Neural-network-based space-vector PWM of a three-level inverter covering overmodulation region and performance evaluation on induction motor drive

Cong Wang, B. Bose, V. Oleschuk, S. Mondal, J. Pinto
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引用次数: 10

Abstract

A multi-layer feedforward artificial neural-network (ANN) based implementation of space-vector pulse width modulation algorithm for a three-level voltage-fed inverter has been described in the paper that fully covers the undermodulation and overmodulation regions with linear transfer characteristics extending operation smoothly up to square-wave. The ANN, when implemented by dedicated application-specific IC chip, permits simple, fast and reliable operation far exceeding the capability of digital signal processor (DSP). The network receives the voltage magnitude and angular position of the command space vector at the input, and generates the digital words for switching times at the output that are then converted to symmetrical pulse widths for the three phases through simple logic circuits and a single timer. Several alternative ANN topologies have been proposed after successful training, and their structures and performances have been compared. The ANN-based modulator was then incorporated in induction motor drive systems with rotor flux-oriented indirect vector control, and the drive performances were evaluated extensively. In all the cases, performances were found to be excellent.
一种覆盖过调制区域的三电平逆变器的神经网络空间矢量PWM及感应电机驱动性能评价
本文描述了一种基于多层前馈人工神经网络(ANN)的空间矢量脉宽调制算法在三电平电压馈电逆变器中的实现,该算法完全覆盖了具有线性传输特性的欠调制和过调制区域,将工作平滑地扩展到方波。该人工神经网络采用专用IC芯片实现,操作简单、快速、可靠,远远超过数字信号处理器(DSP)的能力。该网络在输入端接收指令空间矢量的电压幅度和角位置,并在输出端产生开关时间的数字字,然后通过简单的逻辑电路和单个定时器转换为三相的对称脉冲宽度。在训练成功后,提出了几种备选的人工神经网络拓扑,并对它们的结构和性能进行了比较。将基于人工神经网络的调制器应用于具有转子磁链定向间接矢量控制的异步电机驱动系统中,并对其驱动性能进行了广泛的评价。在所有的案例中,表现都很出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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