Sensorless Control of Induction Motors by the MSA based MUSIC Technique
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | Sensorless Control of Induction Motors by the MSA based MUSIC Technique |
| Type de publication | Conference Paper |
| Year of Publication | 2015 |
| Auteurs | Ye B, Cirrincione M, Pucci M, Cirrincione G |
| Conference Name | 2015 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE) |
| Publisher | IEEE; IEEE Power Elect Soc; IEEE Ind Applicat Soc |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-4673-7151-3 |
| Mots-clés | induction motor, minor space analysis (MCA), neural adaptive filtering, neural networks (NNs), speed sensorless |
| Résumé | This paper proposes a speed sensorless technique for induction motor drives based on the retrieval and tracking of the rotor slot harmonics (RSH). The RSH related to the rotor speed is first extracted from the stator phase current signature by the adoption of two cascaded ADALINEs (ADAptive Linear Element), whose output is the estimated slot harmonic. Then, the frequency of this slot harmonic as well as the speed is estimated by using minor space analysis (MSA) EXIN neural networks, which work on-line to iteratively compute the frequency of the slot harmonics based on MUSIC spectrum estimation theory. Thanks to its sample-based learning and the reduced mean square frequency estimation error, the speed estimation is fast and accurate. The proposed sensorless technique has been experimentally tested on a suitably developed test set-up with a 2 kW induction motor drive. It has been verified that this algorithm can track the rotor speed rapidly and accurately in a very wide speed range, working from rated speed down to 1.3% of it. |