Rotor Speed Estimation Based on Extended Kalman Filter for Sensorless Vector Control of Induction Motor

Doan, Phuc Thinh; Bui, Thanh Luan; Kim, Hak Kyeong; Byun, Gi Sig

doi:10.1007/978-3-642-41968-3_48

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…It should be emphasized that the parameters r s and r r in the respective matrices given by Equations ( 24), ( 25), (28), and ( 29) are treated as constant, and are rated just like the other parameters in the coefficients a 1 -a 6 . The input and output vectors and matrices for EKF 1 and EKF 2 take the form of Equations ( 16)- (19).…”

Section: Mathematical Models Of the Modified Extended Kalman Filters ...mentioning

confidence: 99%

“…Of course, in the event of failure of all CSs, the only way to restore the stator current comes down to its estimation using an open-loop observer [9], such as a Virtual Current Sensor (VCS), proposed in [12], or flux-based observer [13]. When it comes to speed-sensorless structures, extended Kalman filters (EKFs) are commonly presented [16][17][18][19][20][21][22][23][24][25][26]. Surprisingly, in the literature known to the authors, there is only one work [14] where an EKF was proposed for the current reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

Orlowska-Kowalska,

Miniach,

Adamczyk

2024

Electronics

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In electric drive systems, one of the most common faults is related to measurement equipment, including current sensors (CSs). As information about the stator current is crucial to ensure precise control of AC drives, such a fault significantly affects the quality and security of the entire system. For this reason, a modified extended Kalman filter (EKF) has been presented in this paper as an algorithmic solution to restore stator current in the event of CS failure. In order to minimize the impact of rotor and stator resistance variations on the quality of the estimation, the proposed model includes an estimation of the general coefficient of their changes. In contrast to solutions known in the literature, the presented model considers changes in both resistances in the form of a single coefficient. This approach allows us to maintain a low order of the estimator (fifth) and thus minimize the tendency to system instability and decrease computation burden. Extensive simulation tests have shown a significant improvement in the accuracy of stator current estimation under both motor and regenerating modes, a wide speed range (1–100%), and changes in motor parameters.

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Section: Mathematical Models Of the Modified Extended Kalman Filters ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

Orlowska-Kowalska,

Miniach,

Adamczyk

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Extended Kalman filter is a nonlinear extension of kalman filter for linear system and is implemented on discrete nonlinear state model. For discretization, the forward Euler method is applied on (2) with sampling time T s [s] as in [28].…”

Section: The Proposed Extended Kalman Filter Estimatormentioning

confidence: 99%

“…The EKF estimation algorithm can be concluded in the following steps as illustrated in Figure 1b [28,38].…”

Section: The Proposed Extended Kalman Filter Estimatormentioning

confidence: 99%

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Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

et al. 2020

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In this paper, a simplified efficient method for sensorless finite set current predictive control (FSCPC) for synchronous reluctance motor (SynRM) based on extended Kalman filter (EKF) is proposed. The proposed FSCPC is based on reducing the computation burden of the conventional FSCPC by using the commanded reference currents to directly calculate the reference voltage vector (RVV). Therefore, the cost function is calculated for only three times and the necessity to test all possible voltage vectors will be avoided. For sensorless control, EKF is composed to estimate the position and speed of the rotor. Whereas the performance of the proposed FSCPC essentially necessitates the full knowledge of SynRM parameters and provides an insufficient response under the parameter mismatch between the controller and the motor, online parameter estimation based on EKF is combined in the proposed control strategy to estimate all parameters of the machine. Furthermore, for simplicity, the parameters of PI speed controller and initial values of EKF covariance matrices are tuned offline using Particle Swarm Optimization (PSO). To demonstrate the feasibility of the proposed control, it is implemented in MATLAB/Simulink and tested under different operating conditions. Simulation results show high robustness and reliability of the proposed drive.

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An Effective Technique for Controlling the Speed of Sensorless Induction Motor Through Modified EKF

Basha¹,

Krishna

2018

Smart Intelligent Computing and Applications

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Rotor Speed Estimation Based on Extended Kalman Filter for Sensorless Vector Control of Induction Motor

Cited by 11 publications

References 10 publications

Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

Compensation of Current Sensor Faults in Vector-Controlled Induction Motor Drive Using Extended Kalman Filters

Simplified Sensorless Current Predictive Control of Synchronous Reluctance Motor Using Online Parameter Estimation

An Effective Technique for Controlling the Speed of Sensorless Induction Motor Through Modified EKF

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