基于DSIM平衡策略的5级DTC-ANN

E. Benyoussef, S. Barkat
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引用次数: 1

摘要

本文提出了一种人工神经网络控制器,用于改善由两个五电平二极管箝位逆变器供电的双星感应电机直接转矩控制策略的性能。所得到的控制方案具有足够的自由度来控制转矩和磁链,具有非常低的纹波和高动态。不幸的是,二极管箝位逆变器具有直流电容电压变化的固有问题。为了克服这一问题,提出了一种基于人工神经网络的直接转矩控制与平衡策略,以抑制直流链路电容电压的不平衡。仿真结果表明了所提控制方法的有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Five-level DTC-ANN with Balancing Strategy of DSIM
This paper presents an artificial neural networks controller devoted to improve the performance of direct torque control strategy of double star induction machine fed by two five-level diode-clamped inverters. The resulting control scheme presents enough degrees of freedom to control both torque and flux with very low ripple and high dynamics. Unfortunately, the diode-clamped inverter has an inherent problem of DC-link capacitors voltages variations. To overcome this problem, an artificial neural network based direct torque control with balancing strategy is proposed to suppress the unbalance of DC-link capacitor voltages. Simulations results are given to show the effectiveness and the robustness of the suggested control method.
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