Active Current Sensor Fault-Tolerant Control of Induction Motor Drive with Online Neural Network-Based Rotor and Stator Resistance Estimation

• Autorzy: Adamczyk Michal , Orlowska-Kowalska Teresa

Identyfikatory

DOI

10.2478/pead-2023-0016

• Języki publikacji: EN

Abstrakty

EN

This article presents an active current sensor (CS) fault-tolerant control (FTC) strategy for induction motor (IM) drive with adaptation of rotor and stator resistances. The stator current estimator with online adaptation of resistance parameters was applied for the reconstruction of missing current signals. A model reference adaptive system (MRAS), based on a neural network (NN), was used to estimate the rotor resistance. Additionally, stator resistance estimation was applied based on ratio index. The use of such a solution allowed for a significant increase in the quality of stator current reconstruction, which is particularly important for the design of CS fault detection (FD) and compensation algorithms. A wide range of simulation studies have been carried out for different operating conditions of the IM drive. The results showed that applying rotor and stator resistance estimation can improve the quality of stator current estimation by up to approximately 95% under rated operating point. The study was carried out for nominal and low speeds, with two, one, and without healthy CS.

Słowa kluczowe

EN

induction motor; fault-tolerant control; current sensor; resistances adaptation; neural network

• Wydawca: De Gruyter
• Czasopismo: Power Electronics and Drives
• Rocznik: 2023
• Tom: Vol. 8 (43)
• Strony: 235--251
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Adamczyk Michal

• Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wroclaw, Poland

• https://orcid.org/0000-0002-8716-8919

autor

Orlowska-Kowalska Teresa

• Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wroclaw, Poland

• https://orcid.org/0000-0002-4592-5336

Bibliografia

• Adamczyk, M. (2020). Rotor Resistance Estimator based on Virtual Current Sensor Algorithm for Induction Motor Drives. Power Electronics and Drives, 5(40), pp. 143-156.
• Adamczyk, M. and Orlowska-Kowalska, T. (2019). Virtual Current Sensor in the Fault-Tolerant Field Oriented Control Structure of an Induction Motor Drive. Sensors, 19(22), p. 4979.
• Adamczyk, M. and Orlowska-Kowalska, T. (2021). Current sensors fault detection and tolerant control for induction motor drive. In: 2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC), 25-29 April 2021, Gliwice, Poland, pp. 888-892.
• Adamczyk, M. and Orlowska-Kowalska, T. (2022). Post fault Direct Field-Oriented Control of Induction Motor Drive using Adaptive Virtual Current Sensor. IEEE Transactions on Industrial Electronics, 69(4), pp. 3418-3427.
• Azzoug, Y., Sahraoui, M., Pusca, R., Ameid, T., Romary, R. and Cardoso, A. J. M. (2021). High-performance Vector Control without AC Phase Current Sensors for Induction Motor Drives: Simulation and Real-Time Implementation. ISA Transactions, 109, pp. 295-306.
• Badran, O., Sarhan, H. and Alomour, B. (2012). Thermal Performance Analysis of Induction Motor. International Journal of Heat and Technology, 30(1), pp. 75-88.
• Baghli, L., Al-Rouh, I. and Rezzoug, A. (2006). Signal Analysis and Identification for Induction Motor Sensorless Control. Control Engineering Practice, 14(11), pp. 1313-1324.
• Ben-Brahim, L. and Kurosawa, R. (1993). Identification of induction motor speed using neural networks. In: Conference Record of the Power Conversion Conference-Yokohama 1993, 19-21 April 1993, Yokohama, Japan, pp. 689–694.
• Blasdel, N. J., Wujcik, E. K., Carletta, J. E., Lee, K. S. and Monty, C. N. (2014). Fabric Nanocomposite Resistance Temperature Detector. IEEE Sensors Journal, 15(1), pp. 300-306.
• Demir, R., Barut, M., Yildiz, R., Inan, R. and Zerdali, E. (2017). EKF based rotor and stator resistance estimations for direct torque control of induction motors. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 25–27 May 2017, Brasov, Romania, pp. 376-381).
• Gonzalez, H., Rivas, R. and Rodriguez, T. (2008). Using an Artificial Neural Network as a Rotor Resistance Estimator in the Indirect Vector Control of an Induction Motor. IEEE Latin America Transactions, 6(2), pp. 176-183.
• Jankowska, K. and Dybkowski, M. (2022). Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives based on Neural Networks. Designs, 6(1), p. 18.
• Karanayil, B., Rahman, M. F. and Grantham, C. (2007). Online Stator and Rotor Resistance Estimation Scheme using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive. IEEE Transactions on Industrial Electronics, 54(1), pp. 167-176.
• Kim, J., Ko, J., Lee, J. and Lee, Y. (2017). Rotor Flux and Rotor Resistance Estimation using Extended Luenberger-Sliding Mode Observer (ELSMO) for three Phase Induction Motor Control. Canadian Journal of Electrical and Computer Engineering, 40(3), pp. 181-188.
• Kojabadi, H. M. (2009). Active Power and MRAS based Rotor Resistance Identification of an IM Drive. Simulation Modelling Practice and Theory, 17(2), pp. 376-389.
• Kubota, H., Matsuse, K. and Nakano, T. (1993). DSP based Speed Adaptive Flux Observer of Induction Motor. IEEE Transactions on Industry Applications, 29(2), pp. 344-348.
• Mapelli, F. L., Bezzolato, A. and Tarsitano, D. (2012). A rotor resistance MRAS estimator for induction motor traction drive for electrical vehicles. In: 2012 XXth International Conference on Electrical Machines, 02-05 September 2012, Marseille, France, pp. 823-829.
• Matsuo, T. and Lipo, T. A. (1985). A Rotor Parameter Identification Scheme for Vector-Controlled Induction Motor Drives. IEEE Transactions on Industry Applications, IA-21(3), pp. 624-632.
• Najafabadi, T. A., Salmasi, F. R. and Jabehdar Maralani, P. (2010). Detection and Isolation of Speed-, DC-Link Voltage-, and Current-Sensor Faults based on an Adaptive Observer in Induction Motor Drives. IEEE Transactions on Industrial Electronics, 58(5), pp. 1662-1672.
• Orlowska-Kowalska, T. (1989). Application of Extended Luenberger Observer for Flux and Rotor Time Constant Estimation in Induction Motor Drives. IEE Proceedings D (Control Theory and Applications), 136(6), pp. 324-330.
• Orlowska-Kowalska, T. (2003). Sensoless Induction Motor Drives. Wrocław: Wroclaw University of Technology Press.
• Salmasi, F. R. (2017). A Self-Healing Induction Motor Drive with Model Free Sensor Tampering and Sensor Fault Detection, Isolation, and Compensation. IEEE Transactions on Industrial Electronics, 64(8), pp. 6105-6115.
• Shi, X. and Krishnamurthy, M. (2014). Survivable Operation of Induction Machine Drives with Smooth Transition Strategy for EV Applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2(3), pp. 609-617.
• Talla, J., Peroutka, Z., Blahnik, V. and Streit, L. (2015). Rotor and stator resistance estimation of induction motor based on augmented EKF. In: 2015 International Conference on Applied Electronics (AE), 08-09 September 2015, Pilsen, Czech Republic, pp. 253-258.
• Toliyat, H. A., Arefeen, M. S., Rahman, K. M. and Figoli, D. (1999). Rotor Time Constant Updating Scheme for a Rotor Flux-Oriented Induction Motor Drive. IEEE Transactions on Power Electronics, 14(5), pp. 850-857.
• Toliyat, H. A., Levi, E. and Raina, M. (2003). A Review of RFO Induction Motor Parameter Estimation Techniques. IEEE Transactions on Energy Conversion, 18(2), pp. 271-283.
• Zerdali, E. and Barut, M. (2018). Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors. Power Electronics and Drives, 3(38), pp. 115-127.
• Zorgani, Y. A., Jouili, M., Koubaa, Y. and Boussak, M. (2018). A Very-Low-Speed Sensorless Control Induction Motor Drive with Online Rotor Resistance Tuning by using MRAS Scheme. Power Electronics and Drives, 4(1), pp. 125-140.
• Zorgani, Y. A., Koubaa, Y. and Boussak, M. (2010). Simultaneous estimation of speed and rotor resistance in sensorless ISFOC induction motor drive based on MRAS scheme. In: The XIX International Conference on Electrical Machines ICEM 2010, 06-08 September 2010, Rome, Italy, pp. 1-6.

Uwagi

Special Section - Advanced Control Methods of Electrical Machines and Drives.

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).