Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/5712
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dc.contributor.authorAdewole, Adeyemi Charlesen_US
dc.contributor.authorTzoneva, Raynitchkaen_US
dc.contributor.authorBehardien, Shaheenen_US
dc.date.accessioned2017-06-13T08:34:23Z-
dc.date.available2017-06-13T08:34:23Z-
dc.date.issued2016-
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2016.05.013-
dc.identifier.urihttp://hdl.handle.net/11189/5712-
dc.description.abstractFault location in power system distribution networks is especially difficult because of the existence of several laterals/tap-offs in distribution networks. This implies that the calculated fault point can be wrongly estimated to be in any of the laterals. This paper proposes a new hybrid method combining Discrete Wavelet Transform (DWT) and artificial neural network (ANN) for fault section identification (FSI) and fault location (FL) in power system distribution networks. DWT was used in the analysis and extraction of the characteristic features from fault transient signals of the three phase line current measurements obtained at a single substation relaying point, rather than the double-ended approach used in the existing literature. Entropy Per Unit (EPU) indices are afterwards computed from the DWT decomposition, and are used as input to multi-layer ANN models serving as FSI classifiers and FL predictors respectively. The proposed hybrid method is tested using a benchmark IEEE 34-node test feeder. Comparisons, verification, and analysis made using the experimental results obtained from the application of the method showed very good performance for different fault types, fault locations, fault inception angles, and fault resistances. The proposed hybrid method is unique because of the pre-processing stage done with the DWT-EPU indices, the use of only line current measurements from a single relaying point, and the division of the FSI and FL tasks into sub-problems with respective ANN models.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/za/-
dc.subjectArtificial neural networken_US
dc.subjectDiscrete waveform transformen_US
dc.subjectFault locationen_US
dc.subjectFault section identificationen_US
dc.subjectWavelet energy spectrum entropyen_US
dc.titleDistribution network fault section identification and fault location using wavelet entropy and neural networksen_US
dc.type.patentArticleen_US
Appears in Collections:Appsc - Journal Articles (DHET subsidised)
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