Automatic Classification of Transmission Line Faults Using Probabilistic Neural Network and Discrete Wavelet Transform
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
The largest physical length of transmission network is the most critical part of the power system. The fast recognition of faults and events in transmission line has a significant role in order to prevent equipment damage and suddenly collapse of power system. The signal-processing and computational-intelligence based techniques have been proposed in literature for automatic classification of faults and events in transmission network. In this paper, discrete wavelet transform based probabilistic neural network have been proposed for the identification and classification of faults in transmission network. The short circuit faults are created at various fault resistances and fault locations. The wavelet transform is used to extract the features in order to distinguish the type of faults. The probabilistic neural network is used to automatically classify the type of faults. A real-time transmission network is used for simulation of faults. The simulation results show that the proposed algorithm is efficient and reliable for automatic classification of faults in electrical power system.
Authors and Affiliations
Suhail Khokhar, Suhail Mustafa, Adnan Ahmed Arain, Mohsin Ali Tunio
Keywords
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- EP ID EP24360
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