Fault Location in Distribution Network Based on Fault Current Analysis Using Artificial Neural Network

In this study, fault location is implemented on an IEEE-15bus sample network using artificial neural network. The basis of this work is such that initially, in order to train the neural network, a series of specific characteristic are extracted by the relay from the observed fault signal. These characteristics are obtained by wavelet transform which properly extracts high and low frequency coefficients of the signal. Hence, since high frequencies are generated during the occurrence of the fault, signal information could be extracted using wavelet transform. After wavelet transform, the entropies of the minor components of the sequences could be obtained using statistics to extract the hidden features inside them and present them to train the neural network. Also, since the obtained inputs for the training of the neural network depend on the fault angle, resistance and location, the training data should be selected such that these differences be properly presented so the neural network does not face any issues in its identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters and their combinations are important. The simulation results show the good performance of neural network for the faults in different angles, locations, and resistances.

[1]  Juan Mora-Flórez,et al.  Fault location considering load uncertainty and distributed generation in power distribution systems , 2015 .

[2]  K.L. Butler-Purry,et al.  Characterization of underground cable incipient behavior using time-frequency multi-resolution analysis and artificial neural networks , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[3]  Adly A. Girgis,et al.  A fault location technique for rural distribution feeders , 1991, [Proceedings] 1991 Rural Electric Power Conference. Papers presented at the 35th Annual Conference.

[4]  M. Khezri,et al.  RKPM with Augmented Corrected Collocation Method for Treatment of Material Discontinuities , 2010 .

[5]  M. Paolone,et al.  Continuous-Wavelet Transform for Fault Location in Distribution Power Networks: Definition of Mother Wavelets Inferred From Fault Originated Transients , 2008, IEEE Transactions on Power Systems.

[6]  Mathukumalli Vidyasagar,et al.  A Fast Noniterative Algorithm for Compressive Sensing Using Binary Measurement Matrices , 2017, IEEE Transactions on Signal Processing.

[7]  Ali Abur,et al.  A new fault location technique for radial distribution systems based on high frequency signals , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[8]  Anahita Araghi,et al.  Assessment of Pilot Pollution Problem for Multi-Cell Multi-User MIMO , 2018 .

[9]  Bijaya Ketan Panigrahi,et al.  High impedance fault detection in power distribution networks using time-frequency transform and probabilistic neural network , 2008 .

[10]  A.S. Bretas,et al.  Extended Fault-Location Formulation for Power Distribution Systems , 2009, IEEE Transactions on Power Delivery.

[11]  A. S. Bretas,et al.  Impedance-based fault location for overhead and underground distribution systems , 2012, 2012 North American Power Symposium (NAPS).

[12]  David Thomas,et al.  Fault location in distribution systems based on traveling waves , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[13]  D. Thukaram,et al.  Artificial neural network and support vector Machine approach for locating faults in radial distribution systems , 2005, IEEE Transactions on Power Delivery.

[14]  Raynitchka Tzoneva,et al.  Distribution network fault section identification and fault location using wavelet entropy and neural networks , 2016, Appl. Soft Comput..

[15]  Arturo S. Bretas,et al.  Fault location for underground distribution feeders: An extended impedance-based formulation with capacitive current compensation , 2009 .

[16]  Behzad Nazari,et al.  The detection of Dacrocyte, Schistocyte and Elliptocyte cells in Iron Deficiency Anemia , 2015, 2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA).

[17]  Sami Ekici,et al.  Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition , 2008, Expert Syst. Appl..

[18]  D.T.W. Chan,et al.  Distribution system fault identification by mapping of characteristic vectors , 2001 .

[19]  C. Y. Teo Automation of knowledge acquisition and representation for fault diagnosis in power distribution networks , 1993 .

[20]  Eugeniusz Rosolowski,et al.  ATP-EMTP Investigation for Fault Location in Medium Voltage Networks , 2005 .

[21]  V. Fernao Pires,et al.  A NEW ACCURATE FAULT LOCATION METHOD USING α β SPACE VECTOR ALGORITHM , 2002 .

[22]  S.N. Singh,et al.  A wavelet based approach for classification and location of faults in distribution systems , 2008, 2008 Annual IEEE India Conference.

[23]  Juan Mora-Flórez,et al.  k-means algorithm and mixture distributions for locating faults in power systems , 2009 .

[24]  Mathukumalli Vidyasagar,et al.  A fast single-pass algorithm for compressive sensing based on binary measurement matrices , 2017, 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[25]  Javad Sadeh,et al.  A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System , 2009 .

[26]  Shu Hongchun,et al.  A new method for locating faults on transmission lines based on rough set and FNN , 2002, Proceedings. International Conference on Power System Technology.

[27]  A.S. Bretas,et al.  Hybrid Fault Diagnosis Scheme Implementation for Power Distribution Systems Automation , 2008, IEEE Transactions on Power Delivery.

[28]  Y. H. Song,et al.  Wavelet analysis based scheme for fault detection and classification in underground power cable systems , 2000 .

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

[30]  E. A. Mohamed,et al.  Artificial neural network based fault diagnostic system for electric power distribution feeders , 1995 .

[31]  Zhang Zhaoning,et al.  Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines , 2007 .

[32]  Meshal Al-Shaher,et al.  FAULT LOCATION IN MULTI-RING DISTRIBUTION NETWORK USING ARTIFICIAL NEURAL NETWORK , 2003 .

[33]  P. S. Bhowmik,et al.  A novel wavelet transform aided neural network based transmission line fault analysis method , 2009 .