Yazar "Demir, Ridvan" seçeneğine göre listele

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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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    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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    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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    Improved Speed-Sensorless Input/Output Linearisation-Based SVPWM-DTC of IM
    (IEEE, 2019) Inan, Remzi; Demir, Ridvan
    In this paper, speed-sensorless input-output linearisation controller (IOLC) based direct torque control (DTC) of induction motor (IM) is implemented in simulation. The switching states of the inverter are determined by the space vector pulse width modulation (SVPWM) method to provide switching conditions with constant switching frequency instead of the lookup table-based conventional DTC method. Thus the torque and flux fluctuations, total current harmonic distortions and switching loss are decreased. In this drive system, IOLC is used instead of proportional-integral (PI) controller to obtain voltage reference vector of the SVPWM. In order to increase the control performance, an extended Kalman filter (EKF)-based estimator is used for the estimation of stator resistance in addition to stator current, stator flux, rotor mechanical angular velocity and load torque. According to the simulation results, it is understood that the sensitivity of the drive system to stator resistance is eliminated by EKF-based estimator and a very high dynamic control performance is achieved with the accurate estimations of EKF algorithm.
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    Improved Speed-Sensorless Input/Output Linearisation-Based SVPWM-DTC of im
    (Institute of Electrical and Electronics Engineers Inc., 2019) Inan, Remzi; Demir, Ridvan
    In this paper, speed-sensorless input-output linearisation controller (IOLC) based direct torque control (DTC) of induction motor (IM) is implemented in simulation. The switching states of the inverter are determined by the space vector pulse width modulation (SVPWM) method to provide switching conditions with constant switching frequency instead of the lookup table-based conventional DTC method. Thus the torque and flux fluctuations, total current harmonic distortions and switching loss are decreased. In this drive system, IOLC is used instead of proportional-integral (PI) controller to obtain voltage reference vector of the SVPWM. In order to increase the control performance, an extended Kalman filter (EKF)-based estimator is used for the estimation of stator resistance in addition to stator current, stator flux, rotor mechanical angular velocity and load torque. According to the simulation results, it is understood that the sensitivity of the drive system to stator resistance is eliminated by EKF-based estimator and a very high dynamic control performance is achieved with the accurate estimations of EKF algorithm. © 2019 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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    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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    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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    Novel hybrid estimator based on model reference adaptive system and extended Kalman filter for speed-sensorless induction motor control
    (Sage Publications Ltd, 2018) Demir, Ridvan; Barut, Murat
    This paper presents a novel hybrid estimator consisting of an extended Kalman filter (EKF) and an active power-based model reference adaptive system (AP-MRAS) in order to solve simultaneous estimation problems of the variations in stator resistance estimation to the EKF. Both the AP-MRAS, whose adaptation mechanism is developed with the help of the least mean squares method in this paper, and the EKF only utilize the measured stator voltages and currents. Performances of the proposed hybrid estimator in this paper are tested by challenging scenarios generated in simulations and real-time experiments. The obtained results demonstrate the effectiveness of the introduced hybrid estimator, together with a reduction in the processing time and size of the estimation algorithm in terms of previous studies performing the same estimations of the states and parameters. From this point of view, it is the first such study in the literature, to our knowledge.
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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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    Real-Time Implementation of Bi Input-Extended Kalman Filter-Based Estimator for Speed-Sensorless Control of Induction Motors
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2012) Barut, Murat; Demir, Ridvan; Zerdali, Emrah; Inan, Remzi
    This paper presents the real-time implementation of a bi input-extended Kalman filter (EKF) (BI-EKF)-based estimator in order to overcome the simultaneous estimation problem of the variations in stator resistance R-s and rotor resistance R-r' aside from the load torque t(L) and all states required for the speed-sensorless control of induction motors (IMs) in the wide speed range. BI-EKF algorithm consists of a single EKF algorithm using consecutively two inputs based on two extended IM models developed for the simultaneous estimation of R-r' and R-s. Therefore, from the point of real-time implementation, it requires less memory than previous EKF-based studies exploiting two separate EKF algorithms for the same aim. By using the measured stator phase voltages and currents, the developed estimation algorithm is tested with real-time experiments under challenging variations of R-s, R-r', and t(L) in a wide speed range; the results obtained from BI-EKF reveal significant improvement in the all estimated states and parameters when compared with those of the single EKFs estimating only R-r' or R-s.
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    Speed-Sensorless FCS-PTC Based Induction Motor Drive Capable of Disturbance Rejection
    (IEEE, 2020) Zerdali, Emrah; Demir, Ridvan; Barut, Murat
    Recently, finite control set-predictive torque control (FCS-PTC) is one of the most promising control methods for induction motor (IM) control due to its simplicity, fast dynamics, ability to handle nonlinearities, and the easy inclusion of constraints. However, time-varying parameters and unknown load input adversely affect its performance. Another consideration is the elimination of speed-sensor to reduce the maintenance requirements, hardware complexity, and cost. For this purpose, an adaptive fading extended Kalman filter observer is first designed for the estimations of stator flux, rotor mechanical speed, load torque, and stator resistance, and then this observer is included in the FCS-PTC based IM drive. Finally, a speed-sensorless FCS-PTC based IM drive is achieved, which rejects the disturbances caused by unknown input load and stator resistance variations.
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    Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications
    (Tubitak Scientific & Technological Research Council Turkey, 2021) Zerdali, Emrah; Demir, Ridvan
    Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as having simple and robust design, its low cost maintenance requirements and the ability to operate in harsh environments. However, it has a highly nonlinear model with timevarying electrical and mechanical parameters making them difficult to control. Finite control set-predictive torque control (FCS-PTC) is an inherently suitable and a promising control method for the IM because FCS-PTC is easy to implement and has the ability to handle nonlinearities with the inclusion of constraints. In addition, the elimination of speed sensors increases the reliability of electric motor drives while reducing cost and hardware complexity. In this paper, a speed-sensorless FCS-PTC based IM drive system is designed in order to combine the aforementioned advantages. Unlike the current literature, to improve the torque response of conventional FCS-PTC, the load torque is also estimated by an adaptive fading extended Kalman filter and is fed back into the torque control loop. The results show that improved control performance is achieved.