Fast estimation of voltage and current phasors in power networks using an adaptive neural network

Author

Dash, P.K. ; Panda, S.K. ; Mishra, Baburam ; Swain, D.P.

Author_Institution

Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore

Volume

12

Issue

4

fYear

1997

fDate

11/1/1997 12:00:00 AM

Firstpage

1494

Lastpage

1499

Abstract

A new algorithm for the estimation of parameters of voltage or current waveform of power networks contaminated by noise is proposed. The problem of estimation is formulated by using an adaptive neural network consisting of linear adaptive neurons called adaline. The learning parameters of the adaline are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation, rather than to minimize an error function. Illustrative computer simulation results confirm the validity and accurate performance of the proposed method. Laboratory test results are also presented in this paper to support the effectiveness of the proposed approach in tracking the waveforms in real-time

Keywords

digital simulation; neural nets; parameter estimation; power system analysis computing; adaline; adaptive neural network; computer simulation; current phasors estimation; laboratory test results; learning parameters; linear adaptive neurons; noise contaminated waveforms; power networks; real-time waveform tracking; stable difference error equation; voltage phasors estimation; Adaptive systems; Computer errors; Computer simulation; Difference equations; Laboratories; Neural networks; Neurons; Parameter estimation; Testing; Voltage;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.627847

Filename

627847