A study on improving the state estimation of induction motor
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Extended Kalman filter (EKF) is widely used in state estimation of induction motor (IM), and its performance depends on both the use of proper noise covariance matrices and the precise knowledge of IM parameters. These matrices are generally tuned using the trial-and-error method. However, they vary with operating conditions and should be updated online to achieve higher estimation performance. Furthermore, the assumption of constant rotor resistance (R-r) in the IM model adversely affects the estimation performance at all speeds due to temperature-and frequency-dependent variations of R-r. To overcome these issues, an adaptive fading EKF (AFEKF) is designed and tested by simulation and experimental studies. The results, which include performance comparison between EKF and AFEKF, clearly demonstrate the improvement in estimating IM states, especially in transients. Finally, an AFEKF observer compensating for the adverse effects of incorrect selection of noise covariance matrices and parameter changes is introduced to the literature.
Açıklama
Anahtar Kelimeler
Induction motor, Adaptive fading extended Kalman filter, Speed estimation, Load torque estimation, Rotor resistance estimation
Kaynak
Electrical Engineering
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
105
Sayı
4
