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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/78

Title: Power quality assessment using an adaptive neural network
Authors: Dash, P K
Swain, D P
Mishra, B R
Rahman, S
Keywords: Fourier analysis
adaptive estimation
harmonic distortion
power supply quality
Issue Date: 1996
Publisher: IEEE
Citation: Proceedings of the 1996 International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth, 8-11 Jan. 1996, New Delhi, 770 - 775 vol.2
Abstract: The paper presents an adaptive neutral network approach for the estimation of harmonic components of a power system and its power quality. The neural estimator is based on the use of an adaptive perceptron consisting of a linear adaptive neuron called Adaline. The learning parameters in the proposed 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 performed using this algorithm. Several numerical tests have been conducted for the adaptive estimation of harmonic components, total harmonic distortion and power quality of power system signals mixed with noise and decaying DC components
Description: Personal 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.
URI: http://hdl.handle.net/2080/78
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