NDLI: Transmission line fault detection and classification

Content Provider	IEEE Xplore Digital Library
Author	Singh, M. Panigrahi, B.K. Maheshwari, R.P.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, Indian Institute of Technology, Delhi, India-110016 (Singh, M.; Panigrahi, B.K.) \|\| Department of Electrical Engineering, Indian Institute of Technology, Roorkee, India-247667 (Maheshwari, R.P.)
Abstract	Transmission line protection is an important issue in power system engineering because 85–87% of power system faults are occurring in transmission lines. This paper presents a technique to detect and classify the different shunt faults on a transmission lines for quick and reliable operation of protection schemes. Discrimination among different types of faults on the transmission lines is achieved by application of evolutionary programming tools. PSCAD/EMTDC software is used to simulate different operating and fault conditions on high voltage transmission line, namely single phase to ground fault, line to line fault, double line to ground and three phase short circuit. The discrete wavelet transform (DWT) is applied for decomposition of fault transients, because of its ability to extract information from the transient signal, simultaneously both in time and frequency domain. The data sets which are obtained from the DWT are used for training and testing the SVM architecture. After extracting useful features from the measured signals, a decision of fault or no fault on any phase or multiple phases of a transmission line is carried out using three SVM classifiers. The ground detection task is carried out by a proposed ground index. Gaussian radial basis kernel function (RBF) has been used, and performances of classifiers have been evaluated based on fault classification accuracy. In order to determine the optimal parametric settings of an SVM classifier (such as the type of kernel function, its associated parameter, and the regularization parameter c), fivefold cross-validation has been applied to the training set. It is observed that an SVM with an RBF kernel provides better fault classification accuracy than that of an SVM with polynomial kernel. It has been found that the proposed scheme is very fast and accurate and it proved to be a robust classifier for digital distance protection.
Starting Page	15
Ending Page	22
File Size	1475147
Page Count	8
File Format	PDF
ISBN	9781424479238
e-ISBN	9781424479269
DOI	10.1109/ICETECT.2011.5760084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-23
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Wavelet transforms Power transmission lines Fault classification accuracy and Ground index Feature extraction Discrete wavelet transforms Transient analysis Multiresolution analysis Support vector machine (SVMs)
Content Type	Text
Resource Type	Article

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