Model Predictive Control (MPC) has become an effective control strategy, particularly in Multiphase Induction Machines (MIMs). Unlike their three-phase counterparts, MIMs have additional degrees of freedom, known as (x − y) voltages or currents. MPC can integrate diverse constraints through a predefined cost function to regulate (x − y) components, but this can come at the cost of disturbing the flux and torque production. To address this challenge, a new approach has been introduced in this paper: Model Predictive Torque Control using Virtual Vectors (PTC-VV) for a six-phase IM. This approach aims to regulate copper losses in the (x − y) plane, which classic PTC cannot achieve using a single switching state during the sampling period. This work demonstrates the effectiveness of using virtual vectors in torque control for six-phase IMs through comprehensive simulation studies. The PTC-VV approach provides robust reference tracking for torque, flux, and stator (α − β) and (x − y) current regulations. This results in enhanced efficiency and adaptability of the control system, marking a notable advancement in PTC techniques. Additionally, this approach reduces the (x − y) currents in six-phase IMs.


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    Titel :

    Model Predictive Torque Control based on Virtual Vectors for Six-Phase Induction Machines


    Beteiligte:
    Gonzalez, Osvaldo (Autor:in) / Doval-Gandoy, Jesus (Autor:in) / Ayala, Magno (Autor:in) / Maidana, Paola (Autor:in) / Medina, Christian (Autor:in) / Rodas, Jorge (Autor:in) / Romero, Carlos (Autor:in) / Delorme, Larizza (Autor:in) / Maciel, Ricardo (Autor:in) / Gregor, Raul (Autor:in)


    Erscheinungsdatum :

    19.06.2024


    Format / Umfang :

    2176473 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch