ارزیابی جریان راه اندازی موتورهای القایی با استفاده از شبکه عصبی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشگاه صنعتی اصفهان

2 دانشگاه صنعتی مالک اشتر، اصفهان

چکیده

موتورهای القایی به صورت گسترده‌ای در صنعت مورد استفاده قرا می‌گیرند. با این وجود در طول پروسه راه‌اندازی، جریان راه‌اندازی آنها آنچنان بزرگ است که می‌تواند به تجهیزات آسیب برساند. بنابراین این جریان بایستی با دقت تخمین زده شود. در این مقاله، از شبکه عصبی مصنوعی برای ارزیابی مقدار پیک جریان راه‌اندازی موتورهای القایی استفاده می‌شود. هر دو ساختار متداول پرسپترون چندلایه (MLP) و تابع پایه‌ای شعاعی  (RBF)مورد بررسی قرار می‌گیرند. برای آموزش ساختار MLP از شش الگوریتم پس انتشار (BP)، دلتا-بار-دلتا (DBD)، دلتا-بار-دلتا توسعه‌یافته (EDBD)، جستجوی تصادفی جهت‌دار (DRS)، انتشار سریع (QP) و لونبرگ مارکواردت (LM) استفاده می‌شود. نتایج شبیه‌سازی نشان می‌دهند که هرچند اکثر شبکه‌های آموزش‌دیده قادر به تخمین مناسب مقدار پیک جریان راه‌اندازی هستند، اما الگوریتم‌هایLM  و EDBD بهترین نتیجه را بر اساس میانگین خطای نسبی و مطلق ارائه می‌دهد. این روش می‌تواند به شرکت‌های سازنده و اپراتورها برای ارزیابی مقدار پیک جریان راه‌اندازی در مرحله طراحی و بهره‌برداری کمک کند تا بتوانند تدابیر لازم را برای عملکرد ایمن موتور فراهم نمایند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Starting Current of Induction Motors Using Artificial Neural Network

نویسندگان [English]

  • Iman Sadeghkhani 1
  • Ali Reza Sadoughi 2
1 Isfahan University of Technology
2 Malek-Ashtar University of Technology
چکیده [English]

Induction motors (IMs) are widely used in industry including it be an electrical or not. However during starting period, their starting currents are so large that can damage equipment. Therefore, this current should be estimated accurately to prevent hazards caused by it. In this paper, the artificial neural network (ANN) as an intelligent tool is used to evaluate starting current peak of IMs. Both Multilayer Perceptron (MLP) and Radial Basis Function (RBF) structures have been analyzed. Six learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD), directed random search (DRS), quick propagation (QP), and levenberg marquardt (LM) were used to train the MLP. The simulation results using MATLAB show that most developed ANNs can estimate the starting current peak of IMs with good accuracy. However, it is proven that LM and EDBD algorithms present better performance for starting current evaluation based on average of relative and absolute errors.

کلیدواژه‌ها [English]

  • Induction motors
  • Multilayer Perceptron
  • radial basis function
  • starting current
[1] M. Ito, H. Okuda, N. Takahashi, T. Miyata, "Starting current analysis of three-phase squirrel-cage induction motor by finite element method", Electrical Engineering in Japan, Vol. 99, pp. 36-42, 1979.

[2] J. Buksnaitis, "Analytical Determination of Mechanical Characteristics of Asynchronous Motors by Varying the Electric Current Frequency", Electronics and Electrical Engineering (Elektronika ir Elektrotechnika), Vol. 112, pp. 3-6, 2011.

[3] Z. Yongchang, Z. Jianguo, Z. Zhengming, Xu Wei, D.G. Dorrell, "An improved direct torque control for three-level inverter-fed induction motor sensorless drive", IEEE Transactions on Power Electronics, Vol. 27, pp. 1502 - 1513, Mar. 2012.

[4] R. Natarajan, V.K. Misra, M. Oommen, "Time domain analysis of induction motor starting transients", In Proc. 21st Annual North-American Power Symposium, Vol. 17, pp. 120-128, October 1989.

[5] S. Jangjit, P. Laohachai, "Parameter estimation of three-phase induction motor by using genetic algorithm", Journal of Electrical Engineering & Technology, Vol. 4, No. 3, pp. 360-364, 2009.

[6] S.R. Shaw, S.B. Leeb, "Identification of induction motor parameters from transient stator current measurements", IEEE Transactions on Industrial Electronics, Vol. 46, No. 1, pp. 139-149, Feb. 1999.

[7] Z. Nasiri-Gheidari, H. Lesani, "Using stator discharge current for the parameter estimation of a single-phase axial flux induction motor", Scientia Iranica, Vol. 19, No. 6, pp. 1794-1801, Dec. 2012.

[8] D. Lindenmeyer, H.W. Dommel, A. Moshref, P. Kundur, "An induction motor parameter estimation method", International Journal of Electrical Power & Energy Systems, Vol. 23, No. 4, pp. 251-262, May 2001.

[9] M. Stocks, A. Medvedev, "Estimation of induction machine parameters at start-up using current envelope", in Proc. IEEE 37th IAS Annual Meeting, Conference Record of the Industry Applications Conference, Vol. 2, pp. 1163-1170, Oct. 2002.

[10] S. Aksoy, A. Muhurcu, H. Kizmaz, "State and parameter estimation in induction motor using the Extended Kalman Filtering algorithm", In Proc. International Symposium on Modern Electric Power Systems (MEPS), pp. 1-5, Sep. 2010.

[11] J. Luszcz, "Motor cable effect on the converter-fed AC motor common mode current", PRZEGLA˛D ELEKTROTECHNICZNY (Electrical Review), Vol. 88, pp. 177-181, Jan. 2012.

[12] M.K. Kirar, G. Aginhotri, "Cable sizing and effects of cable length on dynamic performance of induction motor", IEEE Fifth Power India Conference, Murthal, India, Dec. 2012.

[13] A. Ketabi, I. Sadeghkhani, "Electric power systems simulation using MATLAB", 3rd Edition, Morsal Publications Allameh Feiz Kashani Institute of Higher Education Publications, Kashan, Iran, Feb. 2014. (in Persian)

[14] P.C. Krause, O. Wasynczuk, S.D. Sudhoff, S. Pekarek, "Analysis of electric machinery and drive systems", 3rd Edition, Wiley-IEEE Press, Jul. 2013.

[15] I. Sadeghkhani, A. Ketabi, R. Feuillet, "Investigation of transmission line models for switching overvoltages studies", Int. J. of Emerging Electric Power Systems, Vol. 14, pp. 231-238, July. 2013. 

[16] L. Wang, C.N. M. Ho, F. Canales, J. Jatskevich, "High-frequency modeling of the long-cable-fed induction motor drive system using TLM approach for predicting overvoltage transients", IEEE Transactions on Power Electronics, Vol. 25, pp. 2653 – 2664, Oct. 2010.

[17] [Online]. Available: www.valiadis.gr/pool/ftp/drawings/KHV355-2_200KW_3300V_TEST_REPORT.pdf.

[18] P.C. Sen, "Principles of electric machines and power electronics", 2nd Edition, John Willey, Jan. 1997.

[19] C. Yildiz, S. Gultekin, K. Guney, S. Sagiroglu, "Neural models for the resonant frequency of electrically thin and thick circular microstrip antennas and the characteristic parameters of asymmetric coplanar waveguides backed with a conductor", AEU - International Journal of Electronics and Communications, Vol. 56, pp. 396–406, 2002.

[20] S. Haykin, "Neural network: A comprehensive foundation", 2nd ed., Prentice Hall, Upper Saddle River, NJ, USA, 1998.

[21] I. Sadeghkhani, A. Ketabi, "Switching overvoltages during restoration: Evaluation and control using ANN", Lambert Academic Publishing, Köln, Germany, Aug. 2012.

[22] R. Bayindir, S. Sagiroglu, I. Colak, "An intelligent power factor corrector for power system using artificial neural networks", Electric Power Systems Research, Vol. 79, pp. 152–160, 2009.

[23] S. Bunjongjit, A. Ngaopitakkul, "Selection of proper artificial neural networks for fault classification on single circuit transmission line", International Journal of Innovative Computing, Information and Control, Vol. 8, pp. 361-374, 2012.

[24] M.T. Hagan, M.B. Menhaj, "Training feedforward networks with the Marquardt algorithm", IEEE Trans. Neural Network, Vol. 5, pp. 989-993, Nov. 1994.