Title :
An adaptive linear combiner for on-line tracking of power system harmonics
Author :
Dash, P.K. ; Swain, D.P. ; Liew, A.C. ; Rahman, Saifur
Author_Institution :
Dept. of Electr. Eng., Regional Eng. Coll., Rourkela, India
fDate :
11/1/1996 12:00:00 AM
Abstract :
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
Keywords :
Fourier transforms; neural nets; parameter estimation; power system analysis computing; power system harmonics; Adaline; Fourier coefficients tracking; adaptive linear combiner; corrupted signal data; decaying DC components; harmonic components estimation; learning parameters; linear adaptive neuron; neural estimation algorithm; on-line tracking; power system harmonics; power system signals; stable difference error equation; Adaptive estimation; Backpropagation algorithms; Difference equations; Frequency estimation; Neural networks; Power engineering and energy; Power harmonic filters; Power system harmonics; System testing; Vectors;
Journal_Title :
Power Systems, IEEE Transactions on
