Real-Time Selective Harmonic Minimization for Multilevel Inverters Connected to Solar Panels Using Artificial Neural Network Angle Generation

被引：105

作者：

Filho, Faete ^{[1
]}

Tolbert, Leon M. ^{[1
,2
]}

Cao, Yue ^{[1
]}

Ozpineci, Burak ^{[3
]}

机构：

[1] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA

[2] Oak Ridge Natl Lab, Power Elect & Elect Machinery Res Ctr, Knoxville, TN 37932 USA

[3] Oak Ridge Natl Lab, Power Elect & Elect Machinery Grp, Oak Ridge, TN 37831 USA

来源：

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS | 2011年 / 47卷 / 05期

关键词：

Artificial neural network; cascade; genetic algorithm; harmonic elimination; multilevel inverter; photovoltaic; POWER-SYSTEMS; ELIMINATION; CONVERTERS;

D O I：

10.1109/TIA.2011.2161533

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This work approximates the selective harmonic elimination problem using artificial neural networks (ANNs) to generate the switching angles in an 11-level full-bridge cascade inverter powered by five varying dc input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195-W solar panel. The angles were chosen such that the fundamental was kept constant and the low-order harmonics were minimized or eliminated. A nondeterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50-/60-Hz time window).

引用

页码：2117 / 2124

页数：8

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