Fault Detection and Classification of Multi-location and Evolving Faults in Double-Circuit Transmission Line Using ANN

Author(s):  
V. Ashok ◽  
A. Yadav ◽  
Vinod Kumar Naik
Author(s):  
Sanjay Kumar Mishra

<p align="center"> </p><p align="center"> </p><table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>This paper discusses the time-frequency transform based fault detection and classification of STATCOM (Static synchronous compensator) integrated single circuit transmission line. Here, fast-discrete S-Transform (FDST) based time-frequency transformation is proposed for evaluation of fault detection and classification including STATCOM in transmission line. The STATCOM is placed at mid-point of transmission line. The system starts processing by extracting the current signals from both end of current transformer (CT) connected in transmission line. The current signals from CT’s are fed to FDST to compute the spectral energy (SE) of phase current at both end of the line. The differential spectral energy (DSE) is evaluated by subtracting the SE obtained from sending end and SE obtained from receiving end of the line. The DSE is the key indicator for deciding the fault pattern detection and classification of transmission line. This proposed scheme is simulated using MATLAB simulink R2010a version and successfully tested under various parameter condition such as fault resistance (Rf),source impedance (SI), fault inception angle (FIA) and reverse flow of current. The proposed approach is simple, reliable and efficient as the processing speed is very fast to detect the fault within a cycle period of FDT (fault detection time).</p></td></tr></tbody></table>


Author(s):  
KULKARNISAKEKAR SUMANT SUDHIR ◽  
R.P. HASABE

An appropriate method of fault detection and classification of power system transmission line using discrete wavelet transform is proposed in this paper. The detection is carried out by the analysis of the detail coefficients energy of phase currents. Discrete Wavelet Transform (DWT) analysis of the transient disturbance caused as a result of occurrence faults is performed. The work represented in this paper is focused on classification of simple power system faults using the maximum detail coefficient, energy of the signal and the ratio of energy change of each type of simple simulated fault are characteristic in nature and used for distinguishing fault types.


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