scholarly journals A Discrete Wavelet Transform based STATCOM Compensated Transmission Line

2018 ◽  
Vol 7 (4.5) ◽  
pp. 61
Author(s):  
S. K Mishra ◽  
L. N Tripathy ◽  
S. C Swain
Keyword(s):  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Transmission Lines ◽  
Reactive Power ◽  
Spectral Energy ◽  
Discrete Wavelet ◽  
Current Signal ◽  
Static Synchronous Compensator ◽  
Phase Current

This paper describes the Discrete Wavelet transform (DWT) applied for classification and detection of fault with respect to the position of the Static Synchronous Compensator (STATCOM).  STATCOM is positioned at mid-point of the transmission line. The reactive power injection or absorption (i.e shifting phase voltage with respect to bus voltage) depends upon the apparent impedance during the fault. The current signals of each phase are retrieved from both sending and receiving end of three phase transmission line synchronously. It starts processing through DWT to acquire spectral energy (SE) content of each phase current signals from both end of the transmission line. Differential spectral energy (DSE) of each phase current signals (i.e SE of current signal recovered from sending end minus SE of current signal recovered at receiving end) of transmission lines is used to record the fault configuration. 

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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