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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/780

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contributor.authorSamantaray, S R-
contributor.authorPanigrahi, B K-
contributor.authorDash, P K-
date.accessioned2009-02-28T05:41:53Z-
date.available2009-02-28T05:41:53Z-
date.issued2008-
identifier.citationIET Generation, Transmission & Distribution, Vol 2, No 2, P 261-270en
identifier.urihttp://dx.doi.org/10.1049/iet-gtd:20070319-
identifier.urihttp://hdl.handle.net/2080/780-
descriptionCopyright for the paper belongs to IETen
description.abstractAn intelligent approach for high impedance fault (HIF) detection in power distribution feeders using advanced signal-processing techniques such as time–time and time–frequency transforms combined with neural network is presented. As the detection of HIFs is generally difficult by the conventional over-current relays, both time and frequency information are required to be extracted to detect and classify HIF from no fault (NF). In the proposed approach, S- and TT-transforms are used to extract time–frequency and time–time distributions of the HIF and NF signals, respectively. The features extracted using S- and TT-transforms are used to train and test the probabilistic neural network (PNN) for an accurate classification of HIF from NF. A qualitative comparison is made between the HIF classification results obtained from feed forward neural network and PNN with same features as inputs. As the combined signal-processing techniques and PNN take one cycle for HIF identification from the fault inception, the proposed approach was found to be the most suitable for HIF classification in power distribution networks with wide variations in operating conditions.en
format.extent699504 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIETen
subjectfault diagnosisen
subjectfeature extractionen
subjectfeedforward neural netsen
subjectpattern classificationen
subjectpower distribution protectionen
subjectpower system analysis computingen
subjectstatistical distributionsen
titleHigh impedance fault detection in power distribution networks using time¿frequency transform and probabilistic neural networken
typeArticleen
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