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http://archives.univ-biskra.dz/handle/123456789/2516
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DC Field | Value | Language |
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dc.contributor.author | H. Talhaoui | - |
dc.contributor.author | A. Menacer | - |
dc.contributor.author | R. Kechida | - |
dc.date.accessioned | 2014-05-09T07:22:12Z | - |
dc.date.available | 2014-05-09T07:22:12Z | - |
dc.date.issued | 2014-05-09 | - |
dc.identifier.uri | http://archives.univ-biskra.dz/handle/123456789/2516 | - |
dc.description.abstract | The aim of this paper is the diagnosis of the rotor fault of squirrel cage induction motor controlled in slidingmode (SMC). The faulty identification in this case is very difficult, due to the action of the control loop (SMC). Motor current signature analysis (MCSA) is the most widely used method for the faults identification in induction motors. This method generally suffers from load disturbance, speed variation. A broken rotor bar essentially leads to an increase in the rotor resistance of the induction motor. This paper present a method of broken rotor bars diagnosis based on the estimation of the rotor resistance using an Extended Kalman filter (EKF) and the spectrum analysis of stator current. The simulation results show that the presented algorithm is effective and accurate. | en_US |
dc.language.iso | en | en_US |
dc.subject | Induction Motor, Sliding Mode Control, Fault Detection, Broken Rotor Bars, Diagnosis, Motor Current Signature Analysis, FFT, Rotor Resistance Estimation, Extended Kalman Filter. | en_US |
dc.title | Rotor Resistance Estimation using EKF for the Rotor Fault Diagnosis in Sliding Mode Control Induction Motor | en_US |
dc.type | Article | en_US |
Appears in Collections: | Communications Internationales |
Files in This Item:
File | Description | Size | Format | |
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CI 31_Talhaoui_.pdf | 386,42 kB | Adobe PDF | View/Open |
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