EN
TR
Online stator and rotor resistance estimations of IM by using EKF
Öz
In this paper, a state and parameter observer, based on a novel extended
Kalman filter (EKF), is designed to solve the parameter variations
dependent estimation performance deterioration of induction motor
(IM) drive systems. The proposed EKF based observer algorithm
performs online estimation of the rotor mechanical speed, stator
stationary axis component of the stator currents and rotor fluxes, stator
resistance, rotor resistance, reciprocal of the total inertia of the system,
and load torque including viscous friction term in a single EKF by using
measured rotor mechanical speed and stator currents. Thus, frequency
and temperature-dependent variations of the resistances are estimated
to be updated in the observer, which leads to control performance
enhancement of the IM drive. Moreover, to rise the dynamic
performance of the observer, the load torque and reciprocal of the total
inertia of the system which are mechanical parameters are also
estimated. To verify the robustness of the IM drive and the estimation
performance of the proposed observer, they have been tested under
challenging scenarios including changes in parameters and speed
reference. Moreover, the estimation performance of the proposed ninth
order observer is compared with that of a sixth order EKF estimating
the same electrical parameters by using directly measured speed.
Ultimately, the simulation results obviously reveal the efficacy of the
proposed IM drive.
Anahtar Kelimeler
Kaynakça
- [1] Yildiz R, Demir R, Barut M. “Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter”. Transactions of the Institute of Measurement and Control, 45(14), 2725-2738. 2023.
- [2] Tikkani A, Prasad PVN. “a fuzzy-2 indirect vector control of induction motor using space vector fuzzy-2 based PWM”. Journal of Electrical Engineering & Technology, 17(3), 1845–1858, 2022.
- [3] Demir R. “Robust stator flux and load torque estimations for induction motor drives with EKF-based observer”. Electrical Engineering, 105(1), 551–562, 2023.
- [4] Demir R, Barut M.“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, 2018.
- [5] Rodriguez J, Kennel RM, Espinoza JR, Trincado M, Silva C A, Rojas CA. “High-Performance control strategies for electrical drives: an experimental assessment”. IEEE Transactions on Industrial Electronics, 59(2), 812–820, 2012.
- [6] Nguyen ND, Nam NN, Yoon C, Lee Y. I. “Speed sensorless model predictive torque control of induction motors using a modified adaptive full-order observer”. IEEE Transactions on Industrial Electronics, 69(6), 6162–6172, 2022.
- [7] Özdemir S.“A new stator voltage error-based MRAS model for field-oriented controlled induction motor speed estimation without using voltage transducers”. Electrical Engineering, 102(4), 2465–2479, 2020.
- [8] Yin Z, Bai C, Du N, Du C, Liu J. “Research on internal model control of induction motors based on luenberger disturbance observer”. IEEE Transactions on Power Electronics, 36(7), 8155–8170, 2021.
Ayrıntılar
Birincil Dil
İngilizce
Konular
Elektrik Mühendisliği (Diğer)
Bölüm
Araştırma Makalesi
Yayımlanma Tarihi
29 Kasım 2024
Gönderilme Tarihi
19 Haziran 2023
Kabul Tarihi
27 Kasım 2023
Yayımlandığı Sayı
Yıl 2024 Cilt: 30 Sayı: 6
APA
Yıldız, R., Barut, M., & Demir, R. (2024). Online stator and rotor resistance estimations of IM by using EKF. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 30(6), 771-778. https://izlik.org/JA53FM99PB
AMA
1.Yıldız R, Barut M, Demir R. Online stator and rotor resistance estimations of IM by using EKF. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2024;30(6):771-778. https://izlik.org/JA53FM99PB
Chicago
Yıldız, Recep, Murat Barut, ve Rıdvan Demir. 2024. “Online stator and rotor resistance estimations of IM by using EKF”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30 (6): 771-78. https://izlik.org/JA53FM99PB.
EndNote
Yıldız R, Barut M, Demir R (01 Kasım 2024) Online stator and rotor resistance estimations of IM by using EKF. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30 6 771–778.
IEEE
[1]R. Yıldız, M. Barut, ve R. Demir, “Online stator and rotor resistance estimations of IM by using EKF”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 30, sy 6, ss. 771–778, Kas. 2024, [çevrimiçi]. Erişim adresi: https://izlik.org/JA53FM99PB
ISNAD
Yıldız, Recep - Barut, Murat - Demir, Rıdvan. “Online stator and rotor resistance estimations of IM by using EKF”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 30/6 (01 Kasım 2024): 771-778. https://izlik.org/JA53FM99PB.
JAMA
1.Yıldız R, Barut M, Demir R. Online stator and rotor resistance estimations of IM by using EKF. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2024;30:771–778.
MLA
Yıldız, Recep, vd. “Online stator and rotor resistance estimations of IM by using EKF”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, c. 30, sy 6, Kasım 2024, ss. 771-8, https://izlik.org/JA53FM99PB.
Vancouver
1.Recep Yıldız, Murat Barut, Rıdvan Demir. Online stator and rotor resistance estimations of IM by using EKF. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi [Internet]. 01 Kasım 2024;30(6):771-8. Erişim adresi: https://izlik.org/JA53FM99PB