Induction motor state estimation using tuned Extended Kalman Filter

Allaoui, Samia; Chafaa, Kheireddine; Laamari, Yahia; Athamena, Belkacem

doi:10.1109/intee.2015.7416676

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…In this paper, an induction motor with 2 pole 1.5kW was used to illustrate the effectiveness of the proposed approach for this research. The value of this induction motor parameters are listed in Table 1, which was adopted from the work of Allaoui et al [13].…”

Section: Resultsmentioning

confidence: 99%

Fault Estimation on Induction Motor Based on Stator Inter-Turn Fault

Widjiantoro

Munir

Indriawati

2021

IJPS

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Since the 19th century, the use of electric motors continues to grow. Nowadays electric motors have been widely used in various fields of industry. One type of electric motor that is often used is an induction motor. Induction motors work in the presence of induced currents due to the relative difference in rotor rotation with rotating magnetic fields. Induction motors are preferred for industrial purposes because of low cost, easy to maintain, and high efficiency. Induction motors that are used continuously can experience several types of fault. The existence of fault can affect the performance of the induction motor. One of the fault that often occurs in induction motor is the result of stator inter-turn fault. This fault is caused by the gradual deterioration of insulation in the stator winding which cause a short-circuit. Sooner or later, this fault can cause damage to the induction motor in a short time if left unchecked. So, it is very important to monitor the fault in real-time. Therefore, this research proposes a fault estimation method on induction motor. The design of fault estimation based on particle filtering and extended state space equations is used to estimate the stator inter-turn fault. The effectiveness of this approach is validated by use of a computer simulation with using two fault signal represented by 𝛈 𝐜𝐜 ramp and step signal. The performances of this fault estimation are measured by RMSE and with using 500 particles has smallest RMSE value, which are 0.0112 and 0.0124 for dq current fault when using 𝛈 𝐜𝐜 ramp signal and 0.2373 and 0.2367 for dq current fault when using 𝛈 𝐜𝐜 step signal.

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Section: Resultsmentioning

confidence: 99%

Fault Estimation on Induction Motor Based on Stator Inter-Turn Fault

Widjiantoro

Munir

Indriawati

2021

IJPS

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“…Space vector control (SVC) pulse width modulation is used as a control for three phase IM [23]- [25]. Table 1 shows the parameter of IM under test.…”

Section: Sensorless Dfocim Drive Understudymentioning

confidence: 99%

Optimized Kalman filters for sensorless vector control induction motor drives

Hussain

Alshadeedi²,

Hejeejo³

2023

IJECE

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This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions.

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“…This platform uses OPAL-RT real-time simulator. One Drivelab OPAL-RT Board and one OP5600 OPAL-RT Simulator are now installed at the CAOSEE Laboratory at Bechar University [21], [26], [27]. RT-LAB is employed to build the SM subsystem wich is converted into C code by using the Real-Time-Works hop (RTW).…”

Section: Real-time Platform Using Rt-labmentioning

confidence: 99%

Real-time implementation of SVPWM-sensorless vector control of induction motor using an extended Kalman filter

Bendjima¹,

Hazzab²,

Bechar³

et al. 2023

IJEECS

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In this research paper, space vector pulse width modulation (SVPWM)-sensorless vector control of an induction motor using an extended Kalman filter is presented. The aim of the proposed sensorless control method is to design, implement, and test a sensorless vector control scheme by simulation and experimental implementation. An extended Kalman filter (EKF) simultaneously estimates the rotor speed, the stator stationary axis components (iαs, iβs), and the rotor fluxes (jαs, jβs). The measured stator voltages and currents are employed as inputs for a recursive filter. Simulation results under various operating conditions validate the performances and effectiveness of the proposed observer. The experimental system consists of a host computer with two subsystems: console (SC) and master (SM). The SM subsystem converts to real-time C code, and this code is uploaded into OP5600 real time digital simulation (RTDS) for real-time execution. The obtained experimental results prove that the EKF speed observer can replace the speed or position sensor. This has the benefits of reducing the drive system’s size and overall cost as well as high system reliability.

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Induction motor state estimation using tuned Extended Kalman Filter

Cited by 8 publications

References 3 publications

Fault Estimation on Induction Motor Based on Stator Inter-Turn Fault

Fault Estimation on Induction Motor Based on Stator Inter-Turn Fault

Optimized Kalman filters for sensorless vector control induction motor drives

Real-time implementation of SVPWM-sensorless vector control of induction motor using an extended Kalman filter

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