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ASM’nin MUS tabanlı hız-algılayıcısız öngörülü moment kontrolü

Year 2023, Volume: 12 Issue: 1, 126 - 133, 15.01.2023
https://doi.org/10.28948/ngumuh.1208031

Abstract

Bu çalışmada, asenkron motorların (ASM’lerin) yüksek başarımlı kontrolünü gerçekleştirmek için uyarlama mekanizmasında en küçük ortalama kareler (EKOK) algoritmasını kullanan modele uyarlamalı sisteme (MUS’a) dayanan hız-algılayıcısız öngörülü moment kontrol (ÖMK) tabanlı ASM sürücüsü tasarlanmıştır. Burada, EKOK uyarlamalı MUS ASM’nin stator akımları (i_sα ve i_sβ) tabanlıdır. Rotor akıları (φ_rα ve φ_rβ), rotor mekanik hızı (ω_m) ile birlikte i_sα ve i_sβ gerektiren akım model kullanılarak elde edilmiştir. Uyarlama mekanizmasında oransal-integral kullanan MUS tabanlı çalışmaların aksine, EKOK uyarlamalı MUS’da durum ve/veya parametreler her iterasyonda hesaplanan ve güncellenen ağırlık katsayıları olarak tanımlanabilir. Bu çalışmada ω_m her iterasyonda ağırlık katsayısı olarak kestirilir ve güncellenir. Ayrıca, EKOK uyarlamalı MUS geleneksel oransal-integrali kullanan MUS ile benzetim ortamında karşılaştırılmıştır. Benzetim sonuçları EKOK uyarlamasını kullanan stator akımları tabanlı MUS’un kestirim başarımını ve önerilen ÖMK tabanlı ASM sürücüsünün etkinliğini açıkça göstermektedir.

References

  • B. Reddy, G. Poddar, and B. P. Muni, ‘Parameter Estimation and Online Adaptation of Rotor Time Constant for Induction Motor Drive’, IEEE Trans. Ind. Appl., 58(2), 1416–1428, 2022. https://doi.org/10.1109 /TIA.2022.3141700.
  • I. M. Alsofyani and N. R. N. Idris, ‘Simple Flux Regulation for Improving State Estimation at Very Low and Zero Speed of a Speed Sensorless Direct Torque Control of an Induction Motor’, IEEE Trans. Power Electron., 31(4), 3027–3035, 2016. https://doi.org/10.1109/TPEL.2015.2447731.
  • M. A. Usta, H. I. Okumus, and H. Kahveci, ‘A simplified three-level SVM-DTC induction motor drive with speed and stator resistance estimation based on extended Kalman filter’, Electr. Eng., 99(2), 707–720, 2017. https://doi.org/10.1007/s00202-016-0442-x.
  • J. Rodriguez, R. M. Kennel, J. R. Espinoza, M. Trincado, C. A. Silva, and C. A. Rojas, ‘High-Performance Control Strategies for Electrical Drives: An Experimental Assessment’, IEEE Trans. Ind. Electron., 59(2), 812–820, 2012. https://doi.org/10.11 09/TIE.2011.2158778.
  • K. Wróbel, P. Serkies, and K. Szabat, ‘Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches’, Energies, 13(5), 1193, 2020. https://doi.org/10.3390 /en13051193.
  • D. Casadei, F. Profumo, G. Serra, and A. Tani, ‘FOC and DTC: two viable schemes for induction motors torque control’, IEEE Trans. Power Electron., 17(5), 779–787, 2002. https://doi.org/10.1109/TPEL.2002.80 2183.
  • F. Korkmaz, I. Topaloglu, H. Mamur, M. Ari, and I. Tarimer, ‘Reduction of torque ripple in induction motor by artificial neural multinetworks’, Turk. J. Electr. Eng. Comput. Sci., 24, 3492–3502, 2016. https://doi.org /10.3906/elk-1406-54.
  • E. Zerdali̇ and R. Demi̇r, ‘Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications’, Turk. J. Electr. Eng. Comput. Sci., 29, 223–240, 2021. https://doi.org/10.3906/elk-2005-75.
  • K. V Praveen Kumar and T. V. Kumar, ‘Enhanced direct torque control and predictive torque control strategies of an open‐End winding induction motor drive to eliminate common‐mode voltage and weighting factors’, IET Power Electron., 12(8), 1986–1997, 2019. https://doi.org/10.1049/iet-pel.2018.5599.
  • S. R. Eftekhari, S. A. Davari, P. Naderi, C. Garcia, and J. Rodriguez, ‘Robust Loss Minimization for Predictive Direct Torque and Flux Control of an Induction Motor With Electrical Circuit Model’, IEEE Trans. Power Electron., 35(5), 5417–5426, 2020. https://doi.org/10.1 109/TPEL.2019.2944190.
  • P. R. U. Guazzelli, W. C. de Andrade Pereira, C. M. R. de Oliveira, A. G. de Castro, and M. L. de Aguiar, ‘Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm’, IEEE Trans. Power Electron., 34(7), 6628–6638, 2019. https://doi.org/10.1109/TPEL.2018. 2834304.
  • F. Wang, H. Xie, Q. Chen, S. A. Davari, J. Rodriguez, and R. Kennel, ‘Parallel Predictive Torque Control for Induction Machines Without Weighting Factors’, IEEE Trans. Power Electron., 35(2), 1779–1788, 2020. https ://doi.org/10.1109/TPEL.2019.2922312.
  • J. Wang, F. Wang, Z. Zhang, S. Li, and J. Rodriguez, ‘Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems’, IEEE Trans. Ind. Inform., 13(5), 2645–2656, 2017. https://doi.org/10.1109/TII.2017.2679283.
  • S. A. Bednarz and M. Dybkowski, ‘Estimation of the Induction Motor Stator and Rotor Resistance Using Active and Reactive Power Based Model Reference Adaptive System Estimator’, Appl. Sci., 9(23), 5145, 2019. https://doi.org/10.3390/app9235145.
  • I. Vicente, A. Endeman, X. Garin, and M. Brown, ‘Comparative study of stabilising methods for adaptive speed sensorless full-order observers with stator resistance estimation’, IET Control Theory Appl., 4(6), 993–1004, 2010. https://doi.org/10.1049/iet-cta.2008 .0506.
  • Y. Zhang, Z. Yin, Y. Zhang, J. Liu, and X. Tong, ‘A Novel Sliding Mode Observer With Optimized Constant Rate Reaching Law for Sensorless Control of Induction Motor’, IEEE Trans. Ind. Electron., 67(7), 5867–5878, 2020. https://doi.org/10.1109/TIE.2019.29 42577.
  • R. Yildiz, M. Barut, and E. Zerdali, ‘A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors’, IEEE Trans. Ind. Inform., 16(10), 6423–6432, 2020. https://doi.org/10.1109/TII.2020.29 64876.
  • C. Schauder, ‘Adaptive speed identification for vector control of induction motors without rotational transducers’, IEEE Trans. Ind. Appl., 28(5), 1054–1061, 1992. https://doi.org/10.1109/28.158829.
  • V. Vasic, S. N. Vukosavic, and E. Levi, ‘A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives’, IEEE Trans. Energy Convers., 18(4), 476–483, 2003. https://doi .org10.1109/TEC.2003.816595.
  • M. N. Gayathri, S. Himavathi, and R. Sankaran, Performance enhancement of vector controlled drive with rotor flux based MRAS rotor resistance estimator. International Conference on Computer Communication and Informatics (ICCCI -2012), pp. 1–6, Coimbatore, India, 2012.
  • F. L. Mapelli, D. Tarsitano, and F. Cheli, ‘MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results’, Electr. Power Syst. Res., 146, 298–307, 2017. https://doi.org/10.1016/j.epsr.2017.02. 005.
  • R. Demir and M. Barut, ‘Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control’, Trans. Inst. Meas. Control, 40(13), 3884–3898, 2018. https://doi.org/10.1177/01423312177346 31.
  • A. V. R. Teja, C. Chakraborty, S. Maiti, and Y. Hori, ‘A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives’, IEEE Trans. Ind. Electron., 59(10), 3757–3767, 2012. https://doi.org/10.1109/TIE.2011.2164769.
  • S. Basak, A. V. Ravi Teja, C. Chakraborty, and Y. Hori, A new model reference adaptive formulation to estimate stator resistance in field oriented induction motor drive. 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), pp. 8470–8475, Vienna, Austria, 2013.
  • T. Orlowska-Kowalska and M. Dybkowski, ‘Stator-Current-Based MRAS Estimator for a Wide Range Speed-Sensorless Induction-Motor Drive’, IEEE Trans. Ind. Electron., 57(4), 1296–1308, 2010. https:// doi.org/10.1109/TIE.2009.2031134.
  • M. Barut, S. Bogosyan, and M. Gokasan, ‘Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters’, IEEE Trans. Ind. Electron., 54(1), 272–280, 2007. https://doi.org/10.1109/TIE.200 6.885123.
  • S. Haykin, Adaptive filter theory (3rd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1996.
  • R. Yildiz, R. Demir, and M. Barut, Speed-sensorless predictive torque control of IM based on the adaptive fading extended Kalman filter. IV. International Turkic World Congress on Science and Engineering, pp. 82–93. Niğde, Turkey, 2022.
  • M. Habibullah, D. D. C. Lu, D. Xiao, J. E. Fletcher, and M. F. Rahman, ‘Predictive Torque Control of Induction Motor Sensorless Drive Fed by a 3L-NPC Inverter’, IEEE Trans. Ind. Inform., 13(1), 60–70, 2017. https:// doi.org/10.1109/TII.2016.2603922.

Speed-sensorless predictive torque control of the IM based on MRAS

Year 2023, Volume: 12 Issue: 1, 126 - 133, 15.01.2023
https://doi.org/10.28948/ngumuh.1208031

Abstract

In this study, an induction motor (IM) drive based on speed-sensorless predictive torque control (PTC) is designed to perform the high-performance control of the IMs by utilizing the least mean square (LMS) algorithm for the adaptation mechanism of the model reference adaptive system (MRAS). Here, the MRAS with LMS adaptation is based on the stator currents (i_sα and i_sβ) of the IM. Moreover, the rotor fluxes (φ_rα and φ_rβ) are obtained by the current model, which requires the rotor mechanical speed (ω_m) along with i_sα and i_sβ. In contrast to the other MRAS based studies using proportional-integral (PI) in the adaptation mechanisms to estimate state or parameter, it is possible to determine the states and/or parameters as weight coefficients in the MRAS with LMS adaptation which are calculated and updated in each iteration. Here, ω_m value is estimated and updated in each iteration as weight coefficient. Furthermore, the MRAS with LMS adaptation is compared to the MRAS using conventional PI in simulations. The simulation results clearly visualize both the estimation performance of stator current based MRAS using LMS adaptation and the effectiveness of the proposed PTC based IM drive.

References

  • B. Reddy, G. Poddar, and B. P. Muni, ‘Parameter Estimation and Online Adaptation of Rotor Time Constant for Induction Motor Drive’, IEEE Trans. Ind. Appl., 58(2), 1416–1428, 2022. https://doi.org/10.1109 /TIA.2022.3141700.
  • I. M. Alsofyani and N. R. N. Idris, ‘Simple Flux Regulation for Improving State Estimation at Very Low and Zero Speed of a Speed Sensorless Direct Torque Control of an Induction Motor’, IEEE Trans. Power Electron., 31(4), 3027–3035, 2016. https://doi.org/10.1109/TPEL.2015.2447731.
  • M. A. Usta, H. I. Okumus, and H. Kahveci, ‘A simplified three-level SVM-DTC induction motor drive with speed and stator resistance estimation based on extended Kalman filter’, Electr. Eng., 99(2), 707–720, 2017. https://doi.org/10.1007/s00202-016-0442-x.
  • J. Rodriguez, R. M. Kennel, J. R. Espinoza, M. Trincado, C. A. Silva, and C. A. Rojas, ‘High-Performance Control Strategies for Electrical Drives: An Experimental Assessment’, IEEE Trans. Ind. Electron., 59(2), 812–820, 2012. https://doi.org/10.11 09/TIE.2011.2158778.
  • K. Wróbel, P. Serkies, and K. Szabat, ‘Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches’, Energies, 13(5), 1193, 2020. https://doi.org/10.3390 /en13051193.
  • D. Casadei, F. Profumo, G. Serra, and A. Tani, ‘FOC and DTC: two viable schemes for induction motors torque control’, IEEE Trans. Power Electron., 17(5), 779–787, 2002. https://doi.org/10.1109/TPEL.2002.80 2183.
  • F. Korkmaz, I. Topaloglu, H. Mamur, M. Ari, and I. Tarimer, ‘Reduction of torque ripple in induction motor by artificial neural multinetworks’, Turk. J. Electr. Eng. Comput. Sci., 24, 3492–3502, 2016. https://doi.org /10.3906/elk-1406-54.
  • E. Zerdali̇ and R. Demi̇r, ‘Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications’, Turk. J. Electr. Eng. Comput. Sci., 29, 223–240, 2021. https://doi.org/10.3906/elk-2005-75.
  • K. V Praveen Kumar and T. V. Kumar, ‘Enhanced direct torque control and predictive torque control strategies of an open‐End winding induction motor drive to eliminate common‐mode voltage and weighting factors’, IET Power Electron., 12(8), 1986–1997, 2019. https://doi.org/10.1049/iet-pel.2018.5599.
  • S. R. Eftekhari, S. A. Davari, P. Naderi, C. Garcia, and J. Rodriguez, ‘Robust Loss Minimization for Predictive Direct Torque and Flux Control of an Induction Motor With Electrical Circuit Model’, IEEE Trans. Power Electron., 35(5), 5417–5426, 2020. https://doi.org/10.1 109/TPEL.2019.2944190.
  • P. R. U. Guazzelli, W. C. de Andrade Pereira, C. M. R. de Oliveira, A. G. de Castro, and M. L. de Aguiar, ‘Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm’, IEEE Trans. Power Electron., 34(7), 6628–6638, 2019. https://doi.org/10.1109/TPEL.2018. 2834304.
  • F. Wang, H. Xie, Q. Chen, S. A. Davari, J. Rodriguez, and R. Kennel, ‘Parallel Predictive Torque Control for Induction Machines Without Weighting Factors’, IEEE Trans. Power Electron., 35(2), 1779–1788, 2020. https ://doi.org/10.1109/TPEL.2019.2922312.
  • J. Wang, F. Wang, Z. Zhang, S. Li, and J. Rodriguez, ‘Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems’, IEEE Trans. Ind. Inform., 13(5), 2645–2656, 2017. https://doi.org/10.1109/TII.2017.2679283.
  • S. A. Bednarz and M. Dybkowski, ‘Estimation of the Induction Motor Stator and Rotor Resistance Using Active and Reactive Power Based Model Reference Adaptive System Estimator’, Appl. Sci., 9(23), 5145, 2019. https://doi.org/10.3390/app9235145.
  • I. Vicente, A. Endeman, X. Garin, and M. Brown, ‘Comparative study of stabilising methods for adaptive speed sensorless full-order observers with stator resistance estimation’, IET Control Theory Appl., 4(6), 993–1004, 2010. https://doi.org/10.1049/iet-cta.2008 .0506.
  • Y. Zhang, Z. Yin, Y. Zhang, J. Liu, and X. Tong, ‘A Novel Sliding Mode Observer With Optimized Constant Rate Reaching Law for Sensorless Control of Induction Motor’, IEEE Trans. Ind. Electron., 67(7), 5867–5878, 2020. https://doi.org/10.1109/TIE.2019.29 42577.
  • R. Yildiz, M. Barut, and E. Zerdali, ‘A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors’, IEEE Trans. Ind. Inform., 16(10), 6423–6432, 2020. https://doi.org/10.1109/TII.2020.29 64876.
  • C. Schauder, ‘Adaptive speed identification for vector control of induction motors without rotational transducers’, IEEE Trans. Ind. Appl., 28(5), 1054–1061, 1992. https://doi.org/10.1109/28.158829.
  • V. Vasic, S. N. Vukosavic, and E. Levi, ‘A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives’, IEEE Trans. Energy Convers., 18(4), 476–483, 2003. https://doi .org10.1109/TEC.2003.816595.
  • M. N. Gayathri, S. Himavathi, and R. Sankaran, Performance enhancement of vector controlled drive with rotor flux based MRAS rotor resistance estimator. International Conference on Computer Communication and Informatics (ICCCI -2012), pp. 1–6, Coimbatore, India, 2012.
  • F. L. Mapelli, D. Tarsitano, and F. Cheli, ‘MRAS rotor resistance estimators for EV vector controlled induction motor traction drive: Analysis and experimental results’, Electr. Power Syst. Res., 146, 298–307, 2017. https://doi.org/10.1016/j.epsr.2017.02. 005.
  • R. Demir and M. Barut, ‘Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control’, Trans. Inst. Meas. Control, 40(13), 3884–3898, 2018. https://doi.org/10.1177/01423312177346 31.
  • A. V. R. Teja, C. Chakraborty, S. Maiti, and Y. Hori, ‘A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives’, IEEE Trans. Ind. Electron., 59(10), 3757–3767, 2012. https://doi.org/10.1109/TIE.2011.2164769.
  • S. Basak, A. V. Ravi Teja, C. Chakraborty, and Y. Hori, A new model reference adaptive formulation to estimate stator resistance in field oriented induction motor drive. 39th Annual Conference of the IEEE Industrial Electronics Society (IECON 2013), pp. 8470–8475, Vienna, Austria, 2013.
  • T. Orlowska-Kowalska and M. Dybkowski, ‘Stator-Current-Based MRAS Estimator for a Wide Range Speed-Sensorless Induction-Motor Drive’, IEEE Trans. Ind. Electron., 57(4), 1296–1308, 2010. https:// doi.org/10.1109/TIE.2009.2031134.
  • M. Barut, S. Bogosyan, and M. Gokasan, ‘Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters’, IEEE Trans. Ind. Electron., 54(1), 272–280, 2007. https://doi.org/10.1109/TIE.200 6.885123.
  • S. Haykin, Adaptive filter theory (3rd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1996.
  • R. Yildiz, R. Demir, and M. Barut, Speed-sensorless predictive torque control of IM based on the adaptive fading extended Kalman filter. IV. International Turkic World Congress on Science and Engineering, pp. 82–93. Niğde, Turkey, 2022.
  • M. Habibullah, D. D. C. Lu, D. Xiao, J. E. Fletcher, and M. F. Rahman, ‘Predictive Torque Control of Induction Motor Sensorless Drive Fed by a 3L-NPC Inverter’, IEEE Trans. Ind. Inform., 13(1), 60–70, 2017. https:// doi.org/10.1109/TII.2016.2603922.
There are 29 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Electrical and Electronics Engineering
Authors

Rıdvan Demir 0000-0001-6509-9169

Recep Yıldız 0000-0002-8167-321X

Murat Barut 0000-0001-6798-0654

Publication Date January 15, 2023
Submission Date November 22, 2022
Acceptance Date December 26, 2022
Published in Issue Year 2023 Volume: 12 Issue: 1

Cite

APA Demir, R., Yıldız, R., & Barut, M. (2023). Speed-sensorless predictive torque control of the IM based on MRAS. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(1), 126-133. https://doi.org/10.28948/ngumuh.1208031
AMA Demir R, Yıldız R, Barut M. Speed-sensorless predictive torque control of the IM based on MRAS. NOHU J. Eng. Sci. January 2023;12(1):126-133. doi:10.28948/ngumuh.1208031
Chicago Demir, Rıdvan, Recep Yıldız, and Murat Barut. “Speed-Sensorless Predictive Torque Control of the IM Based on MRAS”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12, no. 1 (January 2023): 126-33. https://doi.org/10.28948/ngumuh.1208031.
EndNote Demir R, Yıldız R, Barut M (January 1, 2023) Speed-sensorless predictive torque control of the IM based on MRAS. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12 1 126–133.
IEEE R. Demir, R. Yıldız, and M. Barut, “Speed-sensorless predictive torque control of the IM based on MRAS”, NOHU J. Eng. Sci., vol. 12, no. 1, pp. 126–133, 2023, doi: 10.28948/ngumuh.1208031.
ISNAD Demir, Rıdvan et al. “Speed-Sensorless Predictive Torque Control of the IM Based on MRAS”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 12/1 (January 2023), 126-133. https://doi.org/10.28948/ngumuh.1208031.
JAMA Demir R, Yıldız R, Barut M. Speed-sensorless predictive torque control of the IM based on MRAS. NOHU J. Eng. Sci. 2023;12:126–133.
MLA Demir, Rıdvan et al. “Speed-Sensorless Predictive Torque Control of the IM Based on MRAS”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 12, no. 1, 2023, pp. 126-33, doi:10.28948/ngumuh.1208031.
Vancouver Demir R, Yıldız R, Barut M. Speed-sensorless predictive torque control of the IM based on MRAS. NOHU J. Eng. Sci. 2023;12(1):126-33.

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