Neural Network Based Switching State Selection For Direct Power Control of Three Phase PWM-Rectifier

被引：0

作者：

Fekik, Arezki ^{[1
]}

Denoun, Hakim ^{[1
]}

Hamida, Mohamed Lamine ^{[1
]}

Azar, Ahmad Taher ^{[2
,3
]}

Atig, Mandi ^{[1
]}

Zhu, Quan Min ^{[4
]}

机构：

[1] Mouloud Mammeri Univ, Engn Adv Technol Lab LATAGE, Tizi Ouzou, Algeria

[2] Benha Univ, Fac Comp & Informat, Banha, Egypt

[3] Nile Univ, Sch Engn & Appl Sci, Giza, Egypt

[4] Univ West England, Dept Engn Design & Math, Bristol, Avon, England

来源：

PROCEEDINGS OF 2018 10TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL (ICMIC) | 2018年

关键词：

Direct Power Control (DPC); Artificial Neural Network (ANN); Total harmonic distortion (TIM); Proportional Integral (PI); PWM-rectifier; DPC;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article proposes an intelligent approach to the Direct Power Control technique of the PWM rectifier, this control technique improves the performance of PWM converter, called Direct Power Control Based on Artificial Neural Network (ANN), applied for the selection of the optimal control vector. DPC-ANN ensures smooth control of active and reactive power in all Sectors and reduces current ripple. Finally, the developed DPC was tested by simulation, the simulation results proved the excellent performance of the proposed DPC scheme.

引用

页数：6

共 21 条

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