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

• Published in: IEEE transactions on power systems Vol. 11; no. 4; pp. 1730 - 1735
• Main Authors: Dash, P.K., Swain, D.P., Liew, A.C., Rahman, S.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.11.1996; Institute of Electrical and Electronics Engineers
• 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; Amplitude; On line; Electrical network; Phase shift; Harmonic analysis; Tracking task; Adaptive estimation; Perturbation; Neural network
• ISSN: 0885-8950
• DOI: 10.1109/59.544635

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.