Estimating current derivatives for sensorless motor drive applications
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 pre...
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| Format: | Conference or Workshop Item |
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2015
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| Online Access: | https://eprints.nottingham.ac.uk/43407/ |
| _version_ | 1848796680652062720 |
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| author | Hind, David Martin Sumner, M. Gerada, C. |
| author_facet | Hind, David Martin Sumner, M. Gerada, C. |
| author_sort | Hind, David Martin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:51:50Z |
| format | Conference or Workshop Item |
| id | nottingham-43407 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:51:50Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-434072020-05-04T17:17:29Z https://eprints.nottingham.ac.uk/43407/ Estimating current derivatives for sensorless motor drive applications Hind, David Martin Sumner, M. Gerada, C. 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. 2015-09-09 Conference or Workshop Item PeerReviewed Hind, David Martin, Sumner, M. and Gerada, C. (2015) Estimating current derivatives for sensorless motor drive applications. In: 17th European Conference on Power Electronics and Applications (EPE'15 ECCE-Europe), 8-10 Sept 2015, Geneva, Switzerland. Estimation technique Field Programmable Gate Array (FPGA) Neural network Self-sensing control Sensorless control http://ieeexplore.ieee.org/document/7311672/ |
| spellingShingle | Estimation technique Field Programmable Gate Array (FPGA) Neural network Self-sensing control Sensorless control Hind, David Martin Sumner, M. Gerada, C. Estimating current derivatives for sensorless motor drive applications |
| title | Estimating current derivatives for sensorless motor drive applications |
| title_full | Estimating current derivatives for sensorless motor drive applications |
| title_fullStr | Estimating current derivatives for sensorless motor drive applications |
| title_full_unstemmed | Estimating current derivatives for sensorless motor drive applications |
| title_short | Estimating current derivatives for sensorless motor drive applications |
| title_sort | estimating current derivatives for sensorless motor drive applications |
| topic | Estimation technique Field Programmable Gate Array (FPGA) Neural network Self-sensing control Sensorless control |
| url | https://eprints.nottingham.ac.uk/43407/ https://eprints.nottingham.ac.uk/43407/ |