RT info:eu-repo/semantics/conferencePaper
T1 Comparative assessment of model predictive current control strategies applied to six-phase induction machines
A1 González Barrios, Osvaldo Julián
A1 Ayala Silva, Magno Elías
A1 Romero Aquino, Carlos Alberto Aníbal
A1 Rodas Benítez, Jorge Esteban
A1 Gregor Recalde, Raúl Igmar
A1 Delorme Diarte, Silvia Larizza
A1 González Prieto, Ignacio
A1 Durán, Mario Javier
A1 Rivera, Marco
A2 Universidad Nacional de Asunción. Facultad de Ingeniería
A2 Universidad del Cono Sur de las Américas
K1 Current control
K1 Induction machines
K1 Mathematical model
K1 Predictive models
K1 Rotors
K1 Stators
K1 Switches
AB Nowadays, model predictive current control strategy has become a viable alternative because of its fast response for high-reliability systems, such as multiphase machines. In that regard, this paper proposes a comparative assessment of four current controllers based on the model using different approaches as virtual vectors, modulation techniques and further, combining these strategies in order to deal at the same time with the regulation of the main and secondary currents components, known as (α-ß) and (x-y), respectively, applied to six-phase induction machines. Simulation results are presented so as to show the effectiveness of the four model predictive current controllers, taking into account the mean squared error and the total harmonic distortion of the stator currents in both steady and dynamic conditions.
PB Institute of Electrical and Electronics Engineers
SN 978-1-7281-5754-2
YR 2020
FD 2020-04-16
LK http://hdl.handle.net/20.500.14066/4650
UL http://hdl.handle.net/20.500.14066/4650
LA eng
NO González, O., Ayala. M., Romero, C., Rodas, J., Gregor, R., Delorme, L., González-Prieto, I., Durán, M. J., & Rivera, M. (2020, 26-28 de febrero). Comparative assessment of model predictive current control strategies applied to six-phase induction machines [Artículo de la Conferencia]. 2020 IEEE International Conference on Industrial Technology (ICIT), Buenos Aires, Argentina.https://doi.org/10.1109/ICIT45562.2020.9067279
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 03-sep-2026