ASENKRON MOTORUN İNDİRGENMİŞ-DERECELİ UYARLAMALI GENİŞLETİLMİŞ KALMAN FİLTRESİ İLE DURUM VE PARAMETRE KESTİRİMİ
Abstract
Bu çalışmada, asenkron motorların
(ASM’lerin) hız-algılayıcısız kontrolü için indirgenmiş-dereceli uyarlamalı
genişletilmiş Kalman filtresinin (GKF’nin) tasarımı gerçekleştirilmiş ve bu gözlemleyici
hız-algılayıcısız doğrudan vektör kontrollü sürücü sisteminde kullanılarak
başarımı benzetim çalışmaları altında test edilmiştir. Önerilen gözlemleyici
ASM’lerin vektör kontrolü için gerekli olan rotor akılarının duran eksen takımı
bileşenleri ve rotor mekanik hızına ek olarak bozucu yük momenti değişimlerini
de kestirmektedir. Diğer taraftan, GKF’lerin kestirim başarımı sistem ()
ve ölçme ()
hatası kovaryans matrislerinin doğru bilinmesine bağlıdır. Bu matrisler
literatürde genellikle sabit kabul edilmekte ve deneme-yanılma yöntemi ile
belirlenmektedir. Fakat bu matrisler ASM’nin çalışma koşullarından etkilenmekte
ve daha yüksek başarımlı kestirimler elde edebilmek için çalışma koşullarına
göre güncellenmelidirler. Hem hem de ’nin
eşzamanlı değiştirilmesi ıraksama veya takip sorunlarına neden olabileceğinden,
önerilen çalışmada sadece çalışma koşullarını göz önünde bulundurularak unutma
faktörüne sahip uyarlamalı GKF (UGKF) algoritması ile güncellenmiştir. Ayrıca,
gerçek-zamanlı uygulamalar için işlem yükünü azaltmak amacıyla UGKF
indirgenmiş-dereceli olarak tasarlanmıştır.
Keywords
Asenkron motor hız-algılayıcısız kontrol indirgenmiş-dereceli uyarlamalı genişletilmiş Kalman filtresi
References
- KUMAR, R., DAS, S., SYAM, P., CHATTOPADHYAY, A. K., “Review on model reference adaptive system for sensorless vector control of induction motor drives”, IET Electr. Power Appl., 9 (7), 496–511, 2015.
- QU, Z., HINKKANEN, M., HARNEFORS, L., “Gain Scheduling of a Full-Order Observer for Sensorless Induction Motor Drives”, IEEE Trans. Ind. Appl., Early Access Online, 2014.
- JOUILI, M., JARRAY, K., KOUBAA, Y., BOUSSAK, M., “Luenberger state observer for speed sensorless ISFOC induction motor drives”, Electr. Power Syst. Res., 89, 139–147, Aug. 2012.
- BARUT, M., DEMIR, R., ZERDALI, E., INAN, R., “Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors”, IEEE Trans. Ind. Electron., 59(11), 4197–4206, Nov. 2012.
- ZAKY, M. S., KAMEL METWALLY, M., AZAZI, H., DERAZ, S., “A New Adaptive SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Very Low Frequencies”, IEEE Trans. Ind. Electron., Early Access Online, 2018.
- ALONGE, F., CANGEMI, T., D’IPPOLITO, F., FAGIOLINI, A., SFERLAZZA, A., “Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor”, IEEE Trans. Ind. Electron., 62(4), 2341–2352, Apr. 2015.
- ZERDALI, E., BARUT, M., “The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors”, IEEE Trans. Ind. Electron., 64(6), 4340–4351, Jun. 2017.
- DEMIR, R., BARUT, M., “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, Sep. 2018.
- ALMAGBILE, A., WANG, J., DING, W., “Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration”, J. Glob. Position. Syst., 9(1), 33–40, Jun. 2010.
- AYDIN, M., GOKASAN, M., BOGOSYAN, S., “Fuzzy based parameter tuning of EKF observers for sensorless control of Induction Motors”, International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 1174–1179, 2014.
- DROZDZ, K., “Estimation of the mechanical state variables of the two-mass system using fuzzy adaptive Kalman filter - Experimental study”, IEEE 2nd International Conference on Cybernetics (CYBCONF), 455–459, 2015.
- ZERDALI, E., “Adaptive Extended Kalman Filter for Speed-Sensorless Control of Induction Motors”, IEEE Trans. Energy Convers., Early Access Online, 2018.
- JWO, D.-J., CHUNG, F.-C., WENG, T.-P., “Adaptive Kalman Filter for Navigation Sensor Fusion”, 2010.
- ZERDALI, E., YILDIZ, R., INAN, R., DEMIR, R., BARUT, M., “Adaptive Fading Extended Kalman Filter Based Speed-Sensorless Induction Motor Drive”, XIII International Conference on Electrical Machines (ICEM), 1367–1373, Alexandroupoli, Greece, 2018.
- LEE, K.-B., BLAABJERG, F., “Reduced-order extended Luenberger observer based sensorless vector control driven by matrix converter with nonlinearity compensation”, IEEE Trans. Ind. Electron., 53(1), 66–75, Feb. 2005.
- HARNEFORS, L., HINKKANEN, M., “Complete Stability of Reduced-Order and Full-Order Observers for Sensorless IM Drives”, IEEE Trans. Ind. Electron., 55(3), 1319–1329, Mar. 2008.
- DAVARI, S. A., KHABURI, D. A., WANG, F., KENNEL, R. M., “Using Full Order and Reduced Order Observers for Robust Sensorless Predictive Torque Control of Induction Motors”, IEEE Trans. Power Electron., 27(7), 3424–3433, Jul. 2012.
- DEMIR, R., BARUT, M., YILDIZ, R., “Reduced-order extended Kalman filter based parameter estimations for speed-sensored induction motor drive”, Pamukkale Univ. J. Eng. Sci., 24(8), 1464–1471, 2018.
- INAN, R., DEMIR, R., BARUT, M., “Asenkron Motorun Karma Kestirici Tabanlı Hız-Algılayıcılı Doğrudan Vektör Kontrolü”, Ömer Halisdemir Üniversitesi Mühendis. Bilim. Derg., Temmuz 2018.
STATE AND PARAMETER ESTIMATIONS OF INDUCTION MOTOR WITH REDUCED-ORDER ADAPTIVE EXTENDED KALMAN FILTER
Abstract
In this study, the design of reduced-order
adaptive extended Kalman filter (EKF) for speed-sensorless control of induction
motors (IMs) is performed, and its performance is tested using it in a speed-sensorless
direct vector controlled drive system under simulations. The proposed observer
estimates the stator stationary axis components of rotor fluxes and rotor
mechanical speed required for vector control in addition to disturbance load
torque. On the other hand, estimation performance of EKFs depends on the
correct selection of system () and measurement () error covariance matrices.
In the literature, these matrices are generally assumed as constant and
determined by the trial-and-error method. However, those matrices are affected
by operating conditions of IM and should be updated according to operating
conditions in order to obtain higher performance estimations. Since the simultaneously update of both and may lead to divergence or tracking problems, only
is updated considering operating conditions by
adaptive EKF (AEKF) algorithm having a forgetting factor. In addition, AEKF has
been designed as reduced-order with the aim of reduction of its computational
burden for real-time applications.
References
- KUMAR, R., DAS, S., SYAM, P., CHATTOPADHYAY, A. K., “Review on model reference adaptive system for sensorless vector control of induction motor drives”, IET Electr. Power Appl., 9 (7), 496–511, 2015.
- QU, Z., HINKKANEN, M., HARNEFORS, L., “Gain Scheduling of a Full-Order Observer for Sensorless Induction Motor Drives”, IEEE Trans. Ind. Appl., Early Access Online, 2014.
- JOUILI, M., JARRAY, K., KOUBAA, Y., BOUSSAK, M., “Luenberger state observer for speed sensorless ISFOC induction motor drives”, Electr. Power Syst. Res., 89, 139–147, Aug. 2012.
- BARUT, M., DEMIR, R., ZERDALI, E., INAN, R., “Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors”, IEEE Trans. Ind. Electron., 59(11), 4197–4206, Nov. 2012.
- ZAKY, M. S., KAMEL METWALLY, M., AZAZI, H., DERAZ, S., “A New Adaptive SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Very Low Frequencies”, IEEE Trans. Ind. Electron., Early Access Online, 2018.
- ALONGE, F., CANGEMI, T., D’IPPOLITO, F., FAGIOLINI, A., SFERLAZZA, A., “Convergence Analysis of Extended Kalman Filter for Sensorless Control of Induction Motor”, IEEE Trans. Ind. Electron., 62(4), 2341–2352, Apr. 2015.
- ZERDALI, E., BARUT, M., “The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors”, IEEE Trans. Ind. Electron., 64(6), 4340–4351, Jun. 2017.
- DEMIR, R., BARUT, M., “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, Sep. 2018.
- ALMAGBILE, A., WANG, J., DING, W., “Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration”, J. Glob. Position. Syst., 9(1), 33–40, Jun. 2010.
- AYDIN, M., GOKASAN, M., BOGOSYAN, S., “Fuzzy based parameter tuning of EKF observers for sensorless control of Induction Motors”, International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 1174–1179, 2014.
- DROZDZ, K., “Estimation of the mechanical state variables of the two-mass system using fuzzy adaptive Kalman filter - Experimental study”, IEEE 2nd International Conference on Cybernetics (CYBCONF), 455–459, 2015.
- ZERDALI, E., “Adaptive Extended Kalman Filter for Speed-Sensorless Control of Induction Motors”, IEEE Trans. Energy Convers., Early Access Online, 2018.
- JWO, D.-J., CHUNG, F.-C., WENG, T.-P., “Adaptive Kalman Filter for Navigation Sensor Fusion”, 2010.
- ZERDALI, E., YILDIZ, R., INAN, R., DEMIR, R., BARUT, M., “Adaptive Fading Extended Kalman Filter Based Speed-Sensorless Induction Motor Drive”, XIII International Conference on Electrical Machines (ICEM), 1367–1373, Alexandroupoli, Greece, 2018.
- LEE, K.-B., BLAABJERG, F., “Reduced-order extended Luenberger observer based sensorless vector control driven by matrix converter with nonlinearity compensation”, IEEE Trans. Ind. Electron., 53(1), 66–75, Feb. 2005.
- HARNEFORS, L., HINKKANEN, M., “Complete Stability of Reduced-Order and Full-Order Observers for Sensorless IM Drives”, IEEE Trans. Ind. Electron., 55(3), 1319–1329, Mar. 2008.
- DAVARI, S. A., KHABURI, D. A., WANG, F., KENNEL, R. M., “Using Full Order and Reduced Order Observers for Robust Sensorless Predictive Torque Control of Induction Motors”, IEEE Trans. Power Electron., 27(7), 3424–3433, Jul. 2012.
- DEMIR, R., BARUT, M., YILDIZ, R., “Reduced-order extended Kalman filter based parameter estimations for speed-sensored induction motor drive”, Pamukkale Univ. J. Eng. Sci., 24(8), 1464–1471, 2018.
- INAN, R., DEMIR, R., BARUT, M., “Asenkron Motorun Karma Kestirici Tabanlı Hız-Algılayıcılı Doğrudan Vektör Kontrolü”, Ömer Halisdemir Üniversitesi Mühendis. Bilim. Derg., Temmuz 2018.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Electrical Engineering |
| Journal Section | Electrical and Electronics Engineering |
| Authors | |
| Publication Date | July 31, 2019 |
| Submission Date | February 14, 2019 |
| Acceptance Date | July 4, 2019 |
| Published in Issue | Year 2019 Volume: 8 Issue: 2 |
