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dc.contributor.authorDash, P K-
dc.contributor.authorSwain, D P-
dc.contributor.authorLiew, A C-
dc.contributor.authorRahman, S-
dc.identifier.citationIEEE Transactions on Power Systems, Vol 11, Iss 4, P 1730-1735en
dc.descriptionPersonal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
dc.description.abstractThe 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 componentsen
dc.format.extent566247 bytes-
dc.subjectFourier transformsen
dc.subjectparameter estimationen
dc.subjectpower system harmonicsen
dc.titleAn adaptive linear combiner for on-line tracking of power system harmonicsen
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