This paper proposes an innovative Supervised model Reference Adaptive System (SuMRAS) for the rotor flux linkage identification in Surface Permanent Magnet Synchronous Machines (SPMSM). Estimations are updated in an autonomous way when convergence is reached. The proposed SuMRAS can be applied also to operating motors without requiring manual analysis, hence, it is suitable for large-scale implementation in cloud services. The effectiveness of the proposed approach is assessed in a real-world cloud environment through hardware-in-the-loop (HIL) experiments.
SuMRAS: A new SPMSM parameter identification in cloud computing environment / Costantino, Donatello; Brescia, Elia; Massenio, Paolo Roberto; Serafino, Pietro; Cascella, Giuseppe Leonardo; Cupertino, Francesco. - ELETTRONICO. - (2021), pp. 9425641.297-9425641.302. (Intervento presentato al convegno IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2021 tenutosi a Modena, Italy nel April 8-9, 2021) [10.1109/WEMDCD51469.2021.9425641].
SuMRAS: A new SPMSM parameter identification in cloud computing environment
Donatello Costantino;Elia Brescia;Paolo Roberto Massenio;Pietro Serafino;Giuseppe Leonardo Cascella;Francesco Cupertino
2021-01-01
Abstract
This paper proposes an innovative Supervised model Reference Adaptive System (SuMRAS) for the rotor flux linkage identification in Surface Permanent Magnet Synchronous Machines (SPMSM). Estimations are updated in an autonomous way when convergence is reached. The proposed SuMRAS can be applied also to operating motors without requiring manual analysis, hence, it is suitable for large-scale implementation in cloud services. The effectiveness of the proposed approach is assessed in a real-world cloud environment through hardware-in-the-loop (HIL) experiments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
