An adaptive linear combiner for on-line tracking of power system harmonics

The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on power systems 1996-11, Vol.11 (4), p.1730-1735
Main Authors: Dash, P.K., Swain, D.P., Liew, A.C., Rahman, S.
Format: Article
Language:English
Subjects:
Adaptive estimation
Applied sciences
Backpropagation algorithms
Difference equations
Disturbances. Regulation. Protection
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Frequency estimation
Neural networks
Power engineering and energy
Power harmonic filters
Power networks and lines
Power system harmonics
System testing
Vectors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The paper presents a new approach for the estimation of harmonic components of a power system using a linear adaptive neuron called Adaline. The learning parameters in the proposed neural estimation algorithm are adjusted to force the error between the actual and desired outputs to satisfy a stable difference error equation. The estimator tracks the Fourier coefficients of the signal data corrupted with noise and decaying DC components very accurately. Adaptive tracking of harmonic components of a power system can easily be done using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components of power system signals mixed with noise and decaying DC components.
ISSN:0885-8950
1558-0679
DOI:10.1109/59.544635