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

Title: High impedance fault detection in power distribution networks using time¿frequency transform and probabilistic neural network
Authors: Samantaray, S R
Panigrahi, B K
Dash, P K
Keywords: fault diagnosis
feature extraction
feedforward neural nets
pattern classification
power distribution protection
power system analysis computing
statistical distributions
Issue Date: 2008
Publisher: IET
Citation: IET Generation, Transmission & Distribution, Vol 2, No 2, P 261-270
Abstract: An 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 fr...
Description: Copyright for the paper belongs to IET
URI: http://dx.doi.org/10.1049/iet-gtd:20070319
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