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Application of Neural Network Structure in Voltage Vector Selection of Direct Torque Control Induction Motor

机译:神经网络结构在直接转矩控制感应电动机电压矢量选择中的应用

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摘要

Direct Torque Control (DTC) of Induction Motor drive has quick torque response without complex orientation transformation and inner loop current control. Although DTC has some drawbacks, such as the torque and flux ripple. The important point in DTC is the right selection of the stator voltage vector. This paper presents simple structured neural network for stator voltage vector selection for induction motors using direct torque control (DTC) method. The Gradient Descent with momentum back-propagation technique has been used to train the neural network. The simple structure network facilitates a short training and processing times. The twelve sector stator voltage vector selector based DTC scheme compared with the proposed scheme and the results are validated through simulation.
机译:感应电动机驱动器的直接转矩控制(DTC)具有快速的转矩响应,而无需复杂的方向转换和内环电流控制。尽管DTC有一些缺点,例如转矩和磁通波动。 DTC中的重点是正确选择定子电压矢量。本文提出了一种使用直接转矩控制(DTC)方法的用于感应电动机定子电压矢量选择的简单结构化神经网络。具有动量反向传播技术的梯度下降技术已被用于训练神经网络。结构简单的网络有助于缩短培训和处理时间。与提出的方案相比,基于十二扇区定子电压矢量选择器的DTC方案进行了仿真验证。

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