An improved artificial neural network for a direct-power control based on instantaneous power-ripple minimization of the shunt active-power filter

被引：0

作者：

Lazreg, Mohamed Haithem ^{[1
]}

Bentaallah, Abderrahim ^{[2
]}

Mesai-Ahmed, Hamza ^{[2
,3
]}

Djeriri, Youcef ^{[2
]}

机构：

[1] Univ Tlemcen, Lab Automat Tlemcen LAT, Tilimsen, Algeria

[2] Univ Djillali Liabes Sidi Bel Abbes, Lab ICEPS, Sidi Bel Abbes, Algeria

[3] Univ Beira Interior, Electromechatron Syst Res Ctr, CISE, Covilha, Portugal

来源：

ELEKTROTEHNISKI VESTNIK | 2022年 / 89卷 / 04期

关键词：

Shunt Active-Power Filter (SAPF); Direct-Power Control (DPC); Artificial Neural Networks (ANN); Harmonics compensation; Nonlinear load; CONVERTER;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The paper presents a method for a direct-power control (DPC) based on Artificial Neural Networks (ANN) applied to a shunt active-power filter (SAPF). The aim is to improve the performance of conventional controls. SAPF is one of the most advanced pollution control solutions. DPC is a high-performance control for PWM converters based on the instantaneous-power theory. However, the presented control has some drawbacks, such as the presence of ripples in the current. To improve the performance of the system to be controlled, artificial neural networks are applied to the conventional control. To achieve the objective, DPC-ANN is combined with conventional DPC using MATLAB/Simulink, Simulation results show a very satisfactory performance.

引用

页码：181 / 187

页数：7

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