Implementation of a new flux rotor based on model reference adaptive system for sensorless direct torque control modified for induction motor
DOI:
https://doi.org/10.20998/2074-272X.2023.2.06Keywords:
induction motor, model reference adaptive system, sensorless speed, direct torque controlAbstract
Introduction. In order to realize an efficient speed control of induction motor, speed sensors, such as encoder, resolver or tachometer may be utilized. However, some problems appear such as, need of shaft extension, which decreases the mechanical robustness of the drive, reduce the reliability, and increase in cost. Purpose. In order to eliminate of speed sensors without losing. Several solutions to solve this problem have been suggested. Based on the motor fundamental excitation model, high frequency signal injection methods. The necessity of external hardware for signal injection and the adverse influence of injecting signal on the motor performance do not constitute an advantage for this technique. Fundamental model-based strategies method using instantaneous values of stator voltages and currents to estimate the rotor speed has been investigate. Several other methods have been proposed, such as model reference adaptive system, sliding mode observers, Luenberger observer and Kalman filter. The novelty of the proposed work consists in presenting a model reference adaptive system based speed estimator for sensorless direct torque control modified for induction motor drive. The model reference adaptive system is formed with flux rotor and the estimated stator current vector. Methods. The reference model utilizes measured current vector. On the other hand, the adjustable model uses the estimated stator current vector. The current is estimated through the solution of machine state equations. Practical value. The merits of the proposed estimator are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions. The experimental results show the efficiency of the proposed speed estimation technique. Experimental results show the effectiveness of the proposed speed estimation method at nominal speed regions and speed reversal, and good results with respect to measurement speed estimation errors obtained.
References
Maiti S., Chakraborty C. A new instantaneous reactive power based MRAS for sensorless induction motor drive. Simulation Modelling Practice and Theory, 2010, vol. 18, no. 9, pp. 1314-1326. doi: https://doi.org/10.1016/j.simpat.2010.05.005.
Chaabane H., Khodja D.E., Chakroune S., Hadji D. Model reference adaptive backstepping control of double star induction machine with extended Kalman sensorless control. Electrical Engineering & Electromechanics, 2022, no. 4, pp. 3-11. doi: https://doi.org/10.20998/2074-272X.2022.4.01.
Farhan A., Saleh A., Abdelrahem M., Kennel R., Shaltout A. High-Precision Sensorless Predictive Control of Salient-Pole Permanent Magnet Synchronous Motor based-on Extended Kalman Filter. 2019 21st International Middle East Power Systems Conference (MEPCON), 2019, pp. 226-231. doi: https://doi.org/10.1109/MEPCON47431.2019.9008188.
Ammar A., Kheldoun A., Metidji B., Ameid T., Azzoug Y. Feedback linearization based sensorless direct torque control using stator flux MRAS-sliding mode observer for induction motor drive. ISA Transactions, 2020, vol. 98, pp. 382-392. doi: https://doi.org/10.1016/j.isatra.2019.08.061.
Khamari D., Benlaloui I., Ouchen S., Makouf A., Drid S., Alaoui L.C., Ouriagli M. LPV Induction Motor Control with MRAS Speed Estimation. 2019 8th International Conference on Systems and Control (ICSC), 2019, pp. 460-464. doi: https://doi.org/10.1109/ICSC47195.2019.8950563.
Kumar R., Das S., Bhaumik A. Speed sensorless model predictive current control of doubly-fed induction machine drive using model reference adaptive system. ISA Transactions, 2019, vol. 86, pp. 215-226. doi: https://doi.org/10.1016/j.isatra.2018.10.025.
Xie H., Wang F., Zhang W., Garcia C., Rodriguez J., Kennel R. Predictive Field Oriented Control based on MRAS Current Estimator for IM Drives. 2020 IEEE 9th International Power Electronics and Motion Control Conference (IPEMC2020-ECCE Asia), 2020, pp. 1029-1032. doi: https://doi.org/10.1109/IPEMC-ECCEAsia48364.2020.9367976.
Guo X., Yin Z., Zhang Y., Bai C. Position sensorless control of PMLSM based on adaptive complex coefficient sliding mode observer. Energy Reports, 2022, vol. 8, pp. 687-695. doi: https://doi.org/10.1016/j.egyr.2022.02.271.
Babes B., Hamouda N., Kahla S., Amar H., Ghoneim S.S.M. Fuzzy model based multivariable predictive control design for rapid and efficient speed-sensorless maximum power extraction of renewable wind generators. Electrical Engineering & Electromechanics, 2022, no. 3, pp. 51-62. doi: https://doi.org/10.20998/2074-272X.2022.3.08.
Ankarao M., Kumar M.V., Dmesh P. Dynamic Performance Analysis of Reactive Power and Improved Rotor Flux Based MRAS for Induction Motor Drives Employing PI and Fuzzy Controller. 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018, pp. 892-896. doi: https://doi.org/10.1109/ICPEICES.2018.8897401.
Boukhechem I., Boukadoum A., Boukelkoul L., Lebied R. Sensorless direct power control for three-phase grid side converter integrated into wind turbine system under disturbed grid voltages. Electrical Engineering & Electromechanics, 2020, no. 3, pp. 48-57. doi: https://doi.org/10.20998/2074-272X.2020.3.08.
Agha Kashkooli M.R., Jovanovic M.G. Sensorless adaptive control of brushless doubly-fed reluctance generators for wind power applications. Renewable Energy, 2021, vol. 177, pp. 932-941. doi: https://doi.org/10.1016/j.renene.2021.05.154.
El Daoudi S., Lazrak L., El Ouanjli N., Ait Lafkih M. Sensorless fuzzy direct torque control of induction motor with sliding mode speed controller. Computers & Electrical Engineering, 2021, vol. 96, art. no. 107490. doi: https://doi.org/10.1016/j.compeleceng.2021.107490.
Zhang M., Cheng M., Zhang B. Sensorless Control of Linear Flux-Switching Permanent Magnet Motor Based on Improved MRAS. 2018 IEEE 9th International Symposium on Sensorless Control for Electrical Drives (SLED), 2018, pp. 84-89. doi: https://doi.org/10.1109/SLED.2018.8486093.
Fereka D., Zerikat M., Belaidi A. MRAS Sensorless Speed Control of an Induction Motor Drive based on Fuzzy Sliding Mode Control. 2018 7th International Conference on Systems and Control (ICSC), 2018, pp. 230-236. doi: https://doi.org/10.1109/ICoSC.2018.8587844.
Wang G., Zhang H. A new speed adaptive estimation method based on an improved flux sliding-mode observer for the sensorless control of PMSM drives. ISA Transactions, 2022, vol. 128, pp. 675-685. doi: https://doi.org/10.1016/j.isatra.2021.09.003.
Haider Khan M.S., Kumar Mallik S. Mechanical sensorless control of a rotor-tied DFIG wind energy conversion system using a high gain observer. Journal of King Saud University - Engineering Sciences. 2022, no. 04, pp. 1-9. doi: https://doi.org/10.1016/j.jksues.2022.05.005.
Khan Y.A., Verma V. Implementation of a new speed estimation technique for vector controlled switched reluctance machines. Measurement, 2022, vol. 198, art. no. 111315. https://doi.org/10.1016/j.measurement.2022.111315.
El Merrassi W., Abounada A., Ramzi M. Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer. Materials Today: Proceedings, 2021, vol. 45, pp. 7615-7621. doi: https://doi.org/10.1016/j.matpr.2021.03.081.
Laggoun Z.E.Z., Benalla H., Nebti K. A power quality enhanced for the wind turbine with sensorless direct power control under different input voltage conditions. Electrical Engineering & Electromechanics, 2021, no. 6, pp. 64-71. doi: https://doi.org/10.20998/2074-272X.2021.6.09.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 R. Saifi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.