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首页> 外文期刊>International Journal of Electrical and Computer Engineering >High –Performance using Neural Networks in Direct Torque Control for Asynchronous Machine
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High –Performance using Neural Networks in Direct Torque Control for Asynchronous Machine

机译:在异步电机直接转矩控制中使用神经网络实现高性能

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This article investigates solution for the biggest problem of the Direct Torque Control on the asynchronous machine to have the high dynamic performance with very simple hysteresis control scheme. The Conventional Direct Torque Control (CDTC) suffers from some drawbacks such as high current, flux and torque ripple, as well as flux control at very low speed. In this paper, we propose an intelligent approach to improve the direct torque control of induction machine which is an artificial neural networks control. The principle, the numerical procedure and the performances of this method are presented. Simulations results show that the proposed ANN-DTC strategy effectively reduces the torque and flux ripples at low switching frequency, compared with Fuzzy Logic DTC and The Conventional DTC.
机译:本文研究非常简单的磁滞控制方案,从而在异步电机上具有高动态性能的直接转矩控制最大问题的解决方案。常规的直接转矩控制(CDTC)具有一些缺点,例如高电流,磁通和转矩脉动以及极低速下的磁通控制。在本文中,我们提出了一种智能方法来改善感应电机的直接转矩控制,这是一种人工神经网络控制。介绍了该方法的原理,数值过程和性能。仿真结果表明,与模糊逻辑DTC和常规DTC相比,所提出的ANN-DTC策略在低开关频率下可有效降低转矩和磁通波动。

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