<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Chosen speed and current sensor fault detectors for a vector controlled induction motor drive system have been presented. Systems based on the artificial intelligence (neural network) and simple algorithmic systems were analyzed and tested in various drive conditions. The influence of chosen sensor faults on performance of the drive system has been presented. The compensation strategy was proposed and tested. A fault tolerant drive, based on hardware redundancy, has been developed and presented. Simulation and experimental results are obtained in direct field oriented control algorithm (DFOC) on the laboratory set-up with rapid prototyping card Micro Lab Box DS1202 by dSpace.</p></abstract>

Chosen speed and current sensor fault detectors for a vector controlled induction motor drive system have been presented. Systems based on the artificial intelligence (neural network) and simple algorithmic systems were analyzed and tested in various drive conditions. The influence of chosen sensor faults on performance of the drive system has been presented. The compensation strategy was proposed and tested. A fault tolerant drive, based on hardware redundancy, has been developed and presented. Simulation and experimental results are obtained in direct field oriented control algorithm (DFOC) on the laboratory set-up with rapid prototyping card Micro Lab Box DS1202 by dSpace.

Compensation Methods of Current and Speed Sensor Faults for a Vector Controlled Induction Motor Drive System

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Chosen speed and current sensor fault detectors for a vector controlled induction motor drive system have been presented. Systems based on the artificial...

Compensation Methods of Current and Speed Sensor Faults for a Vector Controlled Induction Motor Drive System

Publié en ligne: 27 oct. 2017

Pages: 57 - 69

Reçu: 19 avr. 2016

Accepté: 21 sept. 2016

DOI: https://doi.org/10.5277/ped170106

© 2017

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.