On-Line Harmonic Estimation in Power System Based on Sequential Training Radial Basis Function Neural Network

被引：0
|
作者：
Almaita, Eyad [1 ]
Asumadu, Johnson A. [1 ]
机构：
[1] Western Michigan Univ, Elect & Comp Engn Dept, Kalamazoo, MI 49008 USA
来源：
2011 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) | 2011年
关键词：
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Harmonic estimation is considered the most crucial part in harmonic mitigation process in power system. Artificial intelligent based on pattern recognition techniques is considered one of dependable methods that can effectively realize highly nonlinear functions. In this paper, a radial basis function neural network (RBFNN) is used to dynamically identify and estimate the fundamental, fifth harmonic, and seventh harmonic components in converter waveforms. The fast training algorithm and the small size of the resulted networks, without hindering the performance criteria, prove effectiveness of the proposed method.
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页数：6
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