Strong Tracking Extended Kalman Filter Based Speed and Load Torque Estimations of Induction Motor
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a strong tracking extended Kalman filter (STEKF) algorithm estimating the stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, and load torque is proposed for speed-sensorless control applications of induction motor. As known, Kalman filtering requires complete specifications of both dynamical and statistical model parameters of a system to achieve optimal performance. Therefore, the proper determination of system and measurement noise covariance matrices is crucial. However, these matrices are generally assumed as constant and determined by trial-and-error method. Failure to find optimum values by trial-and-error method for all operating conditions and the variation of these matrices according to operating conditions cause the divergence of algorithm or deterioration of its performance. Therefore, a sixth-order STEKF algorithm improving the estimation performance is designed and tested in simulations. Moreover, its performance is compared to that of standard EKF in order to prove its superiority.
Açıklama
1st Global Power, Energy and Communication Conference (IEEE GPECOM) -- JUN 12-15, 2019 -- Nevsehir, TURKEY
Anahtar Kelimeler
Induction motor, Speed-sensorless control, State estimation, Strong tracking extended Kalman filter
Kaynak
2019 Ieee 1st Global Power, Energy and Communication Conference (Gpecom2019)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
