Main Article Content
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.
Fault location, Wavelet transform, ANN, WEE, EPU, Artificial Neural Network
 G.D. Ferreira, D.d.S. Gazzana, A.S. Bretas, A.H. Ferreira, A.L. Bettiol, A. Carniato. Impedance-based fault location for overhead and underground distribution systems. North American Power Symposium (NAPS), 2012. IEEE2012. pp. 1-6.
 J.J. Mora-FlÃ³rez, R.A. Herrera-Orozco, A.F. Bedoya-Cadena. Fault location considering load uncertainty and distributed generation in power distribution systems. IET Generation, Transmission & Distribution. 9 (2015) 287-95.
 M. Saha, E. Rosolowski, J. Izykowski. Atp-emtp investigation for fault location in medium voltage networks. International Conference on Power Systems Trasients2005.
 H. Shodja, M. Khezri, A. Hashemian, A. Behzadan. RKPM with augmented corrected collocation method for treatment of material discontinuities. Computer Modeling in Engineering & Sciences(CMES). 62 (2010) 171-204.
 F. Nofeli, H. Arabshahi, M. Tayarani. Steady-State and transient electron transport in bulk ZnO and Zn1-xMgxO semiconductorsâ€™. International Journal of Engineering and Technical Research. 2 (2014).
 R.H. Salim, M. Resener, A.D. Filomena, K.R.C. De Oliveira, A.S. Bretas. Extended fault-location formulation for power distribution systems. IEEE transactions on power delivery. 24 (2009) 508-16.
 A.D. Filomena, M. Resener, R.H. Salim, A.S. Bretas. Fault location for underground distribution feeders: An extended impedance-based formulation with capacitive current compensation. International Journal of Electrical Power & Energy Systems. 31 (2009) 489-96.
 F.H. Magnago, A. Abur. A new fault location technique for radial distribution systems based on high frequency signals. Power Engineering Society Summer Meeting, 1999 IEEE. IEEE1999. pp. 426-31.
 D.W. Thomas, R.J. Carvalho, E.T. Pereira. Fault location in distribution systems based on traveling waves. Power Tech Conference Proceedings, 2003 IEEE Bologna. IEEE2003. p. 5 pp. Vol. 2.
 A. Borghetti, M. Bosetti, M. Di Silvestro, C.A. Nucci, M. Paolone. Continuous-wavelet transform for fault location in distribution power networks: Definition of mother wavelets inferred from fault originated transients. IEEE Transactions on Power Systems. 23 (2008) 380-8.
 R.H. Salim, K.R.C. de Oliveira, A.D. Filomena, M. Resener, A.S. Bretas. Hybrid fault diagnosis scheme implementation for power distribution systems automation. IEEE Transactions on Power Delivery. 23 (2008) 1846-56.
 M. Lotfi, M. Vidyasagar. A fast noniterative algorithm for compressive sensing using binary measurement matrices. IEEE Transactions on Signal Processing. (2018).
 A. Araghi, M. Pasebanpoor. Assessment of Pilot Pollution Problem for Multi-Cell Multi-User MIMO. Journal of Electrical and Electronic Engineering. 6 (2018) 120-8.
 U. Dwivedi, S. Singh, S. Srivastava. A wavelet based approach for classification and location of faults in distribution systems. India Conference, 2008 INDICON 2008 Annual IEEE. IEEE2008. pp. 488-93.
 W. Zhao, Y. Song, Y. Min. Wavelet analysis based scheme for fault detection and classification in underground power cable systems. Electric Power Systems Research. 53 (2000) 23-30.
 K.L. Butler-Purry, J. Cardoso. Characterization of underground cable incipient behavior using time-frequency multi-resolution analysis and artificial neural networks. Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE. IEEE2008. pp. 1-11.
 C. Teo. Automation of knowledge acquisition and representation for fault diagnosis in power distribution networks. Electric power systems research. 27 (1993) 183-9.
 E. Mohamed, N. Rao. Artificial neural network based fault diagnostic system for electric power distribution feeders. Electric Power Systems Research. 35 (1995) 1-10.
 D. Chan, C. Lu. Distribution system fault identification by mapping of characteristic vectors. Electric Power Systems Research. 57 (2001) 15-23.
 M.A. Al-shaher, M.M. Sabry, A.S. Saleh. Fault location in multi-ring distribution network using artificial neural network. Electric Power Systems Research. 64 (2003) 87-92.
 L.S. Martins, V.F. Pires, C. Alegria. A new accurate fault location method using Î±Î² space vector algorithm. 14th PSCC (Power Systems Computation Conference)2002. pp. 1-6.
 J. Mora-Florez, J. Cormane-Angarita, G. Ordonez-Plata. K-means algorithm and mixture distributions for locating faults in power systems. Electric Power Systems Research. 79 (2009) 714-21.
 D. Thukaram, H. Khincha, H. Vijaynarasimha. Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery. 20 (2005) 710-21.
 F. Chunju, K. Li, W. Chan, Y. Weiyong, Z. Zhaoning. Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines. International Journal of Electrical Power & Energy Systems. 29 (2007) 497-503.
 M. Lotfi, B. Nazari, S. Sadri, N.K. Sichani. The detection of dacrocyte, schistocyte and elliptocyte cells in iron deficiency anemia. Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on. IEEE2015. pp. 1-5.
 M. Lotfi, M. Vidyasagar. A fast single-pass algorithm for compressive sensing based on binary measurement matrices. Communication, Control, and Computing (Allerton), 2017 55th Annual Allerton Conference on. IEEE2017. pp. 369-73.
 P. Bhowmik, P. Purkait, K. Bhattacharya. A novel wavelet transform aided neural network based transmission line fault analysis method. International Journal of Electrical Power & Energy Systems. 31 (2009) 213-9.
 S. Hongchun, S. Xiangfei, S. Dajun. A new method for locating faults on transmission lines based on rough set and FNN. Power System Technology, 2002 Proceedings PowerCon 2002 International Conference on. IEEE2002. pp. 2584-8.
 J. Sadeh, H. Afradi. A new and accurate fault location algorithm for combined transmission lines using adaptive network-based fuzzy inference system. Electric Power Systems Research. 79 (2009) 1538-45.
 A.C. Adewole, R. Tzoneva, S. Behardien. Distribution network fault section identification and fault location using wavelet entropy and neural networks. Applied soft computing. 46 (2016) 296-306.
 S. Samantaray, B. Panigrahi, P. Dash. High impedance fault detection in power distribution networks using timeâ€“frequency transform and probabilistic neural network. IET generation, transmission & distribution. 2 (2008) 261-70.
 Z. He, L. Fu, S. Lin, Z. Bo. Fault detection and classification in EHV transmission line based on wavelet singular entropy. IEEE transactions on Power Delivery. 25 (2010) 2156-63.
 S. Ekici, S. Yildirim, M. Poyraz. Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Systems with Applications. 34 (2008) 2937-44.
This work is licensed under a Creative Commons Attribution 4.0 International License.
The copyright in the text of individual articles (including research articles, opinion articles, book reviews, conference proceedings and abstracts) is the property of their respective authors, subject to a general license granted to Mapta Publishing Group and a Creative Commons CC-BY licence granted to all others, as specified below. The compilation of all content on this site, as well as the design and look and feel of this website are the exclusive property of Mapta Publishing Group.
All contributions to Mapta Publishig Group may be copied and re-posted or re-published in accordance with the Creative Commons licence referred to below.
Articles and other user-contributed materials may be downloaded and reproduced subject to any copyright or other notices.
As an author or contributor you grant permission to others to reproduce your articles, including any graphics and third-party materials supplied by you, in accordance with the Mapta Publishing GroupTerms and Conditions and subject to any copyright notices which you include in connection with such materials. The licence granted to third parties is a Creative Common Attribution ("CC BY") licence. The current version is CC-BY, version 4.0 (http://creativecommons.org/licenses/by/4.0/), and the licence will automatically be updated as and when updated by the Creative Commons organisation.