A Fault Classification and Localization Method for Three-Terminal Circuits Using Machine Learning

This paper presents a traveling-wave-based method for fault classification and localization for three-terminal power transmission systems. In the proposed method, the discrete wavelet transform is utilized to extract transient information from the recorded voltages. Support-vector-machine classifiers are then used to classify the fault type and faulty line/half in the transmission networks. Bewley diagrams are observed for the traveling-wave patterns and the wavelet coefficients of the aerial mode voltage are used to locate the fault. Alternate Transients Program software is used for transients simulations. The performance of the method is tested for different fault inception angles, different fault resistances, nonlinear high impedance faults, and nontypical faults with satisfactory results.

[1]  S. Rajendra,et al.  Travelling-Wave Techniques Applied to the Protection of Teed Circuits:???Multi-Phase/Multi-Circuit System , 1985, IEEE Power Engineering Review.

[2]  P. McLaren,et al.  Travelling-Wave Techniques Applied to the Protection of Teed Circuits:- Multi-Phase/Multi-circuit Sytstem , 1985, IEEE Transactions on Power Apparatus and Systems.

[3]  B. Cory,et al.  A Travelling Wave-Based Fault Locator for Two- and Three-Terminal Networks , 1986, IEEE Transactions on Power Delivery.

[4]  B. Kulicke,et al.  Neural network approach to fault classification for high speed protective relaying , 1995 .

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  Huisheng Wang,et al.  Fuzzy-neuro approach to fault classification for transmission line protection , 1998 .

[7]  Ali Abur,et al.  Fault location using wavelets , 1998 .

[8]  A. Y. Chikhani,et al.  Power quality detection and classification using wavelet-multiresolution signal decomposition , 1999 .

[9]  R.W. Dunn,et al.  A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Whei-Min Lin,et al.  A Fault Classification Method by RBF Neural Network with OLS Learning Procedure , 2001 .

[12]  O.A.S. Youssef,et al.  New algorithm to phase selection based on wavelet transforms , 2002, IEEE Power Engineering Society Summer Meeting,.

[13]  S. Osowski,et al.  Accurate fault location in the power transmission line using support vector machine approach , 2004, IEEE Transactions on Power Systems.

[14]  B. Biswal,et al.  Higher order statistics-fuzzy integrated scheme for fault classification of a series-compensated transmission line , 2004, IEEE Transactions on Power Delivery.

[15]  Zwe-Lee Gaing,et al.  Wavelet-based neural network for power disturbance recognition and classification , 2004, IEEE Transactions on Power Delivery.

[16]  B. Das,et al.  Fuzzy-logic-based fault classification scheme for digital distance protection , 2005, IEEE Transactions on Power Delivery.

[17]  M. Kezunovic,et al.  Fuzzy ART neural network algorithm for classifying the power system faults , 2005, IEEE Transactions on Power Delivery.

[18]  A. Abur,et al.  Travelling wave based fault location for teed circuits , 2005, IEEE Transactions on Power Delivery.

[19]  M. Kizilcay,et al.  Digital simulation of fault arcs in power systems , 2007 .

[20]  G. Panda,et al.  Fault Classification and Section Identification of an Advanced Series-Compensated Transmission Line Using Support Vector Machine , 2007, IEEE Transactions on Power Delivery.

[21]  Irene Yu-Hua Gu,et al.  Support Vector Machine for Classification of Voltage Disturbances , 2007, IEEE Transactions on Power Delivery.

[22]  J. Izykowski,et al.  A Fault-Location Method for Application With Current Differential Relays of Three-Terminal Lines , 2007, IEEE Transactions on Power Delivery.

[23]  Biswarup Das,et al.  Combined Wavelet-SVM Technique for Fault Zone Detection in a Series Compensated Transmission Line , 2008, IEEE Transactions on Power Delivery.

[24]  Xinzhou Dong,et al.  Fault Classification and Faulted-Phase Selection Based on the Initial Current Traveling Wave , 2009, IEEE Transactions on Power Delivery.

[25]  Mario Oleskovicz,et al.  Combined solution for fault location in three-terminal lines based on wavelet transforms , 2010 .

[26]  A Borghetti,et al.  Integrated Use of Time-Frequency Wavelet Decompositions for Fault Location in Distribution Networks: Theory and Experimental Validation , 2010, IEEE Transactions on Power Delivery.

[27]  Zhiqian Bo,et al.  Fault Detection and Classification in EHV Transmission Line Based on Wavelet Singular Entropy , 2010, IEEE Transactions on Power Delivery.

[28]  Ying-Tung Hsiao,et al.  A Hybrid Framework for Fault Detection, Classification, and Location—Part I: Concept, Structure, and Methodology , 2011, IEEE Transactions on Power Delivery.

[29]  Adisa A. Jimoh,et al.  Fault location in a series compensated transmission line based on wavelet packet decomposition and s , 2011 .

[30]  Ying-Tung Hsiao,et al.  A Hybrid Framework for Fault Detection, Classification, and Location—Part II: Implementation and Test Results , 2011, IEEE Transactions on Power Delivery.

[31]  K. Ramar,et al.  A combined impedance and traveling wave based fault location method for multi-terminal transmission lines , 2011 .

[32]  H. Livani,et al.  A hybrid fault location method for overhead lines combined with underground cables using DWT and SVM , 2012, 2012 IEEE Power and Energy Society General Meeting.

[33]  A. A. Yusuffa,et al.  Fault location in a series compensated transmission line based on wavelet packet decomposition and support vector regression , 2015 .