基于有源电力线调节器的自适应神经网络抑制电流谐波

M. Banaei, S. Hosseini
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引用次数: 6

摘要

本文研究的是专用有源电力线调节器(APLC),它与UPQC一样,是基于串联和并联电源转换器共用一条公共直流链路的集成。并联逆变器的主要目的是补偿由非线性负载引起的无功功率和电流谐波。此外,针对非线性电流负载中谐波电流分量的分离问题,提出了一种结构简单的前馈神经网络。已知在并联路径和并联变压器中也可以提出降低损耗的方法。APLC串联功率变换器的目的是控制潮流,以降低配电系统的损耗,建议APLC在实现UPQC功能的同时,可以有效地降低并联路径的功率损耗。基于PSCAD/EMTDC的仿真结果表明,采用自适应神经网络的策略可以有效地降低敏感非线性负载的功率损耗和消除谐波电流
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mitigation of Current Harmonic Using Adaptive Neural Network with Active Power Line Conditioner
This paper deals with special active power line conditioner (APLC), which is based, the same as UPQC, on integration of series and shunt power converters sharing a common DC link. In this paper the main purpose of the shunt inverter is to compensate the reactive power and current harmonics caused by nonlinear load. In addition, a simple structure feed forward neural network is presented for the separate of harmonic current components in nonlinear current load. It is known that loss reduction can be proposed in the parallel paths and also parallel transformers too. The aim of series power converter of APLC is to control power flow in order to reduce losses of distribution system, suggested APLC not only performs the functions of UPQC but also can effectively reduce power losses in parallel paths. Simulation results, carried out by PSCAD/EMTDC, show that the proposed strategy using adaptive neural network can effectively reduce power losses and eliminate harmonic currents for sensitive nonlinear loads
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