A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement

Ramesh Rudraram, Sasi Chinnathambi
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引用次数: 0

Abstract

— A Novel Intelligent control of a Unified Power Quality Conditioner (UPQC) coupled with Photovoltaic (PV) system is proposed in this work. The utilization of a Re-lift Luo converter in conjunction with a Cascaded Artificial Neural Network (ANN) Maximum Power Point Tracking (MPPT) method facilitates the optimization of power extraction from PV sources. UPQC is made up of a series and shunt Active Power Filter (APF), where the former compensates source side voltage quality issues and the latter compensates the load side current quality issues. The PV along with a series and shunt APFs of the UPQC are linked to a common dc-bus and for regulating a dc-bus voltage a fuzzy tuned Adaptive PI controller is employed. Moreover, a harmonics free reference current is generated with the aid of CNN assisted dq theory in case of the shunt APF. The results obtained from MATLAB simulation.
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一种用于提高电能质量的UPQC与集成太阳能光伏系统的新型智能神经网络技术
--本文提出了一种新型的智能控制与光伏系统耦合的统一电能质量调节器。Re-lift Luo转换器与级联人工神经网络(ANN)最大功率点跟踪(MPPT)方法的结合使用有助于优化光伏电源的功率提取。UPQC由串联和并联有源电力滤波器(APF)组成,前者补偿源侧电压质量问题,后者补偿负载侧电流质量问题。PV以及UPQC的串联和并联APF连接到公共直流母线,并且为了调节直流母线电压,采用了模糊调谐自适应PI控制器。此外,在并联型有源电力滤波器的情况下,借助CNN辅助的dq理论产生了无谐波参考电流。通过MATLAB仿真得到的结果。
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CiteScore
1.50
自引率
14.30%
发文量
0
审稿时长
12 weeks
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