Radial-basis-function-based neural network for harmonic detection

Gary W. Chang, Cheng I. Chen, Yu Feng Teng

Research output: Contribution to journalArticlepeer-review

158 Scopus citations

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 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.

Original languageEnglish
Article number5290151
Pages (from-to)2171-2179
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Volume57
Issue number6
DOIs
StatePublished - Jun 2010

Keywords

  • Adaptive linear combiner (ADALINE)
  • Back-propagation neural network
  • Fast Fourier transform (FFT)
  • Harmonics
  • Radial-basis-function neural network (RBFNN)

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