SavedAdd your journal

Journals / Turkish Journal of Electrical Engineering and Computer Science / 2011 / Cilt: 19 - Sayı: 6

Elman neural network-based nonlinear state estimation for induction motors

Saadettin AKSOY
Aydın MÜHÜRCÜ
Pages
861–875
Publication Date
2011-06-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy and convergence speed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study presents a recurrent neural network (RNN)-based nonlinear state estimator that uses an Elman neural network structure (ENN) for state estimation of a squirrel-cage induction motor.
📊
Key Findings & Data:A squirrel-cage induction motor was fed from sinusoidal, 6-step, and pulse-width modulation (PWM) supply sources at different times in order to observe the performance of the proposed estimator for different operation conditions.
💡
Conclusion & Impact:Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy and convergence speed.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Induction motor · state estimation · extended Kalman filtering

Abstract

This study presents a recurrent neural network (RNN)-based nonlinear state estimator that uses an Elman neural network structure (ENN) for state estimation of a squirrel-cage induction motor. The proposed algorithm only uses the measurements of the stator currents and the rotor angular speed, and it learns the dynamic behavior of the state observer from these measurements through prediction error minimization. A squirrel-cage induction motor was fed from sinusoidal, 6-step, and pulse-width modulation (PWM) supply sources at different times in order to observe the performance of the proposed estimator for different operation conditions. Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy and convergence speed.

Özet

This study presents a recurrent neural network (RNN)-based nonlinear state estimator that uses an Elman neural network structure (ENN) for state estimation of a squirrel-cage induction motor. The proposed algorithm only uses the measurements of the stator currents and the rotor angular speed, and it learns the dynamic behavior of the state observer from these measurements through prediction error minimization. A squirrel-cage induction motor was fed from sinusoidal, 6-step, and pulse-width modulation (PWM) supply sources at different times in order to observe the performance of the proposed estimator for different operation conditions. Estimation results showed that the proposed algorithm is capable of estimating the states of an induction motor and performs better than extended Kalman filtering (EKF) in terms of accuracy and convergence speed.

🔖

Cite This Article

AKSOY, S., MÜHÜRCÜ, A. (2011). Elman neural network-based nonlinear state estimation for induction motors. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 19 - Sayı: 6, 861–875. https://ajindex.com/articles/188779

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. B.K. Bose, Modern Power Electronics and AC Drives, Prentice Hall, 2002.
  2. G.C. Verghese, S.R. Sanders, “Observers for flux estimation in induction machines”, IEEE Trans. on Industry Electronics, Vol. 35, pp. 85-94, 1988.
  3. M. La Cava, C. Picard, F. Ranieri, “Application of the extended Kalman Şlter to parameter and state estimation of induction motors”, Int. Journal of Modeling and Simulation, Vol. 9, pp. 56-68, 1989.
  4. L. Salvatore, S. Stasi, L. Tarchioni, “A new EKF-based algorithm for flux estimation in induction machines”, IEEE Trans. on Ind. Electr., Vol. 40, pp. 496-504, 1999.
  5. T. Yonemoto, T. Furuhashi, S. Okuma, “Design of sliding observer for robust estimation of rotor flux of induction motor”, IPEC, Tokyo, 1990. B.K. Bose, “Sliding mode control of induction motors”, IEEE IAS Ann. Meet. Conf. Rec., pp. 479-486, 1995.
  6. J. Theocharis, V. Petridis, “Neural network observer for induction motor control”, IEEE Control Systems, Vol. 14, pp. 26-37, 1994.
  7. M.G. Simoes, B.K. Bose, “Neural network based estimation of feedback signals for a vector controlled induction motor drive”, IEEE Trans. on Industry Applications, Vol. 31, pp. 620-629, 1995.
  8. N. Yadaiah, G. Sowmya, “Neural network based state estimation of dynamical systems”, Int. Joint Conference on Neural Networks, Vancouver, Canada, pp. 1042-1045, 2006.
  9. Y.C. Cheng, W.M. Qi, W.Y. Cai, “Dynamic properties of Elman and modiŞed Elman neural network”, Proceedings of the First Int. Conf. on Machine Learning and Cybernetics, Beijing, 2002. J. Elman, “Finding structure in time”, Cognitive Science, Vol. 14, pp. 179-211, 1990.
  10. N. Yadaiah, G. Sowmya, “Neural network based state estimation of dynamical systems”, International Joint Conference on Neural Networks, Vancouver, Canada, pp. 1042-1049, 2006.
  11. A. Rubaai, R. Kotaru, M.D. Kankam, “Online training of parallel neural network estimators for control of induction motors”, IEEE Transaction Industry Applications, Vol. 37, pp. 1512-1521, 2001.
  12. G. Mino-Aguilar, J.M. Moreno-Eguilaz, B. Pryymak, J. Peracaula, “A neural network based optimal rotor flux estimator for efficiency optimization of an induction motor drive”, IEEE Industrial Electronics, Vol. 3, pp. 2528-, 2006.
  13. K. Moudgalya, Digital Control, John Wiley& Sons, 2007. P.J. Werbos, “Backpropagation through time: what it does and how to do it”, Proceedings of the IEEE, Vol. 78, pp. 1550-1560, 1990.
  14. H. Guo, S.B. Gelfand, “Analysis of gradient descent learning algorithms for multilayer feedforward neural networks”, IEEE Transacti