Yazar "Inan, Remzi" seçeneğine göre listele

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    Design of adaptive extended Kalman filter-based speed-sensorless direct torque controlled drive system with constant switching frequency
    (Pamukkale Univ, 2020) Inan, Remzi; Zerdali, Emrah
    In this study, the design of the adaptive extended Kalman filter (AEKF) based direct torque controlled drive system with constant switching frequency (DTCD-CSF) is performed for speed-sensorless control of induction motor (IM). The system noise covariance matrix, which directly affects the estimation performance of EKFs and is assumed as constant in conventional EKFs, is updated online according to the operating conditions by the AEKF, which uses stator flux-based IM model. The reason why the proposed algorithm is included on a DTCD-CSF, instead of a conventional DTCD, is to reduce the torque ripples in the traditional direct torque control and the switching losses caused by the variable switching frequency. The load torque and the stator stationary axis components of stator currents in addition to the stator stationary axis components of stator fluxes and the rotor mechanical speed required for the DTCD-CSF are estimated by the proposed AEKF-based observer. By the load torque estimation, it is ensured that the proposed speed-sensorless drive system is robust to changes in load torque. Finally, the proposed AEKF-based speed-sensorless DTCD-CSF is verified under simulation studies.
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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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    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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    Real-time implementation of battery management system designed with improved passive balancing technique for electric vehicles
    (Gazi Univ, Fac Engineering Architecture, 2023) Inan, Remzi; Guckiran, Muhammed; Altinisik, Yunus Emre; Tek, Salih Enes; Potuk, Mesut
    In this study, a battery management system (BMS) that allows charge and discharge of the cells more efficiently by using improved passive balancing technique with the battery package's design composed of the combination of cylindrical li-ion cells is proposed. Incorrect connection, unwanted short circuit condition composed by external intervention, adverse environmental conditions, use of batteries that are not absolutely homogeneous and arising from manufacturing pose a risk for the battery package. During the problems such as excessive temperature, failed charge-discharge and overcurrent-overvoltage due to short circuit which may occur owing to these situations, BMS must be able intervene to the system and give visual / audible warnings. In terms of efficiency, using as close to each other of cell voltage values and even at the same level is important. ARM based STM32F103 as the central and secondary microcontroller in the proposed BMS is preferred and passive balancing method is used. Within the context of project, when one thinks the electronic card designs that are realized in double layer structure by using surface-mounted materials as much as possible, the energy density and system features of the battery package generally, a pretty utilitarian real-time battery management system is acquired. Furthermore, the design of the battery package allows that the proposed battery management system exhibit an adaptable structure in particular in terms of current capacity thanks to the addition and removal of parallel modules according to the state of the system that intended be used. The achievement of the proposed system is tested with a real-time electric vehicle.
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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 Direct Vector Control of Induction Motor with the EKF based stator resistance estimation on FPGA
    (IEEE, 2015) Inan, Remzi; Barut, Murat; Ertan, HB
    This paper present the Field Programmable Gate Array (FPGA) realization of speed-sensorless Direct Vector Control (DVC) utilizing Extended Kalman Filter (EKF) with stator resistance estimation for Induction Motors (IMs). The EKF simultaneously estimates stator stationary axis components of stator currents and rotor fluxes, rotor angular velocity, load torque and stator resistance. The implemented speed-sensorless control system on FPGA also includes the model of IM to be controlled, so it is considered as a Hardware In the Loop (HIL) system. The FPGA is programmed via Very High Speed Integrated Circuit Hardware Description Language (VHDL). Comparison of the estimated states/ parameters obtained from the EKF with the ones of the IM show the effectiveness of the FPGA implementation of the speed-sensorless DVC of IM in this study.