Classification of partial discharge events in GILBS using discrete wavelet transform and probabilistic neural networks

2012 ◽  
Author(s):  
Ming-Shou Su ◽  
Jiann-Fuh Chen ◽  
Chien-Yi Chen ◽  
Cheng-Chi Tai ◽  
Yu-Hsun Lin
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Probabilistic Neural Networks

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

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