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    Investigation on SVM-Backstepping sensorless control of five-phase open-end winding induction motor based on model reference adaptive system and parameter estimation

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    1-s2.0-S221509861831437X-main.pdf (3.014Mb)
    Date
    2019
    Author
    Saad K.
    Abdellah K.
    Ahmed H.
    Iqbal A.
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    Abstract
    This paper deals with a new control technique applied to five-phase induction motor under open-end stator winding (FPIM-OESW) topology using the backstepping nonlinear control. The main objective is to improve the dynamics of this kind of machine, which is intended to be used in high power industrial application, where the maintenance is difficult and the fault tolerant is needed to ensure the continuous motor operating mode with minimized number of interruption. This control technique is combined with the Space Vector Pulse Width Modulation (SVPWM) to maintain a fixed switching frequency. In addition, the Model Reference Adaptive System (MRAS) concept is used for the estimation of the load torque, the rotor flux and the rotor speed to overcome the main drawbacks presented with the previous sensorless systems concepts. However, the great sensitivity to the changes of the motor internal parameters and it operating instability problems, especially in low-speed operating region, that causes an estimation error of the rotor speed, is the most disadvantage of the MRAS technique. Therefore, to solve this problem, an estimation method of the motor internal parameters such as the rotor resistance, the stator resistance and the magnetizing inductance, is proposed in this paper. Where, the main aim is to improve furthermore the control performance, to reduce the computational complexity and to minimize the rotor speed estimation error. The obtained simulation results confirm the enhanced performance and the clear efficacy of the proposed control technique under a variety of cases of different operating conditions. - 2019 Karabuk University
    DOI/handle
    http://dx.doi.org/10.1016/j.jestch.2019.02.008
    http://hdl.handle.net/10576/14541
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    • Electrical Engineering [‎2821‎ items ]

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