New algorithm for detection and fault classification on parallel transmission line using DWT and BPNN based on Clarke’s transformation

This paper presents a new algorithm for fault detection and classification using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation on parallel transmission. Alpha and beta (mode) currents generated by Clarke’s transformation were used to con...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 168; s. 983 - 993
Hlavní autoři: Asuhaimi Mohd Zin, Abdullah, Saini, Makmur, Mustafa, Mohd Wazir, Sultan, Ahmad Rizal, Rahimuddin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 30.11.2015
Témata:
Back-propagation neural network
Clarke’s transformation
Fault detection
Fault location
Transmission parallel line
Wavelet transformation
Back-propagation neural network
Fault location
Clarke’s transformation
Wavelet transformation
Fault detection
Transmission parallel line
ISSN:0925-2312, 1872-8286
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Popis
Shrnutí:This paper presents a new algorithm for fault detection and classification using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation on parallel transmission. Alpha and beta (mode) currents generated by Clarke’s transformation were used to convert the signal of discrete wavelet transform (DWT) to get the wavelet transform coefficients (WTC) and the wavelet energy coefficient (WEC). Daubechies4 (Db4) was used as a mother wavelet to decompose the high frequency components of the signal error. The simulation was performed using PSCAD/EMTDC for transmission system modeling. Simulation was performed at different locations along the transmission line with different types of fault and fault resistance, fault location and fault initial angle on a given power system model. Four statistic methods utilized are in the present study to determine the accuracy of detection and classification faults. The results show that the best Clarke transformation occurred on the configuration of 12-24-48-4, respectively. For instance, the errors using mean square error method, the errors of BPNN, Pattern Recognition Network and Fit Network are 0.03721, 0.13115 and 0.03728, respectively. This indicates that the BPNN results are the lowest error.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.05.026