A neural-network-based method of modeling electric arc furnace load for power engineering study

Gary W. Chang, Cheng I. Chen, Yu Jen Liu

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

It is known that artificial neural network is a powerful scheme for function learning and modeling nonlinear loads. However, a direct application of artificial neural network for modeling time-varying loads may lead to inaccuracies. This paper presents an accurate neural-network-based method for modeling the highly nonlinear voltage-current characteristic of an ac electric arc furnace (EAF). The neural-network-based model can be effectively used to assess waveform distortions, voltage fluctuations, and performances of reactive power compensation devices associated with the EAF in a power system. Simulation results obtained by using the proposed model are compared with the actual measured data and two other traditional neural network models. It is shown that the proposed method yields favorable performance and can be applied for modeling similar types of nonlinear loads for power engineering studies.

Original languageEnglish
Article number5353756
Pages (from-to)138-146
Number of pages9
JournalIEEE Transactions on Power Systems
Volume25
Issue number1
DOIs
StatePublished - Feb 2010

Keywords

  • Electric arc furnace (EAF)
  • Neural network
  • Nonlinear load
  • Radial basis function
  • Voltage-current characteristic

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