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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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| DC Field | Value | Language |
|---|
| contributor.author | Samantaray, S R | - |
| contributor.author | Panigrahi, B K | - |
| contributor.author | Dash, P K | - |
| date.accessioned | 2009-02-28T05:41:53Z | - |
| date.available | 2009-02-28T05:41:53Z | - |
| date.issued | 2008 | - |
| identifier.citation | IET Generation, Transmission & Distribution, Vol 2, No 2, P 261-270 | en |
| identifier.uri | http://dx.doi.org/10.1049/iet-gtd:20070319 | - |
| identifier.uri | http://hdl.handle.net/2080/780 | - |
| description | Copyright for the paper belongs to IET | en |
| description.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 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.extent | 699504 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | IET | en |
| subject | fault diagnosis | en |
| subject | feature extraction | en |
| subject | feedforward neural nets | en |
| subject | pattern classification | en |
| subject | power distribution protection | en |
| subject | power system analysis computing | en |
| subject | statistical distributions | en |
| title | High impedance fault detection in power distribution networks using time¿frequency transform and probabilistic neural network | en |
| type | Article | en |
| Appears in Collections: | Journal Articles
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| File |
Description |
Size | Format |
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| srs6.pdf | | 683Kb | Adobe PDF | View/Open |
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