A novel artificial neural network based selection harmonic reduction technique for single source fed high gain switched capacitor coupled multilevel inverter for renewable energy applications

一种基于新型人工神经网络的单源馈电高增益开关电容耦合多电平逆变器(适用于可再生能源应用)谐波抑制选择技术

阅读:1

Abstract

Multilevel inverters (MLIs) are commonly used in renewable energy systems for their high-quality output, low total harmonic distortion (THD), and reduced component count. This study presents a high-gain, single-source MLI designed for renewable applications like solar or wind power. It features a novel topology with twice the voltage-boosting factor, utilizing a single DC source. The inverter achieves thirteen voltage levels using just 10 power switches and three switched capacitors. The voltage gain is achieved without the need for bulky DC-DC converters or transformers. This is accomplished by configuring the switched capacitors in series and parallel arrangements to attain the desired voltage boost. Additionally, the self-balancing capacitors eliminate the need for extra sensors. Both symmetric and asymmetric variants of the extensible configuration are investigated. The suggested design lowers the total standing voltage (TSV) while achieving high gain. A selective harmonic removal technique using artificial neural networks (ANN) reduces THD by up to 6.07 %. An extensive review of recent literature reveals significant advancements and applications of ANNs in this field. The proposed system's benefits, such as gain factor, total standing voltage (TSV), and minimized device count, are assessed. Comparative analysis reveals that the proposed topology employs fewer components and features a more simplified design. Additionally, the inverter achieves an efficiency of 96.9 %. The design is validated through an experimental prototype after being confirmed with MATLAB/SIMULINK.© YEAR The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the Name of the Conference, Conference Organizer Name, Year or Edition of Conference.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。