Simplified Modulated Model Predictive Control of Synchronous Reluctance Motor

Behnam Nikmaram, Alireza Davari, Peyman Naderi, Jose Rodriguez, Cristian Garcia

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión exhaustiva

Resumen

Modulated model predictive control (M2PC) method is a solution to achieve a fixed switching frequency for model predictive control. This method, compared to the conventional finite control set model predictive control (FCS-MPC,) has a less THD and ripple for the outputs. In the FCS-MPC method, a cost function is used to determine the optimal vector in every sampling interval. But in the M2PC method, in which a modulator is utilized, three cost functions are used instead of one to determine the optimum vectors. The M2PC method resolves some problems of the FCS-MPC method. But because it has a high computational volume, it is more difficult to implement and it costs more. This paper presents a simplified modulated model predictive control (SM2PC) strategy to reduce the computations. This method is applied to the control of synchronous reluctance motor. Simulation results are presented in order to evaluate the proposed method.

Idioma originalInglés
Título de la publicación alojada2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728158495
DOI
EstadoPublicada - feb 2020
Evento11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020 - Tehran, República Islámica de Irán
Duración: 4 feb 20206 feb 2020

Serie de la publicación

Nombre2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020

Conferencia

Conferencia11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
PaísRepública Islámica de Irán
CiudadTehran
Período4/02/206/02/20

Áreas temáticas de ASJC Scopus

  • Ingeniería energética y tecnologías de la energía
  • Energías renovables, sostenibilidad y medio ambiente
  • Ingeniería automovilística
  • Ingeniería eléctrica y electrónica
  • Ingeniería mecánica
  • Control y optimización

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