Yazar "Yildiz, Recep" seçeneğine göre listele

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    A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Yildiz, Recep; Barut, Murat; Zerdali, Emrah
    In this article, the real-time comparison of extended and unscented Kalman filter algorithms, which estimate the stator stationary axis components of stator currents, the stator stationary axis components of rotor fluxes, the rotor mechanical speed, and the load torque including viscous friction term, are performed under different operating conditions for speed-sensorless control applications of induction motors (IMs). Thus, it is clarified which algorithm is more suitable for state and parameter (load torque) estimation problem of IMs. For this purpose, four different real-time experimental tests have been carried out, which examine the effect of noise covariance matrices, parameter changes, sampling time, and computational burdens on estimation performance of both algorithms. Unlike the current literature, remarkable comparison results have been obtained.
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    A study on improving the state estimation of induction motor
    (Springer, 2023) Zerdali, Emrah; Yildiz, Recep
    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.
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    EKF based rotor and stator resistance estimations for direct torque control of induction motors
    (Institute of Electrical and Electronics Engineers Inc., 2017) Demir, Ridvan; Barut, Murat; Yildiz, Recep; Inan, Remzi; Zerdali, Emrah
    This study presents the direct torque controlled induction motor (IM) drive utilizing a novel extended Kalman filter (EKF) that simultaneously estimates stator stationary axis components of stator currents and stator fluxes in addition to rotor and stator resistances with the assumption of available stator voltages/currents and rotor speed. Thus, it is desired to show that the on-line estimations of rotor and stator resistances are possible by using a single EKF algorithm in the case with speed-sensor. Performances of the proposed EKF are tested under challenging scenarios generated in simulations. The obtained results confirm very satisfying performances of the introduced EKF algorithm and thus the IM drive. © 2017 IEEE.
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    EKF Based Rotor and Stator Resistance Estimations for Direct Torque Control of Induction Motors
    (IEEE, 2017) Demir, Ridvan; Barut, Murat; Yildiz, Recep; Inan, Remzi; Zerdali, Emrah
    This study presents the direct torque controlled induction motor (IM) drive utilizing a novel extended Kalman filter (EKF) that simultaneously estimates stator stationary axis components of stator currents and stator fluxes in addition to rotor and stator resistances with the assumption of available stator voltages/currents and rotor speed. Thus, it is desired to show that the on-line estimations of rotor and stator resistances are possible by using a single EKF algorithm in the case with speed-sensor. Performances of the proposed EKF are tested under challenging scenarios generated in simulations. The obtained results confirm very satisfying performances of the introduced EKF algorithm and thus the IM drive.
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    Extended Kalman filter based estimations for improving speed-sensored control performance of induction motors
    (Inst Engineering Technology-Iet, 2020) Yildiz, Recep; Barut, Murat; Demir, Ridvan
    In this study, an extended Kalman filter (EKF)-based estimation algorithm is presented to improve the speed-sensored control performance of induction motors (IMs). The proposed EKF-based estimation algorithm is to simultaneously estimate the stator stationary axis components of stator currents and rotor fluxes, rotor angular speed, load torque including viscous friction term, rotor resistance and magnetising inductance in a single EKF algorithm without requiring any switching operation or a hybrid structure. In order to improve the speed-sensored control performance, the measurement/output matrix of IM model is extended by the measured rotor speed in addition to stationary axis components of the measured stator currents. Therefore, the proposed EKF algorithm uses the speed and stator current errors between the measured and priori estimation values in order to calculate the posterior estimation ones. For performance evaluation, the eighth order (proposed) EKF algorithm is tested by simulations and real-time experiments under challenging scenarios and compared with the developed sixth order EKF in real time. The obtained real-time results also show that the eighth order (proposed) EKF algorithm provides additional and improved estimations with the increased but feasible execution time in terms of the sixth order EKF designed in this paper.
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    Improved speed and load torque estimations with adaptive fading extended Kalman filter
    (Wiley, 2021) Zerdali, Emrah; Yildiz, Recep; Inan, Remzi; Demir, Ridvan; Barut, Murat
    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.
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    Load torque and stator resistance estimations with unscented kalman filter for speed-sensorless control of induction motors
    (Institute of Electrical and Electronics Engineers Inc., 2017) Yildiz, Recep; Barut, Murat; Zerdali, Emrah; Inan, Remzi; Demir, Ridvan
    In this study, speedsensorless IM drive based on unscented Kalman filter (UKF) with the online estimations of stator stationary axis components of stator currents, rotor fluxes, rotor mechanical speed, load torque including the friction term, and stator resistance is designed. Therefore, the proposed speedsensorless IM drive is robust to load torque and stator resistance changes. Different challenging scenarios including ramp-And step-Type variations in load torque and stator resistance at both zero and high speeds are performed in computer simulations to demonstrate the superiority of the proposed UKF based speedsensorless drive. © 2017 IEEE.
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    Load Torque and Stator Resistance Estimations with Unscented Kalman Filter for Speed-Sensorless Control of Induction Motors
    (IEEE, 2017) Yildiz, Recep; Barut, Murat; Zerdali, Emrah; Inan, Remzi; Demir, Ridvan
    In this study, speedsensorless IM drive based on unscented Kalman filter (UKF) with the online estimations of stator stationary axis components of stator currents, rotor fluxes, rotor mechanical speed, load torque including the friction term, and stator resistance is designed. Therefore, the proposed speedsensorless IM drive is robust to load torque and stator resistance changes. Different challenging scenarios including ramp- and step-type variations in load torque and stator resistance at both zero and high speeds are performed in computer simulations to demonstrate the superiority of the proposed UKF based speedsensorless drive.
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    Model Predictive Controlled IM Drive based on IT2FNN Controller
    (Sciendo, 2023) Demir, Ridvan; Yildiz, Recep; Gani, Ahmet
    In this paper, the predictive torque control (PTC) based induction motor (IM) drive using an interval type-2 fuzzy neural network (IT2FNN) controller in the speed control loop is designed and tested in simulations. The states required for the proposed motor drive are estimated by extended complex Kalman filter (ECKF). The ECKF performs online estimations of stator currents, rotor fluxes, rotor mechanical speed, and rotor resistance. Compared to conventional extended Kalman filter (EKF), which estimates the same states/parameters, the designed ECKF has less computational burden because it does not contain matrix inverse and the matrix dimensions have been reduced. In addition, the rotor resistance estimated by ECKF is updated online to the PTC system. Thus, the performance of the PTC-based IM drive is improved against variations in the rotor resistance, whose value changes with operating conditions such as frequency and temperature. In order to force both the ECKF observer and the proposed IM drive, a challenging scenario containing the wide speed range operation of the IM is designed. Simulation results confirm the performance of the proposed speed-sensorless PTC-based drive that uses an IT2FNN controller in the speed control loop and the estimation performance of the ECKF observer.
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    Online estimations for electrical and mechanical parameters of the induction motor by extended Kalman filter
    (Sage Publications Ltd, 2023) Yildiz, Recep; Demir, Ridvan; Barut, Murat
    In this study, a novel extended Kalman filter (EKF)-based observer is designed to increase the number of estimated states and parameters of the induction motor (IM). To perform the online estimations of stationary axis components of stator currents and rotor fluxes (i(sa), i(sb), f(ra), and f(rb)) as well as rotor mechanical speed (?(m)), which are required for direct vector control (DVC) systems along with the load torque (t(L)), rotor resistance (R-r), magnetizing inductance (L-m), and the reciprocal of the total inertia of the system (?(T) = 1=JT), the proposed EKF uses the measured phase currents and voltages together with the measured rotor speed. To estimate all of the five states (i(sa), i(sb), f(sa), f(sb), and ?(m)) plus four parameters (t(L), R-r, L-m, and ?(T)), the proposed EKF-based observer does not include a switching operation nor a hybrid structure, which is a common approach in the literature for online state and parameter estimations of IMs and results in design complexity and computational load increase. In simulation studies, the estimation performance of the proposed EKF is tested and verified under the variations of t(L), R-r, L-m, and ?(T) in DVC systems that perform the speed and position controls of IM. The obtained results confirm the satisfying tracking performances and thus better control achievements of the speed and position controlled IM drives in this paper. Moreover, the proposed EKF and the EKF without ?(T)-estimation are compared in the position control system to demonstrate the importance of the ?(T) estimation. In the comparison, nearly 10 times less mean square error (MSE) is obtained in the estimations t(L), R-r, L-m, and the magnitude of the rotor flux for the proposed EKF. Finally, the proposed EKF algorithm is tested and verified in real-time experiments with a challenging speed reversal scenario causing nonlinear variations in both t(L) and R-r.
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    Rotor Speed and Load Torque Estimations of Induction Motors via LSTM Network
    (Sciendo, 2023) Kosten, Mehmet Muzaffer; Emlek, Alper; Yildiz, Recep; Barut, Murat
    In this study, a long short-term memory (LSTM) based estimator using rotating axis components of the stator voltages and currents as inputs is designed to perform estimations of rotor mechanical speed and load torque values of the induction motor (IM) for electrical vehicle (EV) applications. For this aim, first of all, an indirect vector controlled IM drive is implemented in simulation to collect both training and test datasets. After the initial training, a fine-tuning process is applied to increase the robustness of the proposed LSTM network. Furthermore, the LSTM parameters, layer size, and hidden size are also optimised to increase the estimation performance. The proposed LSTM network is tested under two different challenging scenarios including the operation of the IM with linear and step-like load torque changes in a single direction and in both directions. To force the proposed LSTM network, it is also tested under the variation of stator and rotor resistances for the both-direction scenario. The obtained results confirm the highly satisfactory estimation performance of the proposed LSTM network and its applicability for the EV applications of the IMs.
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    Speed-Sensorless Induction Motor Drive with Unscented Kalman Filter Including the Estimations of Load Torque and Rotor Resistance
    (IEEE, 2016) Yildiz, Recep; Barut, Murat; Zerdali, Emrah
    In this paper, an unscented Kalman filter (UKF) based speed-sensorless vector control of induction motors (IMs) have been implemented for a wide speed range including zero speed. The proposed UKF simultaneously estimates stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance. The effectiveness of the introduced UKF algorithm and thus the speed-sensorless IM drive are verified by computer simulations consisting of different challenging scenarios. From this point of view, it is the first speed-sensorless IM drive in the literature to utilize the UKF algorithm including the simultaneous estimations of stator currents, rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance in simulation.
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    Speed-sensorless induction motor drive with unscented Kalman filter including the estimations of load torque and rotor resistance
    (IEEE Computer Society, 2016) Yildiz, Recep; Barut, Murat; Zerdali, Emrah
    In this paper, an unscented Kalman filter (UKF) based speed-sensorless vector control of induction motors (IMs) have been implemented for a wide speed range including zero-speed. The proposed UKF simultaneously estimates stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance. The effectiveness of the introduced UKF algorithm and thus the speed-sensorless IM drive are verified by computer simulations consisting of different challenging scenarios. From this point of view, it is the first speed-sensorless IM drive in the literature to utilize the UKF algorithm including the simultaneous estimations of stator currents, rotor fluxes, rotor mechanical speed, load torque including viscous friction term, and rotor resistance in simulation. © 2016 IEEE.