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Estimating current derivatives for sensorless motor drive applications

Hind, David Martin; Sumner, M.; Gerada, C.

Authors

David Martin Hind

MARK SUMNER mark.sumner@nottingham.ac.uk
Professor of Electrical Energy Systems



Abstract

The PWM current derivative technique for sensorless control of AC machines requires current derivative measurements under certain PWM vectors. This is often not possible under narrow PWM vectors due to high frequency (HF) oscillations which affect the current and current derivative responses. In previous work, researchers extended the time that PWM vectors were applied to the machine for to a threshold known as the minimum pulse width (tmin), in order to allow the HF oscillations to decay and a derivative measurement to be obtained. This resulted in additional distortion to the motor current New experimental results demonstrate that an artificial neural network (ANN) can be used to estimate derivatives using measurements from a standard current sensor before the HF oscillations have fully decayed. This reduces the minimum pulse width required and can significantly reduce the additional current distortion and torque ripple.

Citation

Hind, D. M., Sumner, M., & Gerada, C. (2015). Estimating current derivatives for sensorless motor drive applications

Conference Name 17th European Conference on Power Electronics and Applications (EPE'15 ECCE-Europe)
End Date Sep 10, 2015
Acceptance Date Mar 2, 2015
Publication Date Sep 9, 2015
Deposit Date Jun 7, 2017
Publicly Available Date Jun 7, 2017
Peer Reviewed Peer Reviewed
Keywords Estimation technique, Field Programmable Gate Array (FPGA), Neural network, Self-sensing control, Sensorless control
Public URL http://eprints.nottingham.ac.uk/id/eprint/43407
Publisher URL http://ieeexplore.ieee.org/document/7311672/
Related Public URLs http://event-epe2015.web.cern.ch/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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