Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator

Jung Wook Park, Ronald G. Harley, Ganesh K. Venayagamoorthy

Research output: Contribution to conferencePaper

41 Citations (Scopus)

Abstract

This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN), for the on-line identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization, and on-line global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster, and global minimum convergence is achieved even when operating conditions change.

Original languageEnglish
Pages274-279
Number of pages6
Publication statusPublished - 2002 Jan 1
Event2002 IEEE Power Engineering Society Winter Meeting - New York, NY, United States
Duration: 2002 Jan 272002 Jan 31

Other

Other2002 IEEE Power Engineering Society Winter Meeting
CountryUnited States
CityNew York, NY
Period02/1/2702/1/31

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Park, J. W., Harley, R. G., & Venayagamoorthy, G. K. (2002). Comparison of MLP and RBF neural networks using deviation signals for on-line identification of a synchronous generator. 274-279. Paper presented at 2002 IEEE Power Engineering Society Winter Meeting, New York, NY, United States.