ANN based three-value logic SVPWM control in CSR

Jinbang Xu, A. Shen, Zhizhuo Wu, Jun Yang, Xuan Yang
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Abstract

To achieve better performance with various load and system parameters in controlling a current-source rectifier (CSR) with less computing cost, a neural-network-based implementation of three-logic space-vector modulation (SVM) is proposed in this research, and the random weight change (RWC)algorithm is employed for on-line parameter tuning. The scheme has been simulated in SABER simulation software and the result is compared with the conventional SVM method. The advantage of the method is explicit with a better performance under a non-rated system load.
基于神经网络的CSR三值逻辑SVPWM控制
为了使电流源整流器在不同负载和系统参数下具有更好的控制性能,且计算成本更低,本研究提出了一种基于神经网络的三逻辑空间矢量调制(SVM)方法,并采用随机权值变化(RWC)算法进行在线参数整定。在SABER仿真软件中对该方案进行了仿真,并与传统的支持向量机方法进行了比较。该方法的优点是明显的,在非额定系统负载下具有更好的性能。
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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