Browsing by Author "Inan,R."

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    Rotor Resistance Estimation of Induction Motors With a Novel Innovation-Based Adaptive Extended Kalman Filter for Self-Tuning
    (Transilvania University of Brasov 1, 2023) Inan,R.; Bulent Ertan,H.; Electrical-Electronics Engineering
    In this study a novel estimator is developed to identify the rotor resistance of the induction motor (IM) at standstill for self-tuning. For this purpose, an innovation-based adaptive extended Kalman (IAEKF) filter estimator is designed. IAEKF provides a more dynamic estimation compared to the conventional extended Kalman filter (EKF), as they have a mechanism where the system noise covariance matrix can be updated continuously, unlike conventional EKF. To increase estimation stability and also for position and amplitude information of the motor flux required for the dynamic control methods, stator stationary axis (-αβ) components of stator current and -αβ components of stator flux are estimated with rotor resistance by using the correlation between states and parameters defined as nonlinear inputs. The estimation performance of the proposed IAEKF algorithm is tested both in the simulation and on the real-time IM experimental setup at standstill. Simulation and real-time results show that the estimation achievement of the proposed IAEKF algorithm is quite impressive. © 2023 IEEE.
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    Citation - Scopus: 2
    Standstill Estimation of Stator Resistance of Induction Motors with Novel Innovation-Based Adaptive Extended Kalman Filter
    (Institute of Electrical and Electronics Engineers Inc., 2021) Inan,R.; Yirtar,M.Z.; Bulent Ertan,H.; Electrical-Electronics Engineering
    In this study, a method is developed to identify stator resistance of an induction motor (IM) at standstill in the self-tuning. An innovation-based adaptive extended Kalman filter (IAEKF) estimator in which the process noise is dynamically updated with an adaptive mechanism different from the conventional extended Kalman filter (EKF) is designed to estimate stator resistance with αβ- stator stationary axis components of stator current and αβ- components of stator flux of an IM. The reason for estimating the stator flux and stator current together with the stator resistance is to both increase the stability of the proposed estimator algorithm by using the correlation between the parameters and states in the non-linear inputs applied to the estimator and obtain the motor flux information needed by the control system. In the proposed IAEKF algorithm, a stator flux-based IM model is used for prediction purposes. The standstill estimation performance of the proposed novel IAEKF is tested with both sinusoidal and PWM power supplies, The real-time estimation results show the effectiveness and prediction accuracy of the proposed stochastic-based estimator. © 2021 IEEE.