SPEED SENSORLESS CONTROL OF INDUCTION MOTORS BASED ON MCA EXIN PISARENKO METHOD
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | SPEED SENSORLESS CONTROL OF INDUCTION MOTORS BASED ON MCA EXIN PISARENKO METHOD |
| 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 component analysis (MCA), neural adaptive filtering, neural networks (NNs), sensorless control |
| Résumé | This paper proposes a speed sensorless technique for high performance induction motor drives based on the retrieval and tracking of the rotor slot harmonic. First, two cascaded ADALINEs (Adaptive Linear Elements) are used to extract the rotor slot harmonic (RSH) from the stator phase current signature, acting as adaptive filters, respectively in configuration band and notch, whose output consists of the RSH. Second, the MCA EXIN neurons are used to extract the eigenvector corresponding to the minimum eigenvalue of the autocorrelation matrix, which is formed by the ADALINEs' output sequence. Then, the slot frequency is estimated by using Pisarenko's theory with this retrieved minimum eigenvector, and subsequently the speed of the motor is estimated. Compared to the original Pisarenko's method however, not only the proposed algorithm can work recursively sample by sample, but the computational complexity and mean square frequency estimation error are largely reduced. 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. |