Abstract

The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system. Variable speed drives are an example. with the widespread proliferation of nonlinear loads in a power distribution network, the voltage at the point of common coupling is rarely a pure sinusoid. It has become necessary to identify accurately which load(s) is injecting the excessively high harmonic currents. Simply measuring the harmonic currents at each individual load is not sufficiently accurate since these harmonic currents may be caused by not only the nonlinear load, but also by a non-sinusoidal PCC voltage. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a three phase variable speed drive, and this technique can be extended to any nonlinear load in general. the proposed method has been experimentally verified by applying the scheme to a commercially available variable speed drive. the scheme has been applied to each phase individually as well as to all three phases together. the goal of this paper is to quantify the difference in current distortion of a load when supplied from a distorted source as compared to a clean sine wave. a Multilayer Perceptron Neural Network is used to estimate the true harmonic current distortion of a load. Theory and practical results are presented. This technology could be integrated into any commercially available power quality instrument or be fabricated as a standalone instrument.

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

978-078039716-3

International Standard Serial Number (ISSN)

0275-9306

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Dec 2006

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