Application of artificial intelligence techniques in the operation of neutral-point clamped rectifier under perturbed conditions

被引：0

作者：

Sharma D. ^{[1
]}

机构：

[1] Shri Mata Vaishno Devi University, Jammu and Kashmir, Katra

来源：

International Journal of Power Electronics | 2022年 / 15卷 / 3-4期

关键词：

backpropagation neural network; BPN; harmonics eliminations; minimising potential difference; neural network; neutral-point clamped converter; NPC;

D O I：

10.1504/IJPELEC.2022.122415

中图分类号：

学科分类号：

摘要：

This paper will demonstrate the viability of neural network algorithm for solving power quality problems in three-phase neutral-point clamped converter. A neural network algorithm is proposed here for the better performance of the converter under perturbed conditions. The neural network algorithm modifies conventional space vector pulse width modulation algorithm. The proposed algorithm generates ideal switching sequences for the converter by developing the best path for the reference vector in the hexagon of the space vector. The data set required to train the neural network was collected from well tuned PI controlled model. In this work, the implementation of neural network is described and simulation results are presented using MATLAB/Simulink software. Simulation results proves fast minimising the potential difference at the clamped bus of the converter with minimum switching losses and excellent performance of converter in terms of unity power factor, better total harmonic distortion at the source side. © 2022 Inderscience Publishers. All rights reserved.

引用

页码：334 / 350

页数：16

共 10 条

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