Radial-Basis-Function-Based Neural Network for Harmonic Detection

被引：130
|
作者：
Chang, Gary W. [1 ]
Chen, Cheng-I [2 ]
Teng, Yu-Feng [1 ]
机构：
[1] Natl Chung Cheng Univ, Dept Elect Engn, Chiayi 62102, Taiwan
[2] Asia Univ, Dept Comp Sci & Informat Engn, Taichung 41354, Taiwan
来源：
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2010年 / 57卷 / 06期
关键词：
Adaptive linear combiner (ADALINE); back-propagation neural network fast Fourier transform (FFT); harmonics; radial-basis-function neural network (RBFNN); ALGORITHM; FREQUENCY;
D O I：
10.1109/TIE.2009.2034681
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The widespread application of power-electronic loads has led to increasing harmonic pollution in the supply system. In order to prevent harmonics from deteriorating the power quality, detecting harmonic components for harmonic mitigations becomes a critical issue. In this paper, an effective procedure based on the radial-basis-function neural network is proposed to detect the harmonic amplitudes of the measured signal. By comparing with several commonly used methods, it is shown that the proposed solution procedure yields more accurate results and requires less sampled data for harmonic assessment.
引用
收藏
页码：2171 / 2179
页数：9
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