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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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| 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
http://hdl.handle.net/2080/780 |
| Appears in Collections: | Journal Articles
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