Wavelet and Neural Network-Based Fault Location in Power Systems Using Statistical Analysis of Traveling Wave

This paper describes a traveling wave-based fault location in power systems without using global positioning system (GPS) timing. To extract the transient wave from the recorded waves at the bus bars, wavelet denoising is used. The residual signal in this procedure has a large amount of information about the fault. The proposed algorithm uses the statistical analysis parameters of the traveling wave as the inputs of a defined artificial neural network. All the possible fault types are generated using the ATP-EMTP and results are discussed. Extensive simulation studies indicate that proposed network has a reliable response. 0.92 % Average error shows the power of this algorithm against the reactance-based techniques. In contrary, this is a higher error than the traveling wave-based fault location using GPS timing. Nevertheless, in proposed method because of omitting complicated utilities such as GPS receiver, the costs will be decreased and a higher degree of performance in an efficient manner is achieved.

[1]  M. M. Morcos,et al.  ANN-based techniques for estimating fault location on transmission lines using Prony method , 2001 .

[2]  K. Srinivasan,et al.  A New Fault Location Algorithm for Radial Transmission Lines with Loads , 1989, IEEE Power Engineering Review.

[3]  D. Spoor,et al.  Improved single-ended traveling-wave fault-location algorithm based on experience with conventional substation transducers , 2006, IEEE Transactions on Power Delivery.

[4]  Mladen Kezunovic,et al.  Automated transmission line fault analysis using synchronized sampling at two ends , 1995 .

[5]  Hashim Hizam,et al.  Review of fault location methods for distribution power system , 2009 .

[6]  A. T. Johns,et al.  A new travelling-wave based scheme for fault detection on overhead power distribution feeders , 1992 .

[7]  Mohammad Reza Mosavi,et al.  Error Reduction for GPS Accurate Timing in Power Systems using Kalman Filters and Neural Networks , 2011 .

[8]  J. Izykowski,et al.  Postfault Analysis of Operation of Distance Protective Relays of Power Transmission Lines , 2007, IEEE Transactions on Power Delivery.

[9]  Liang Jie,et al.  Adaptive travelling wave protection algorithm using two correlation functions , 1999 .

[10]  Bao-hui Zhang,et al.  A novel principle of single-ended fault location technique for EHV transmission lines , 2003 .

[11]  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.

[12]  Adly A. Girgis,et al.  A new fault location technique for two- and three-terminal lines , 1992 .

[13]  I. Zamora,et al.  Best ANN structures for fault location in single-and double-circuit transmission lines , 2005, IEEE Transactions on Power Delivery.

[14]  Kwang Y. Lee,et al.  Nonlinear system identification using diagonal recurrent neural networks , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[15]  Mohammad R. Mosavi,et al.  Gps Receivers Timing Data Processing Using Neural Networks: Optimal Estimation and Errors Modeling , 2007, Int. J. Neural Syst..

[16]  Paul S. Addison,et al.  The Illustrated Wavelet Transform Handbook Introductory Theory And Applications In Science , 2002 .

[17]  M. Sanaye-Pasand,et al.  A Traveling-Wave-Based Protection Technique Using Wavelet/PCA Analysis , 2010, IEEE Transactions on Power Delivery.

[18]  Mohammad R. Mosavi,et al.  Wavelet Neural Network for Corrections Prediction in Single-Frequency GPS Users , 2011, Neural Processing Letters.

[19]  Joaquim Melendez,et al.  Comparison of impedance based fault location methods for power distribution systems , 2008 .

[20]  Mohammad Reza Mosavi,et al.  Fault Location Techniques in Power System based on Traveling Wave using Wavelet Analysis and GPS Timing , 2012 .

[21]  José Ramón Saenz,et al.  Selecting ANN structures to find transmission faults , 2001 .

[22]  M. R. Mosavi,et al.  A practical approach for accurate positioning with L1 GPS receivers using neural networks , 2006, J. Intell. Fuzzy Syst..