Estimating current derivatives for sensorless motor drive applications

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

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: https://nottingham-repository.worktribe.com/output/761706
• Publisher URL: http://ieeexplore.ieee.org/document/7311672/
• Related Public URLs: http://event-epe2015.web.cern.ch/
• 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.