This paper proposes a Model Modulated Predictive Control (M2PC) specifically developed for Synchronous Reluctance Motors (SynRM) drives and based on a purposely developed magnetic model taking into account both self- and cross-saturation. The proposed M2PC exploits the discrete-time version of the dynamic model to compute the current prediction and the resulting predicted current error. The built-in PWM modulator chooses the optimal pair of voltage space-vector to be applied by the inverter to minimize the current error. The magnetic model permits obtaining good dynamic performance in every working condition.

Accetta A., Cirrincione M., Luna M., Pucci M., Sferlazza A. (2022). A Model Modulated Predictive Current Control Algorithm for the Synchronous Reluctance Motor. In 2022 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 1-7). IEEE [10.1109/ECCE50734.2022.9947741].

A Model Modulated Predictive Current Control Algorithm for the Synchronous Reluctance Motor

Sferlazza A.
2022-11-30

Abstract

This paper proposes a Model Modulated Predictive Control (M2PC) specifically developed for Synchronous Reluctance Motors (SynRM) drives and based on a purposely developed magnetic model taking into account both self- and cross-saturation. The proposed M2PC exploits the discrete-time version of the dynamic model to compute the current prediction and the resulting predicted current error. The built-in PWM modulator chooses the optimal pair of voltage space-vector to be applied by the inverter to minimize the current error. The magnetic model permits obtaining good dynamic performance in every working condition.
30-nov-2022
978-1-7281-9387-8
Accetta A., Cirrincione M., Luna M., Pucci M., Sferlazza A. (2022). A Model Modulated Predictive Current Control Algorithm for the Synchronous Reluctance Motor. In 2022 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 1-7). IEEE [10.1109/ECCE50734.2022.9947741].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/586930
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