Investigation of Vector Control Applications for Asynchronous Machines Using Online Parameter Estimation Methods

Year 2023, Volume: 10 Issue: 1, 153 - 161, 31.05.2023

Nihat Pamuk

https://doi.org/10.35193/bseufbd.1200299

Abstract

Two different methods are used in dynamic model vector control applications of asynchronous machines. The first
of these methods is to use the derivative information of the state variables based on the system observability
principle. The second is the use of instantaneous active and reactive power measurement results as a new method.
The classical equivalent circuit model is used in steady-state studies of parameter estimation methods. In dynamic
systems, methods based on nonlinear minimization of the cost function, different initial values, and giving more
precise estimation results are used. In this study, the dynamic system structure is set up as the square sum of the
difference between parameter estimate values. Different parameter estimation methods were used for
asynchronous machine models, and test results under no load and full load were examined. The impedance
measurement results in parameter estimation methods were compared with the measurement results obtained from
the model. It has been shown that the test results performed in real time are very close to the offline and nonlinear
parameter estimation values and their accuracy has been proven.

Keywords

Asynchronous Machine, Vector Control, Dynamic Model, Parameter Estimation Methods

Asenkron Makineler için Çevrimiçi Parametre Tahmin Metotları Kullanılarak Vektör Kontrol Uygulamalarının İncelenmesi

Abstract

Asenkron makinaların dinamik model vektör kontrol uygulamalarında iki farklı yöntem kullanılmaktadır. Bu
yöntemlerden ilki sistem gözlenebilirlik ilkesine dayanan durum değişkenlerine ait türev bilgilerinin
kullanılmasıdır.İkincisi ise yeni bir yöntem olarak önerilen aktif ve reaktif güç ölçüm sonuçlarının anlık olarak
paylaşılmasıdır. Parametre tahmin yöntemlerinin kararlı durum çalışmalarında klasik eşdeğer devre
modelikullanılmaktadır. Dinamik sistemlerde ise,maliyet fonksiyonunun doğrusal olmayan minimizasyonuna
dayanan, başlangıç değerleri birbirinden farklı olan ve daha kesin tahmin sonuçlarıveren yöntemler
kullanılmaktadır. Bu çalışmada, dinamik sistem yapısı parametre tahmin değerleri arasındaki farkın karesel
toplamı olacak şekilde kurulmuştur. Asenkron makine modelleri için farklı parametre tahmin yöntemleri
kullanılmış, yüksüz ve tam yük altındaki testsonuçları incelenmiştir. Empedans ölçümsonuçları ile, modelden elde
edilen ölçüm sonuçları karşılaştırılmıştır. Gerçek zamanlı gerçekleştirilen deney sonuçlarının çevrimdışı olarak gerçekleştirilen ve doğrusal olmayan parametre tahmin değerlerineçok yakın oldukları gösterilmiş ve doğrulukları
ispatlanmıştır.

Keywords

Asenkron Makine, Vektör Kontrolü, Dinamik Model, Parametre Tahmin Metotları

References

• Rengifo, J., Aller, J. M., Bueno, A., Viola, J., & Restrepo, J. (2012). Parameter Estimation Method for Induction Machines Using the Instantaneous Impedance During a Dynamic Start-Up. VI. Andean Region International Conference. 7-9 November, Cuenca, Ecuador, 11-14.
• Barambones, O., & Alkorta, P. (2011). A Robust Vector Control for Induction Motor Drives with an Adaptive Sliding-Mode Control Law. Journal of the Franklin Institute, 348(2), 300-314.
• Adamczyk, M., & Orlowska-Kowalska, T. (2022). Postfault Direct Field-Oriented Control of Induction Motor Drive Using Adaptive Virtual Current Sensor. IEEE Transactions on Industrial Electronics, 69(4), 3418-3427.
• Elmahfoud, M., Bossoufi, B., Taoussi, M., Ouanjli, N. E., & Derouich, A. (2019). Rotor Field Oriented Control of Doubly Fed Induction Motor. 5 th International Conference on Optimization and Applications (ICOA). 25-26 April, 1-6.
• Mondal, A., Sarkar, P., & Hazra, A. (2020). A Unified Approach for PI Controller Design in Delta Domain for Indirect Field-Oriented Control of Induction Motor Drive. Journal of Engineering Research, 8(3), 118- 134.
• Gamazo-Real, J. C., Vázquez-Sánchez, E., & Gómez-Gil, J. (2010). Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends. Sensors, 10(7), 6901-6947.
• Mishra, A., & Choudhary, P. (2012). Speed Control of an Induction Motor by Using Indirect Vector Control Method. International Journal of Emerging Technology and Advanced Engineering, 2(12), 144-150.
• Robyns, B., Francois, B., Degobert, P., & Hautier, J. P. (2012). Vector Control of Induction MachinesDesensitisation and Optimisation Through Fuzzy Logic. Springer, London, 75-121.
• Peretti, L., Zigliotto, M. (2012). Automatic Procedure for Induction Motor Parameter Estimation at Standstill. IET Electric Power Applications, 6(4), 214-224.
• Ando, K., Takahashi, S., Ieda, J., Kurebayashi, H., Trypiniotis, T., Barnes, C. H. W., Maekawa, S., & Saitoh, E. (2011). Electrically Tunable Spin Injector Free from the Impedance Mismatch Problem. Nature Materials, 10(9), 655-659.
• Subasri, R., Meenakumari, R., Panchal, H., Suresh, M., Priya, V., Ashokkumar, R., & Sadasivuni, K. K. (2022). Comparison of BPN, RBFN and Wavelet Neural Network in Induction Motor Modelling for Speed Estimation. International Journal of Ambient Energy, 43(1), 3246-3251.
• Saad, K., Abdellah, K., Ahmed, H., & Iqbal, A. (2019). Investigation on SVM-Backstepping Sensorless Control of Five-Phase Open-End Winding Induction Motor Based on Model Reference Adaptive System and Parameter Estimation. Engineering Science and Technology, An International Journal, 22(4), 1013-1026.
• Soliman, M. A., Hasanien, H. M., Al-Durra, A., & Alsaidan, I. (2020). A Novel Adaptive Control Method for Performance Enhancement of Grid-Connected Variable-Speed Wind Generators. IEEE Access, 8, 82617- 82629.
• Cartis, C., & Roberts, L. (2019). A Derivative-Free Gauss–Newton Method. Mathematical Programming Computation, 11(4), 631-674.
• Burgers, K. C. (2014). The Non-linear Resonant Pole Soft Switching Inverter with Induction Machine Load. University of Johannesburg, South Africa, 49-98.
• Tilli, A., & Conficoni, C. (2014). Induction Motor Sensorless Observer Aligned with Rotor Flux Derivative. IEEE Conference on Control Applications (CCA). 08-10 October, Juan Les Antibes, France, 1722-1728.
• Pamuk, N. (2018). Numerical Method for Calculations of the Multi-Dielectric Fields Based on Flux Density in High Voltage Power Transformer Apparatus. Balkan Journal of Electrical and Computer Engineering, 8(4), 342-347.
• Tang, J., Yang, Y., Blaabjerg, F., Chen, J., Diao, L., & Liu, Z. (2018). Parameter Identification of InverterFed Induction Motors: A Review. Energies, 11(9), 2194.
• Teja, A. R., Verma, V., & Chakraborty, C. (2015). A New Formulation of Reactive-Power-Based Model Reference Adaptive System for Sensorless Induction Motor Drive. IEEE Transactions on Industrial Electronics, 62(11), 6797-6808.
• Tohidi, S. (2016). Analysis and Simplified Modelling of Brushless Doubly‐Fed Induction Machine in Synchronous Mode of Operation. IET Electric Power Applications, 10(2), 110-116.
• Kim, J. G. (2022). Soft Start Analysis of Induction Motor Using Current Phase Angle. Journal of Electrical Engineering & Technology, 17(2), 1475-1480.