Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/2375
Title: Fault detection and classification in a distribution network integrated with distributed generators
Authors: Adewole, Adeyemi Charles 
Tzoneva, Raynitchka 
Issue Date: 2013
Abstract: This paper develops a methodology for application in distribution network fault detection and classification. The proposed methodology is based on wavelet energy spectrum entropy decomposition of disturbance waveforms to extract characteristic features by using level-4 db4 wavelet coefficients. Thus, few input features are required for the implementation. Different simulation scenarios encompassing various fault types at several locations with different load angles, fault resistances, fault inception angles, and load switching are applied to the IEEE 34 Node Test Feeder. In particular, the effects of system changes were investigated by integrating various Distributed Generators (DGs) into the distribution feeder. Extensive studies, verification, and analysis made from the application of this technique validate the approach. Comparison with statistical methods based on standard deviation and mean absolute deviation has shown that the method based on log energy entropy is very reliable, accurate, and robust
Description: IEEE Power & Energy Society Conference and Exposition in Africa: Intelligent Grid Integration of Renewable Energy Resources
URI: http://hdl.handle.net/11189/2375
http://dx.doi.org/10.1109/PowerAfrica.2012.6498611
ISBN: 978-1-4673-2550-9
Appears in Collections:Eng - Conference Proceedings

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