Yıl 2018, Cilt 19, Sayı 2, Sayfalar 293 - 302 2018-03-31

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

Mi̇ne Sertsöz [1] , Mehmet Fidan [2] , Mehmet Kurban [3]

128 292

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.

Efficiency Estimation, Neural Networks, Linear Prediction, Induction Motors
  • [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.
Birincil Dil en
Konular Mühendislik
Dergi Bölümü Araştırma Makalesi
Yazarlar

Yazar: Mi̇ne Sertsöz
Ülke: Turkey


Yazar: Mehmet Fidan

Yazar: Mehmet Kurban

Bibtex @araştırma makalesi { aubtda333118, journal = {Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik}, issn = {1302-3160}, eissn = {2146-0205}, address = {Eskişehir Teknik Üniversitesi}, year = {2018}, volume = {19}, pages = {293 - 302}, doi = {10.18038/aubtda.333118}, title = {EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES}, key = {cite}, author = {Sertsöz, Mi̇ne and Kurban, Mehmet and Fidan, Mehmet} }
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 Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik, 19 (2), 293-302. DOI: 10.18038/aubtda.333118
MLA Sertsöz, M , Fidan, M , Kurban, M . "EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES". Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik 19 (2018): 293-302 <http://dergipark.gov.tr/aubtda/issue/33078/333118>
Chicago Sertsöz, M , Fidan, M , Kurban, M . "EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES". Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik 19 (2018): 293-302
RIS TY - JOUR T1 - EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES AU - Mi̇ne Sertsöz , Mehmet Fidan , Mehmet Kurban Y1 - 2018 PY - 2018 N1 - doi: 10.18038/aubtda.333118 DO - 10.18038/aubtda.333118 T2 - Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik JF - Journal JO - JOR SP - 293 EP - 302 VL - 19 IS - 2 SN - 1302-3160-2146-0205 M3 - doi: 10.18038/aubtda.333118 UR - http://dx.doi.org/10.18038/aubtda.333118 Y2 - 2018 ER -
EndNote %0 Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES %A Mi̇ne Sertsöz , Mehmet Fidan , Mehmet Kurban %T EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES %D 2018 %J Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik %P 1302-3160-2146-0205 %V 19 %N 2 %R doi: 10.18038/aubtda.333118 %U 10.18038/aubtda.333118
ISNAD Sertsöz, Mi̇ne , Fidan, Mehmet , Kurban, Mehmet . "EFFICIENCY ESTIMATION OF INDUCTION MOTORS AT DIFFERENT SIZES WITH ARTIFICIAL NEURAL NETWORKS AND LINEAR ESTIMATION USING CATALOG VALUES". Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A - Uygulamalı Bilimler ve Mühendislik 19 / 2 (Mart 2018): 293-302. http://dx.doi.org/10.18038/aubtda.333118