Radial Basis Function-Based Neural Network for Harmonics Detection

ASIAIR > College of Computer Science > Department of Computer Science and Information Engineering > Journal Artical > Item 310904400/8966

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8966

Title:	Radial Basis Function-Based Neural Network for Harmonics Detection
Authors:	G. W. Chang;C. I Chen;Y. F. Teng
Date:	2009
Issue Date:	2010-04-15 05:42:30 (UTC+0)
Abstract:	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 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 harmonics assessment.
Relation:	IEEETransactions on Industrial Electronics
Appears in Collections:	[Department of Computer Science and Information Engineering] Journal Artical

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