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Continuous wavelet transform and artificial neural network based fault diagnosis in 52 bus hybrid distributed generation systemPublished in Institute of Electrical and Electronics Engineers Inc.
2016
An Artificial Neural Network (ANN) based approach is carried out for power system unsymmetrical fault classification and localization using Continuous Wavelet Transform (CWT) in Hybrid Distributed Generation (HDG) System. In this study, CWT is used as a signal processing tool to extract features of HDG System current signals captured from distribution substation. The extracted features are applied to ANN for fault classification and localization. The simulation results shows a superiority of proposed time frequency domain analysis with CWT which provides a robust and accurate method for detecting and localizing different types of unsymmetrical fault as all faults are correctly classified in this process and the average error in localization is nearly 10.09m. © 2015 IEEE.
Topics: Second-generation wavelet transform (61)%61% related to the paper, Continuous wavelet transform (55)%55% related to the paper, Fault (power engineering) (53)%53% related to the paper and Artificial neural network (51)%51% related to the paper
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About the journal
| Journal | Data powered by Typeset4th Students Conference on Engineering and Systems, SCES 2015 |
|---|---|
| Publisher | Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc. |
Authors (1)
Himadri Lala- Classification of arc fault between broken conductor and high-impedance surface: An empirical mode decomposition and Stockwell transform-based approach
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