<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, a Discrete Wavelet Transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault). The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.</p></abstract>

In this paper, a Discrete Wavelet Transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault). The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.

Fault Location in the Transmission Network Based on Extraction of Fault Components Using Wavelet Transform

Electrical Engineering Department, Bushehr branch

The Scientific Bulletin of Electrical Engineering Faculty

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In this paper, a Discrete Wavelet Transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network...

Fault Location in the Transmission Network Based on Extraction of Fault Components Using Wavelet Transform

Online veröffentlicht: 21. Nov. 2019

Seitenbereich: 1 - 9

DOI: https://doi.org/10.1515/sbeef-2019-0013

Schlüsselwörter
Fault Location, High-frequency components, Signal decomposition, Wavelet transform

© 2019 Majid Dashtdar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fault Location in the Transmission Network Based on Extraction of Fault Components Using Wavelet Transform

Online veröffentlicht: 21. Nov. 2019

Seitenbereich: 1 - 9

DOI: https://doi.org/10.1515/sbeef-2019-0013

SchlüsselwörterFault Location, High-frequency components, Signal decomposition, Wavelet transform

© 2019 Majid Dashtdar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Fault Location, High-frequency components, Signal decomposition, Wavelet transform