Asenkron Motorların GKF Tabanlı Model Öngörülü Moment Kontrolü

Yıl 2021, , 858 - 863, 31.12.2021

Yunus Emre Altınışık , Rıdvan Demir

https://doi.org/10.31590/ejosat.1041552

Öz

Bu çalışmada rotor direncinin kestirimi ile kontrol başarımı iyileştirilmiş hız-algılayıcısız model öngörülü moment kontrol (model predictive torque control, MPTC) tabanlı asenkron motor (ASM) sürücüsü sunulmuştur. Bu amaçla, MPTC’nin yüksek başarımlı hız kontrolü için gerekli olan hız ve akı bilgisine ek olarak rotor direnci kestirimi; girişinde ölçülen stator gerilim ve akımlarını kullanan genişletilmiş Kalman filtresi (GKF) tarafından gerçekleştirilmiştir. GKF tarafından kestirilen rotor direnci MPTC sistemi içerisine her bir örnekleme adımında güncellenerek parametre değişimlerinden kaynaklanan bozulmalar azaltılmıştır. Tasarlanan GKF algoritması ve bu algoritmayı kullanan MPTC tabanlı ASM sürücüsü yük momenti ve rotor direnci değişimlerini içeren zorlayıcı senaryolar altında geniş bir hız aralığında benzetim ortamında test edilmiş ve doğrulanmıştır. Elde edilen benzetim sonuçları, GKF algoritmasının yüksek kestirim başarımına, buna bağlı olarak hız-algılayıcısız MPTC temelli ASM sürücüsünün ise yüksek kontrol başarımına sahip olduğunu onaylamaktadır.

Anahtar Kelimeler

Model öngörülü kontrol, Asenkron motorlar, Genişletilmiş Kalman filtresi.

EKF Based Model Predictive Torque Control of Induction Motors

Öz

In this study, speed-sensorless model predictive torque control (MPTC) based induction motor (IM) drive that control performance is improved by estimating rotor resistance is presented. For this purpose, in addition to the speed and flux information required for high-performance speed control of MPTC, estimation of rotor resistance was realized by Extended Kalman filter (EKF) that uses the stator currents and voltages which are measured as inputs. The rotor resistance estimated by GKF is updated to the MPTC system at each sampling step, reducing the deteriorates caused by parameter changes. Designed EKF algorithm and MPTC based IM driver which used this algorithm is tested and confirmed in simulation environment over a wide speed range under challenging scenarios including load torque and rotor resistance variations. The obtained simulation results confirm that the GKF algorithm has high estimation performance, and accordingly the speed-sensorless MPTC-based ASM drive has high control performance.

Anahtar Kelimeler

Model predictive control, Induction motors, Extended Kalman filter.

Kaynakça

• Altınışık, Y. E., Demi̇r, R., & Barut, M. (2021). Asenkron motorların iyileştirilmiş anahtarlamalı-GKF tabanlı alan yönlendirmeli kontrolü. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(2), 545–552. https://doi.org/10.28948/ngumuh.899447
• Demir, R., & Barut, M. (2018). Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control. Transactions of the Institute of Measurement and Control, 40(13), 3884–3898. https://doi.org/10.1177/0142331217734631
• Lu, Y., & Zhao, J. (2018). A sliding mode flux observer for predictive torque controlled induction motor drive. 2018 Chinese Control And Decision Conference (CCDC), 3280–3285. https://doi.org/10.1109/CCDC.2018.8407690
• Nguyen, N.-D., Nam, N. N. N., Yoon, C., & Lee, Y. I. (2021). Speed Sensorless Model Predictive Torque Control of Induction Motors using A Modified Adaptive Full-order Observer. IEEE Transactions on Industrial Electronics, 1–1. https://doi.org/10.1109/TIE.2021.3094493
• Türker, Ç. G., Karamanakos, P., & Kennel, R. (2018). Asenkron Makinaların Değişken Anahtarlama Noktalı Model Öngörülü Akım Kontrolü. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 30(3), 181–187.
• Wang, F., Chen, Z., Stolze, P., Stumper, J.-F., Rodríguez, J., & Kennel, R. (2014). Encoderless Finite-State Predictive Torque Control for Induction Machine With a Compensated MRAS. IEEE Transactions on Industrial Informatics, 10(2), 1097–1106. https://doi.org/10.1109/TII.2013.2287395
• Xie, H., Wang, F., He, Y., Rodriguez, J., & Kennel, R. (2021). Encoderless Parallel Predictive Torque Control for Induction Machine Using A Robust Model Reference Adaptive System. IEEE Transactions on Energy Conversion, 1–1. https://doi.org/10.1109/TEC.2021.3102305
• Yan, L., Dou, M., Hua, Z., Zhang, H., & Yang, J. (2019). Robustness Improvement of FCS-MPTC for Induction Machine Drives Using Disturbance Feedforward Compensation Technique. IEEE Transactions on Power Electronics, 34(3), 2874–2886. https://doi.org/10.1109/TPEL.2018.2842743
• Yan, L., & Song, X. (2020). Design and Implementation of Luenberger Model-Based Predictive Torque Control of Induction Machine for Robustness Improvement. IEEE Transactions on Power Electronics, 35(3), 2257–2262. https://doi.org/10.1109/TPEL.2019.2939283
• Yin, S., Huang, Y., Xue, Y., Meng, D., Wang, C., Lv, Y., Diao, L., & Jatskevich, J. (2019). Improved Full-Order Adaptive Observer for Sensorless Induction Motor Control in Railway Traction Systems Under Low-Switching Frequency. IEEE Journal of Emerging and Selected Topics in Power Electronics, 1–1. https://doi.org/10.1109/JESTPE.2019.2898875
• Zerdali̇, E., & Demi̇r, R. (2021). Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications. Turkish Journal of Electrical Engineering & Computer Sciences, 29(1), 223–240.
• Zerdali, E., Demir, R., & Barut, M. (2020). Speed-Sensorless FCS-PTC Based Induction Motor Drive Capable of Disturbance Rejection. 2020 2nd Global Power, Energy and Communication Conference (GPECOM), 170–175. https://doi.org/10.1109/GPECOM49333.2020.9247917
• Zhao, Z., Ruan, Z., Meng, D., Xue, Y., & Gu, C. (2019). Sliding Mode Observer Based Sensorless Model Predictive Current Control for Induction Motor. 2019 IEEE 2nd International Conference on Power and Energy Applications (ICPEA), 84–88. https://doi.org/10.1109/ICPEA.2019.8818497