Improved speed and load torque estimations with adaptive fading extended Kalman filter. (31st October 2020)

• Record Type: Journal Article
• Authors: Zerdali, Emrah; Yildiz, Recep; Inan, Remzi; Demir, Ridvan; Barut, Murat
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-31
• Subjects: adaptive fading extended Kalman filter -- induction motor -- parameter estimation -- speed‐sensorless control -- state estimation; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12684
• Languages: English
• ISSNs: 2050-7038

Abstract

Abstract : Background: Extended Kalman filter (EKF) is one of the most preferred observers for state and parameter estimation of induction motor. To achieve optimal estimations, EKFs require a stochastic system with complete dynamic or measurement equation. However, those equations are partially known in practice and may vary depending on operating conditions, leading to a degradation in the estimation performance of conventional EKFs (CEKFs). Aim: To overcome this drawback, this paper proposes an adaptive fading EKF (AFEKF) observer that can compensate for the effect of the incomplete dynamic equation for the estimations of stator currents, rotor fluxes, rotor mechanical speed, and load torque. Materials & Methods: To show the superiority of AFEKF, its estimation performance is compared to that of CEKF in both simulations and real‐time experiments. Both observers have been implemented through the S‐Function block in Matlab/Simulink so that the same observer blocks can be used in both simulation and experimental studies. For real‐time implementations, a DS1104 controller board is used. In addition, the computational burdens of both CEKF and AFEKF are compared with real‐time experiments. Results and Discussion: The simulation and experimental studies indicate that the forgetting factor in AFKEF clearly improves the estimation performance of CEKF, especially in transient states. It also prevents the observer from diverging. Considering its advantages, the additional computational load that causes an increase in the computational load of about 4% can be ignored. Conclusion: The proposed AFEKF observer significantly improves the estimation performance and compensates for the effect of dynamic model inaccuracies. Its superiority has been validated by simulation and experimental studies. Finally, an observer with a better estimation performance has been proposed with a slight increase in computational load. Abstract : This paper proposes an adaptive fading extended Kalman filter (EKF) observer that can compensate for the effect of the incomplete dynamic equation for the estimations of stator currents, rotor fluxes, rotor mechanical speed, and load torque. To show the superiority of adaptive fading EKF, its estimation performance is compared to that of conventional EKF in both simulations and real‐time experiments.