Using Probabilistic Neural Network for Classification High Impedance Faults on Power Distribution Feeders
An intelligent approach probabilistic Neural Network (PNN) combined with advanced signalprocessing techniques such as Discrete Wavelet Transform (DWT) is presented for detection High impedance faults (HIFs) on power distribution networks. HIFs detection is usually very difficult using the common o...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
IDOSI Publications
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/9340/1/marizan%2310.pdf http://eprints.utem.edu.my/id/eprint/9340/ http://www.idosi.org/wasj/wasj23(10)13/1.pdf |
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| Summary: | An intelligent approach probabilistic Neural Network (PNN) combined with advanced signalprocessing
techniques such as Discrete Wavelet Transform (DWT) is presented for detection High impedance
faults (HIFs) on power distribution networks. HIFs detection is usually very difficult using the common over
current devices, both frequency and time data are needed to get the exact information to classify and detect no
fault from HIF. In this proposed method, DWT is used to extract features of the no fault and HIF signals.
The features extracted using DWT which comprises the energy, standard deviation, mean, root mean square
and mean of energy of detail and approximate coefficients of the voltage, current and power signals are utilized
to train and test the PNN for a precise classification of no fault from HIFs. The proposed method shows that
it is more convenient for HIF detection in distribution systems with ample varying in operating cases. |
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