Ontario Hydro, Toronto, ON, Canada, Rep. TSDD-90-043, 1990.
  • [3] B. Lu, T. G. Habetler, and R. G. Harley, “A nonintrusive and in-service motor-efficiency estimation method using air-gap torque with considerations of condition monitoring,” IEEE Trans. Ind. Appl., vol. 44, no. 6, pp. 1666–1674, Nov./Dec. 2008.
  • [4] A. Charette, J. Xu, A. Ba-Razzouk, P. Pillay, and V. Rajagopalan, “The use of the genetic algorithm for in situ efficiency measurement of an induction motor,” in Proc. IEEE Power Eng. Soc. Winter Meet., 2000, pp. 392–397.
  • [5] P. Phumiphak and C. Chat-uthai, “Nonintrusive method for estimating field efficiency of inverter-fed induction motor using measured values,” in Proc. IEEE Int. Conf. Sustainable Energy Technol., 2008, pp. 580–583.
  • [6] M. S. Aspalli, S. B. Shetagar, and S. F. Kodad, “Estimation of induction motor field efficiency for energy audit and management using genetic algorithm,” in Proc. Int. Conf. Sens. Technol., 2008, pp. 440–445.
  • [7] A. Siraki and P. Pillay, “An in situ efficiency estimation technique for induction machines working with unbalanced supplies,” IEEE Trans. Energy Convers., vol. 27, no. 1, pp. 85–95, Mar. 2012.
  • [8] B. Lu, C. Wenping, I. French, K. J. Bradley, and T. G. Habetler, “Nonintrusive efficiency determination of in-service induction motors using genetic algorithm and air-gap torque methods,” in Conf. Rec. IEEE 42nd IAS Annual Meeting, 2007, pp. 1186–1192.
  • [9] T. Phumiphak and C. Chat-uthai, “Estimation of induction motor parameters based on field test coupled with genetic algorithm,” in Proc. Int. Conf. Power Syst. Technol., 2002, pp. 1199–1120. [10] P. Pillay, V. Levin, P. Otaduy, and J. Kueck, “In-situ induction motor efficiency determination using the genetic algorithm,” IEEE Trans. Energy Convers., vol. 13, no. 4, pp. 326–333, Dec. 1998.
  • [11] T. Phumiphak and C. Chat-uthai, “An economical method for induction motor field efficiency estimation for use in on-field energy audit and management,” in Proc. Int. Conf. Power Syst. Technol., 2004, pp. 1250–1254.
  • [12] J. R. Gomez, E. C. Quispe, M. A. de Armas, and P. R. Viego, “Estimation of induction motor efficiency in-situ under unbalanced voltages using genetic algorithms,” in Proc. Int. Conf. Elect. Mach., 2008, pp. 1–4.
  • [13] M. Cunkas and T. Sag, “Efficiency determination of induction motors using multi-objective evolutionary algorithms,” Adv. Eng. Software, vol. 41, no. 2, pp. 255–261, Feb. 2010.
  • [14] V. P. Sakthivel, R. Bhuvaneswari, and S. Subramanian, “Non-intrusive efficiency estimation method for energy auditing and management of inservice induction motor using bacterial foraging algorithm,” IET Elect. Power Appl., vol. 4, no. 8, pp. 579–590, Sep. 2010.
  • [15] A. Siddique, G. S. Yadava, and B. Singh, “Effects of voltage unbalance on induction motors,” in Conf. Rec. IEEE Int. Symp. Elect. Insul., 2004, pp. 26–29.
  • [16] C.-Y. Lee, “Effects of unbalanced voltage on the operation performance of a three-phase induction motor,” IEEE Trans. Energy Convers., vol. 14, no. 2, pp. 202–208, Jun. 1999.
  • [17] http://www.gamak.com/uploads/files/catalogue/Gamak-2016-Urun-katalogu-tr.pdf
  • [18] Hazewinkel, Michiel, ed. , "Covariance", Encyclopedia of Mathematics, Springer, ISBN: 978-1-55608-010-4, 2001.
  • [19] "SPSS Tutorials: Pearson Correlation", Retrieved 2017-05- 14.
  • [20] Hamzaçebi C., Kutay F. ''Yapay Sinir Ağları İle Türkiye Elektrik Enerjisi Tüketiminin 2010 Yılına Kadar Tahmini''. Gazi Üniv. Müh. Mim. Fak. Der. J. Fac. Eng. Arch. Gazi Univ. Cilt 19, No 3, 227-233, 2004.
  • [21]Werbos, P.J., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD thesis, Harvard University, 1974.
  • [22] Rumelhart, D.E., Hinton, G.E., Williams, R.J., “Learning Internal Represantation by BackPropagating Errors”, In: Rumelhart D.E., McCleland J.L., The PDP Research Group, Paralel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, MA, 1986.
  • [23] 1. Hill, T., O’Connor, M., Remus, W., “Neural Networks Models for Time Series Forecasts”, Management Sciences, Cilt 42, No 7,1082-1092, 1996.
  • [24] Sharda, R., Patil, R.B., “Connectionist Approach to Time Series Prediction: An Emprical Test”, Journal of Intelligent Manufacturing, Cilt 3, 317-323, 1992.
  • [25] Tang, Z., Almeida, C., Fishwick, P.A., “Time Series Forecasting Using Neural Networks vs Box-Jenkins Methodology”, Simulation, Cilt 57, No 5, 303-310, 1991.
  • [26] Zhang, G., Patuwo, B.E., Hu, M.Y., “Forecasting with Artificial Neural Networks: The State of the Art”, Inter. Journal of Forecasting, Cilt 14, 35- 62, 1998.

EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES

Year 2018, Volume: 19 Issue: 2, 293 - 302, 31.03.2018
https://doi.org/10.18038/aubtda.333118

Abstract

Induction motors are the most
preferable motors for the locomotives because of their simple but robust
structure. The efficiency of the preferred motor is crucial for the limitation
of the load pulled by the locomotive and suitability for the geographic
conditions.
For
this reason, determining energy efficiency and operating conditions in
induction motors is a very important issue. It is often not possible to
experimentally realize the efficiency of induction motors, because this means
that the motor is stopped during that time. This is an obstacle to the
efficiency of the operator while trying to contribute to energy efficiency in
the enterprise.



 



Therefore,
estimation the efficiency of the motor provides a significant contribution to
the operation and energy efficiency. Many studies have been made in the literature,
which related to this issue. The difference of this study is that efficency
estimations of induction motors at 17 different power are realized with
artificial neural networks and linear prediction by looking at the values of
speed, current and moment in the catalog.
And also before the estimation is applied, the
statistical relations between efficiency and moment, efficiency and speed,
efficiency and current of the motor are also analyzed and presented.

References

  • [1] J. D. Kueck, M. Olszewski, D. A. Casada, J. Hsu, P. J. Otaduy, and L. M. Tolbert, “Assessment of Methods for Estimating Motor Efficiency, Load Under Field Conditions,” Oak Ridge Nat. Lab., Oak Ridge, TN, Rep. ORNL/ TM-13165, 1996.
  • [2] “In-plant electric motor loading and efficiency techniques,” Ontario Hydro, Toronto, ON, Canada, Rep. TSDD-90-043, 1990.
  • [3] B. Lu, T. G. Habetler, and R. G. Harley, “A nonintrusive and in-service motor-efficiency estimation method using air-gap torque with considerations of condition monitoring,” IEEE Trans. Ind. Appl., vol. 44, no. 6, pp. 1666–1674, Nov./Dec. 2008.
  • [4] A. Charette, J. Xu, A. Ba-Razzouk, P. Pillay, and V. Rajagopalan, “The use of the genetic algorithm for in situ efficiency measurement of an induction motor,” in Proc. IEEE Power Eng. Soc. Winter Meet., 2000, pp. 392–397.
  • [5] P. Phumiphak and C. Chat-uthai, “Nonintrusive method for estimating field efficiency of inverter-fed induction motor using measured values,” in Proc. IEEE Int. Conf. Sustainable Energy Technol., 2008, pp. 580–583.
  • [6] M. S. Aspalli, S. B. Shetagar, and S. F. Kodad, “Estimation of induction motor field efficiency for energy audit and management using genetic algorithm,” in Proc. Int. Conf. Sens. Technol., 2008, pp. 440–445.
  • [7] A. Siraki and P. Pillay, “An in situ efficiency estimation technique for induction machines working with unbalanced supplies,” IEEE Trans. Energy Convers., vol. 27, no. 1, pp. 85–95, Mar. 2012.
  • [8] B. Lu, C. Wenping, I. French, K. J. Bradley, and T. G. Habetler, “Nonintrusive efficiency determination of in-service induction motors using genetic algorithm and air-gap torque methods,” in Conf. Rec. IEEE 42nd IAS Annual Meeting, 2007, pp. 1186–1192.
  • [9] T. Phumiphak and C. Chat-uthai, “Estimation of induction motor parameters based on field test coupled with genetic algorithm,” in Proc. Int. Conf. Power Syst. Technol., 2002, pp. 1199–1120. [10] P. Pillay, V. Levin, P. Otaduy, and J. Kueck, “In-situ induction motor efficiency determination using the genetic algorithm,” IEEE Trans. Energy Convers., vol. 13, no. 4, pp. 326–333, Dec. 1998.
  • [11] T. Phumiphak and C. Chat-uthai, “An economical method for induction motor field efficiency estimation for use in on-field energy audit and management,” in Proc. Int. Conf. Power Syst. Technol., 2004, pp. 1250–1254.
  • [12] J. R. Gomez, E. C. Quispe, M. A. de Armas, and P. R. Viego, “Estimation of induction motor efficiency in-situ under unbalanced voltages using genetic algorithms,” in Proc. Int. Conf. Elect. Mach., 2008, pp. 1–4.
  • [13] M. Cunkas and T. Sag, “Efficiency determination of induction motors using multi-objective evolutionary algorithms,” Adv. Eng. Software, vol. 41, no. 2, pp. 255–261, Feb. 2010.
  • [14] V. P. Sakthivel, R. Bhuvaneswari, and S. Subramanian, “Non-intrusive efficiency estimation method for energy auditing and management of inservice induction motor using bacterial foraging algorithm,” IET Elect. Power Appl., vol. 4, no. 8, pp. 579–590, Sep. 2010.
  • [15] A. Siddique, G. S. Yadava, and B. Singh, “Effects of voltage unbalance on induction motors,” in Conf. Rec. IEEE Int. Symp. Elect. Insul., 2004, pp. 26–29.
  • [16] C.-Y. Lee, “Effects of unbalanced voltage on the operation performance of a three-phase induction motor,” IEEE Trans. Energy Convers., vol. 14, no. 2, pp. 202–208, Jun. 1999.
  • [17] http://www.gamak.com/uploads/files/catalogue/Gamak-2016-Urun-katalogu-tr.pdf
  • [18] Hazewinkel, Michiel, ed. , "Covariance", Encyclopedia of Mathematics, Springer, ISBN: 978-1-55608-010-4, 2001.
  • [19] "SPSS Tutorials: Pearson Correlation", Retrieved 2017-05- 14.
  • [20] Hamzaçebi C., Kutay F. ''Yapay Sinir Ağları İle Türkiye Elektrik Enerjisi Tüketiminin 2010 Yılına Kadar Tahmini''. Gazi Üniv. Müh. Mim. Fak. Der. J. Fac. Eng. Arch. Gazi Univ. Cilt 19, No 3, 227-233, 2004.
  • [21]Werbos, P.J., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD thesis, Harvard University, 1974.
  • [22] Rumelhart, D.E., Hinton, G.E., Williams, R.J., “Learning Internal Represantation by BackPropagating Errors”, In: Rumelhart D.E., McCleland J.L., The PDP Research Group, Paralel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, MA, 1986.
  • [23] 1. Hill, T., O’Connor, M., Remus, W., “Neural Networks Models for Time Series Forecasts”, Management Sciences, Cilt 42, No 7,1082-1092, 1996.
  • [24] Sharda, R., Patil, R.B., “Connectionist Approach to Time Series Prediction: An Emprical Test”, Journal of Intelligent Manufacturing, Cilt 3, 317-323, 1992.
  • [25] Tang, Z., Almeida, C., Fishwick, P.A., “Time Series Forecasting Using Neural Networks vs Box-Jenkins Methodology”, Simulation, Cilt 57, No 5, 303-310, 1991.
  • [26] Zhang, G., Patuwo, B.E., Hu, M.Y., “Forecasting with Artificial Neural Networks: The State of the Art”, Inter. Journal of Forecasting, Cilt 14, 35- 62, 1998.
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mine Sertsöz

Mehmet Fidan This is me

Mehmet Kurban

Publication Date March 31, 2018
Published in Issue Year 2018 Volume: 19 Issue: 2

Cite

APA Sertsöz, M., Fidan, M., & Kurban, M. (2018). EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, 19(2), 293-302. https://doi.org/10.18038/aubtda.333118
AMA Sertsöz M, Fidan M, Kurban M. EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES. AUJST-A. June 2018;19(2):293-302. doi:10.18038/aubtda.333118
Chicago Sertsöz, Mine, Mehmet Fidan, and Mehmet Kurban. “EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 19, no. 2 (June 2018): 293-302. https://doi.org/10.18038/aubtda.333118.
EndNote Sertsöz M, Fidan M, Kurban M (June 1, 2018) EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 19 2 293–302.
IEEE M. Sertsöz, M. Fidan, and M. Kurban, “EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES”, AUJST-A, vol. 19, no. 2, pp. 293–302, 2018, doi: 10.18038/aubtda.333118.
ISNAD Sertsöz, Mine et al. “EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering 19/2 (June 2018), 293-302. https://doi.org/10.18038/aubtda.333118.
JAMA Sertsöz M, Fidan M, Kurban M. EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES. AUJST-A. 2018;19:293–302.
MLA Sertsöz, Mine et al. “EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES”. Anadolu University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 19, no. 2, 2018, pp. 293-02, doi:10.18038/aubtda.333118.
Vancouver Sertsöz M, Fidan M, Kurban M. EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES. AUJST-A. 2018;19(2):293-302.