@inproceedings{68210b5083d049628e526733d19c3445,
title = "Estimating current derivatives for sensorless motor drive applications",
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.",
keywords = "Estimation technique, Field Programmable Gate Array (FPGA), Neural network, Self-sensing control, Sensorless control",
author = "David Hind and Mark Sumner and Chris Gerada",
note = "Publisher Copyright: {\textcopyright} 2015 EPE Association and IEEE.; 17th European Conference on Power Electronics and Applications, EPE-ECCE Europe 2015 ; Conference date: 08-09-2015 Through 10-09-2015",
year = "2015",
month = oct,
day = "27",
doi = "10.1109/EPE.2015.7311672",
language = "English",
series = "2015 17th European Conference on Power Electronics and Applications, EPE-ECCE Europe 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 17th European Conference on Power Electronics and Applications, EPE-ECCE Europe 2015",
address = "United States",
}