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Asenkron motorlarda yapay nöral ağlar ile/olmadan dolaylı alan yönlendirmeli kontrol ve doğrudan tork kontrolünün karşılaştırması

Yıl 2021, Cilt: 10 Sayı: 2, 527 - 534, 27.07.2021
https://doi.org/10.28948/ngumuh.643868

Öz

Asenkron motorların akı, hız ve tork kontrolü performansı, motorun parametre sapmalarından ve doğrusal olmayan varyasyonlarından etkilenmektedir. Bu çalışmada, Doğrudan Moment Kontrolü (DMK) ve Dolaylı Alan Yönlendirmeli Kontrol (DAYK) yapıları incelenmiş ve her iki kontrol yapısındaki motor parametre sapmalarını yapay nöral ağlar (YNA) ile duyarsızlaştırılmaya çalışılmıştır. Literatürde Dolaylı Alan Yönlendirmeli Kontrol yapısında PI denetleyiciler kullanılmaktadır. Parametre duyarsızlaştırması için ANN önerilmesi ve bu iki yöntem için literatürde karşılaştırma ve değerlendirme yapılmamıştır. Karşılaştırmalar genellikle Doğrudan Alan Yönlendirmeli Kontrol üzerinedir. Bu çalışma, yapay nöral ağları olan/olmayan DAYK ve DMK’ nın parametre duyarsızlaştırması önererek ve çıkış performanslarına etkisini incelemektedir. Önerilen kontrol yapısı ile asenkron motor çıkışındaki akı, tork ve hızın istenen performansta verilen referans değeri yakaladığı ve hata değerlerinin azaldığı görülmektedir. YNA ile yapılan duyarsızlaştırma ile DAYK ’ın DMK’ e göre, özellikle aşma ve oturma zamanında %50 ’nin üzerinde daha iyi performans gösterdiği saptanmıştır. Önerilen algoritmalar Matlab/Simulink ile gerçeklenmiş ve her metot için aynı referans değerleri kullanılmıştır.

Kaynakça

  • J. Barta, N. Uzhegov, P. Losak, C. Ondrusek, M. MacH, and J. Pyrhonen, Squirrel-Cage Rotor Design and Manufacturing for High-Speed Applications, IEEE Trans. Ind. Electron. 66 6768–6778. 2019. https://doi.org/10.1109/TIE.2018.2879285.
  • J. M. Pena, and E. V. Diaz, Implementation of V/f scalar control for speed regulation of a three-phase induction motor, in: Proc. 2016 IEEE ANDESCON, ANDESCON 2016, Institute of Electrical and Electronics Engineers Inc., 2017. https://doi.org/ 10.1109/ANDESCON.2016.7836196.
  • A. Taheri, H. P. Ren, C. H. Song, Sensorless Direct Torque Control of the Six-Phase Induction Motor by Fast Reduced Order Extended Kalman Filter, Complexity. 2020. https://doi.org/ 10.1155 /2020/ 8 985417.
  • A. Pal, S. Das, and A. K. Chattopadhyay, An improved rotor flux space vector based mras for field-oriented control of ınduction motor drives, IEEE Trans. Power Electron. 33 5131–41.2018 https:// doi.org /10.1109/ TPEL. 2017. 2657648.
  • S. Peresasa, A. Tilli and A. Tonielli, Theoretical and experimental Comparison of indirect Field oriented Controllers for induction motors, IEEE Trans. on Power Electron., vol. 18, 151 163, 2003.
  • Y. Liu, G. Tao, H. Wang, and F. Blaabjerg, Analysis of indirect rotor field oriented control-based induction machine performance under inaccurate field-oriented condition, in: Proc. IECON 2017 - 43rd Annu. Conf. IEEE Ind. Electron. Soc., Institute of Electrical and Electronics Engineers Inc., 1810–15. 2017 https://doi.org/10.1109/IECON. 2017 .8216306.
  • L. Monjo, F. Córcoles, and J. Pedra, Parameter estimation of squirrel-cage motors with parasitic torques in the torque-slip curve, IET Electr. Power Appl. 9 377–87. 2015 https://doi.org/10.1049 /iet-epa.2014.0208.
  • Yang, D. Ding, X. Li, Z. Xie, X. Zhang, and L. Chang, A Novel Online Parameter Estimation Method for Indirect Field Oriented Induction Motor Drives, IEEE Trans. Energy Convers. 32 1562–73. 2017 https://doi.org/10.1109/TEC.2017.2699681.
  • A. Rubai, D. Ricketts, and D. Kanham, Development and implementation of an adaptive fuzzy-neural network controller for brushless drivers, IEEE Transaction on Industry Applications, 38, 441-447, 2002.
  • B. Lazerini, L. M. Reyneri, and M. A. Chiaberge, A neuro- fuzzy Approach to hybrid intelligent control, IEEE Transactions on Industry Applications, 35, 413- 425,1999.
  • B. Kirankumar, Y. V. Siva Reddy, and M. Vijayakumar, Multilevel inverter with space vector modulation: Intelligence direct torque control of induction motor, IET Power Electron. 10 1129–37. 2017 https://doi.org/10.1049/iet-pel. 2016. 0287.
  • K. H. Tan, Squirrel-Cage Induction Generator System Using Wavelet Petri Fuzzy Neural Network Control for Wind Power Applications, IEEE Trans. Power Electron. 31 5242–54. 2016 https://doi.org/10.1109/ TPEL.2015.2480407.
  • Y. Üser, K. Gülez, A new direct torque control algorithm for torque and flux ripple reduction, Internatıonal Revıew of Electrıcal Engıneerıng-IREE,.8, 644-653, 2013
  • R. İnan, Asenkron motorun alan zayıflama bölgesinde kayan kip denetçi tabanlı hız-algılayıcısız doğrudan vektör kontrolü, Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg. 8 762–774. 2019 https://doi.org/10.28948 /ngu muh.515332.
  • R. İnan, R. Demir ve M. Barut, Asenkron motorun karma kestirici tabanlı hız-algılayıcılı doğrudan vektör kontrolü, Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg. 7 612–623. 2018 https ://doi.org/10.28948/ngumuh.443233.
  • E. Zerdali, Modele uyarlamalı sistem temelli model öngörülü moment kontrollü sürücü sisteminin tasarımı, Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg. 9 146–153.2020 https://doi.org/10.28948 /ngumuh.607378.
  • Y. Üser, K. Gülez, and Ş. Özen, Sensorless flux region modification of Dtc controlled Im For torque ripple reduction, IU-JEEE, vol.14, pp.1753-60, 2014.
  • C. Fahassa, Y. Sayouti, and M. Akherraz, Improvement of the induction motor drive’s indirect field oriented control performance by substituting its speed and current controllers with fuzzy logic components, in: Proc. 2015 IEEE Int. Renew. Sustain. Energy Conf. IRSEC 2015, Institute of Electrical and Electronics Engineers Inc., 2016. https://doi.org/10.1109/ IRSEC.2015.7454928
  • L. Zhang, X. Zhu, Y. Fan, C. Li, Optimal flux-weakening control of a new five-phase FT-IPMmotor based on DTC and SVPWM for electric vehicle applications, IET Electr. Power Appl. 13 73–80. 2019 https://doi.org/10.1049/iet-epa.2018 . 5204.
  • L. Saribulut, A. Teke, M. Tümay, Artificial neural network-based discrete-fuzzy logic controlled active power filter, IET Power Electron. 7 1536–46. 2014 https://doi.org/10.1049/iet-pel.2013 .0 522.
  • M. Hamed Chebre, A. Meroufel, Y. Bendaha, Speed control of ınduction motor using genetic algorithm-based pı controller, Acta Polytechnica Hungarica, 2011, Vol. 8, No. 6, 141-153.
  • P. Cao, X. Zhang, and S. Yang, A unified-model-based analysis of mras for online rotor time constant estimation in an ınduction motor drive, IEE Trans. Ind. Electron. 64 4361–71. 2017 https: //doi .org / 10.1109/TIE.2017.2668995.

Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors

Yıl 2021, Cilt: 10 Sayı: 2, 527 - 534, 27.07.2021
https://doi.org/10.28948/ngumuh.643868

Öz

The flux, speed, and torque control performance of asynchronous motors are affected by parameter deviations and nonlinear variations of the asynchronous motor. In this study, Direct Torque Control (DTC) and Indirect Field Oriented Control (IFOC) structures are examined and asynchronous motor parameter deviations in both control structures are varied to desensitize with Artificial Neural Networks (ANN). In the literature, PI controllers are used in the IFOC structure. ANN is proposed for parameter desensitization, to the best of our knowledge no comparison and assessment has been made in the literature for these two methods. Comparisons are usually on the Direct Field Oriented Control (DFOC). This study proposes the parameter desensitization of IFOC and DTC with / without artificial neural networks and examines the effect on output performance. With the proposed control structure, it has been observed that the values of flux, torque and speed of asynchronous motor outputs capture the reference value at the desired performance and decrease the error values. With the proposed desensitization with ANN, IFOC performed over 50% better particularly in the time of overshoot and sitting than DTC. The proposed algorithms are implemented with Matlab / Simulink and the same reference values are used for each method.

Kaynakça

  • J. Barta, N. Uzhegov, P. Losak, C. Ondrusek, M. MacH, and J. Pyrhonen, Squirrel-Cage Rotor Design and Manufacturing for High-Speed Applications, IEEE Trans. Ind. Electron. 66 6768–6778. 2019. https://doi.org/10.1109/TIE.2018.2879285.
  • J. M. Pena, and E. V. Diaz, Implementation of V/f scalar control for speed regulation of a three-phase induction motor, in: Proc. 2016 IEEE ANDESCON, ANDESCON 2016, Institute of Electrical and Electronics Engineers Inc., 2017. https://doi.org/ 10.1109/ANDESCON.2016.7836196.
  • A. Taheri, H. P. Ren, C. H. Song, Sensorless Direct Torque Control of the Six-Phase Induction Motor by Fast Reduced Order Extended Kalman Filter, Complexity. 2020. https://doi.org/ 10.1155 /2020/ 8 985417.
  • A. Pal, S. Das, and A. K. Chattopadhyay, An improved rotor flux space vector based mras for field-oriented control of ınduction motor drives, IEEE Trans. Power Electron. 33 5131–41.2018 https:// doi.org /10.1109/ TPEL. 2017. 2657648.
  • S. Peresasa, A. Tilli and A. Tonielli, Theoretical and experimental Comparison of indirect Field oriented Controllers for induction motors, IEEE Trans. on Power Electron., vol. 18, 151 163, 2003.
  • Y. Liu, G. Tao, H. Wang, and F. Blaabjerg, Analysis of indirect rotor field oriented control-based induction machine performance under inaccurate field-oriented condition, in: Proc. IECON 2017 - 43rd Annu. Conf. IEEE Ind. Electron. Soc., Institute of Electrical and Electronics Engineers Inc., 1810–15. 2017 https://doi.org/10.1109/IECON. 2017 .8216306.
  • L. Monjo, F. Córcoles, and J. Pedra, Parameter estimation of squirrel-cage motors with parasitic torques in the torque-slip curve, IET Electr. Power Appl. 9 377–87. 2015 https://doi.org/10.1049 /iet-epa.2014.0208.
  • Yang, D. Ding, X. Li, Z. Xie, X. Zhang, and L. Chang, A Novel Online Parameter Estimation Method for Indirect Field Oriented Induction Motor Drives, IEEE Trans. Energy Convers. 32 1562–73. 2017 https://doi.org/10.1109/TEC.2017.2699681.
  • A. Rubai, D. Ricketts, and D. Kanham, Development and implementation of an adaptive fuzzy-neural network controller for brushless drivers, IEEE Transaction on Industry Applications, 38, 441-447, 2002.
  • B. Lazerini, L. M. Reyneri, and M. A. Chiaberge, A neuro- fuzzy Approach to hybrid intelligent control, IEEE Transactions on Industry Applications, 35, 413- 425,1999.
  • B. Kirankumar, Y. V. Siva Reddy, and M. Vijayakumar, Multilevel inverter with space vector modulation: Intelligence direct torque control of induction motor, IET Power Electron. 10 1129–37. 2017 https://doi.org/10.1049/iet-pel. 2016. 0287.
  • K. H. Tan, Squirrel-Cage Induction Generator System Using Wavelet Petri Fuzzy Neural Network Control for Wind Power Applications, IEEE Trans. Power Electron. 31 5242–54. 2016 https://doi.org/10.1109/ TPEL.2015.2480407.
  • Y. Üser, K. Gülez, A new direct torque control algorithm for torque and flux ripple reduction, Internatıonal Revıew of Electrıcal Engıneerıng-IREE,.8, 644-653, 2013
  • R. İnan, Asenkron motorun alan zayıflama bölgesinde kayan kip denetçi tabanlı hız-algılayıcısız doğrudan vektör kontrolü, Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg. 8 762–774. 2019 https://doi.org/10.28948 /ngu muh.515332.
  • R. İnan, R. Demir ve M. Barut, Asenkron motorun karma kestirici tabanlı hız-algılayıcılı doğrudan vektör kontrolü, Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg. 7 612–623. 2018 https ://doi.org/10.28948/ngumuh.443233.
  • E. Zerdali, Modele uyarlamalı sistem temelli model öngörülü moment kontrollü sürücü sisteminin tasarımı, Ömer Halisdemir Üniversitesi Mühendislik Bilim. Derg. 9 146–153.2020 https://doi.org/10.28948 /ngumuh.607378.
  • Y. Üser, K. Gülez, and Ş. Özen, Sensorless flux region modification of Dtc controlled Im For torque ripple reduction, IU-JEEE, vol.14, pp.1753-60, 2014.
  • C. Fahassa, Y. Sayouti, and M. Akherraz, Improvement of the induction motor drive’s indirect field oriented control performance by substituting its speed and current controllers with fuzzy logic components, in: Proc. 2015 IEEE Int. Renew. Sustain. Energy Conf. IRSEC 2015, Institute of Electrical and Electronics Engineers Inc., 2016. https://doi.org/10.1109/ IRSEC.2015.7454928
  • L. Zhang, X. Zhu, Y. Fan, C. Li, Optimal flux-weakening control of a new five-phase FT-IPMmotor based on DTC and SVPWM for electric vehicle applications, IET Electr. Power Appl. 13 73–80. 2019 https://doi.org/10.1049/iet-epa.2018 . 5204.
  • L. Saribulut, A. Teke, M. Tümay, Artificial neural network-based discrete-fuzzy logic controlled active power filter, IET Power Electron. 7 1536–46. 2014 https://doi.org/10.1049/iet-pel.2013 .0 522.
  • M. Hamed Chebre, A. Meroufel, Y. Bendaha, Speed control of ınduction motor using genetic algorithm-based pı controller, Acta Polytechnica Hungarica, 2011, Vol. 8, No. 6, 141-153.
  • P. Cao, X. Zhang, and S. Yang, A unified-model-based analysis of mras for online rotor time constant estimation in an ınduction motor drive, IEE Trans. Ind. Electron. 64 4361–71. 2017 https: //doi .org / 10.1109/TIE.2017.2668995.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Elektrik Elektronik Mühendisliği
Yazarlar

Yavuz Üser 0000-0002-1775-0954

Haydar Can Acar Bu kişi benim 0000-0002-4100-8434

Yayımlanma Tarihi 27 Temmuz 2021
Gönderilme Tarihi 7 Kasım 2019
Kabul Tarihi 18 Ocak 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 10 Sayı: 2

Kaynak Göster

APA Üser, Y., & Acar, H. C. (2021). Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(2), 527-534. https://doi.org/10.28948/ngumuh.643868
AMA Üser Y, Acar HC. Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors. NÖHÜ Müh. Bilim. Derg. Temmuz 2021;10(2):527-534. doi:10.28948/ngumuh.643868
Chicago Üser, Yavuz, ve Haydar Can Acar. “Indirect Field Oriented Control and Direct Torque Control Comparison with/Without Artificial Neural Networks on Asynchronous Motors”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10, sy. 2 (Temmuz 2021): 527-34. https://doi.org/10.28948/ngumuh.643868.
EndNote Üser Y, Acar HC (01 Temmuz 2021) Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10 2 527–534.
IEEE Y. Üser ve H. C. Acar, “Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors”, NÖHÜ Müh. Bilim. Derg., c. 10, sy. 2, ss. 527–534, 2021, doi: 10.28948/ngumuh.643868.
ISNAD Üser, Yavuz - Acar, Haydar Can. “Indirect Field Oriented Control and Direct Torque Control Comparison with/Without Artificial Neural Networks on Asynchronous Motors”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 10/2 (Temmuz 2021), 527-534. https://doi.org/10.28948/ngumuh.643868.
JAMA Üser Y, Acar HC. Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors. NÖHÜ Müh. Bilim. Derg. 2021;10:527–534.
MLA Üser, Yavuz ve Haydar Can Acar. “Indirect Field Oriented Control and Direct Torque Control Comparison with/Without Artificial Neural Networks on Asynchronous Motors”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 10, sy. 2, 2021, ss. 527-34, doi:10.28948/ngumuh.643868.
Vancouver Üser Y, Acar HC. Indirect field oriented control and direct torque control comparison with/without artificial neural networks on asynchronous motors. NÖHÜ Müh. Bilim. Derg. 2021;10(2):527-34.

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