Main Article Content

Masoud Dashtdar Majid Dashtdar

Abstract

Electricity distribution systems are subject to a variety of faults such as permanent and transient short circuits due to the extent and multiplicity of equipment. In principle, short circuit fault causes the existing protective equipment to operate and to no electricity the various parts of the distribution network. Rapid and accurate determination of fault location, repair and recovery, it has not prevented the distribution of energy. This will satisfy consumers and prevent the losses of electricity companies. In this paper, the artificial neural network and fault current profiles are used to determine the distance of the fault, determine the type of fault and detect the short circuit. This method provides the information needed to locate the fault by sampling the current before and after the fault occurs from the SCADA system. The effect of connectivity local resistance changes and the effect of load changes on fault location were evaluated. The results show that this method is more accurate than the voltage drop profile variation method in determining the fault distance and short circuit breakdown. If only the net fault current changes profile is used, the effect of the load changes in determining the short-circuit breakdown is much less.

Article Details

Keywords

Distribution network Faultlocation Artificial neural network Net fault current profile

Refrences
[1] R. Salim, K. Salim, A. Bretas. Further improvements on impedance-based fault location for power distribution systems. IET Generation, Transmission & Distribution. 5 (2011) 467-78.
[2] A. Nikoobakht, J. Aghaei, H.R. Massrur, R. Hemmati. Decentralised hybrid robust/stochastic expansion planning in coordinated transmission and active distribution networks for hosting large-scale wind energy. IET Generation, Transmission & Distribution. 14 (2019) 797-807.
[3] I.S. Association. IEEE Guide for Determining Fault Location on AC Transmission and Distribution Lines. Std C37. (2014) 114-2014.
[4] H. Mokhlis, H. Li. Non-linear representation of voltage sag profiles for fault location in distribution networks. International Journal of Electrical Power & Energy Systems. 33 (2011) 124-30.
[5] M. Michalik, W. Rebizant, M. Lukowicz, S.-J. Lee, S.-H. Kang. High-impedance fault detection in distribution networks with use of wavelet-based algorithm. IEEE Transactions on Power Delivery. 21 (2006) 1793-802.
[6] M. Pourahmadi-Nakhli, A.A. Safavi. Path characteristic frequency-based fault locating in radial distribution systems using wavelets and neural networks. IEEE Transactions on Power Delivery. 26 (2010) 772-81.
[7] A. Rafinia, J. Moshtagh. A new approach to fault location in three-phase underground distribution system using combination of wavelet analysis with ANN and FLS. International Journal of Electrical Power & Energy Systems. 55 (2014) 261-74.
[8] R. Dashti, J. Sadeh. Fault section estimation in power distribution network using impedance-based fault distance calculation and frequency spectrum analysis. IET Generation, Transmission & Distribution. 8 (2014) 1406-17.
[9] Y. Liao. A novel method for locating faults on distribution systems. Electric power systems research. 117 (2014) 21-6.
[10] A. Herrera-Orozco, J. Mora-Flórez, S. Pérez-Londoño. An impedance relation index to predict the fault locator performance considering different load models. Electric power systems research. 107 (2014) 199-205.
[11] S. Das, N. Karnik, S. Santoso. Distribution fault-locating algorithms using current only. IEEE transactions on power delivery. 27 (2012) 1144-53.
[12] R. Krishnathevar, E.E. Ngu. Generalized impedance-based fault location for distribution systems. IEEE transactions on power delivery. 27 (2011) 449-51.
[13] S. Hassantabar, Z. Wang, N.K. Jha. SCANN: Synthesis of compact and accurate neural networks. arXiv preprint arXiv:190409090. (2019).
[14] S. Hassantabar, X. Dai, N.K. Jha. STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods. arXiv preprint arXiv:191205831. (2019).
[15] M.R.C. Qazani, H. Asadi, S. Nahavandi. High-Fidelity Hexarot Simulation-Based Motion Platform Using Fuzzy Incremental Controller and Model Predictive Control-Based Motion Cueing Algorithm. IEEE Systems Journal. (2019).
[16] M.R. Gharib, A. Daneshvar. Quantitative-fuzzy Controller Design for Multivariable Systems with Uncertainty. International Journal of Control, Automation and Systems. 17 (2019) 1515-23.
[17] F. Dehghani, H. Nezami. A new fault location technique on radial distribution systems using artificial neural network. (2013).
[18] W.-H. Chen. Fault section estimation using fuzzy matrix-based reasoning methods. IEEE Transactions on Power Delivery. 26 (2010) 205-13.
[19] F.B. Leão, R.A. Pereira, J.R. Mantovani. Fast fault section estimation in distribution control centers using adaptive genetic algorithm. International Journal of Electrical Power & Energy Systems. 63 (2014) 787-805.
[20] S. Tasoujian, S. Salavati, M.A. Franchek, K.M. Grigoriadis. Robust delay-dependent LPV synthesis for blood pressure control with real-time Bayesian parameter estimation. IET Control Theory & Applications. (2020).
[21] S. Tasoujian, S. Salavati, M. Franchek, K. Grigoriadis. Robust IMC-PID and parameter-varying control strategies for automated blood pressure regulation. International Journal of Control, Automation and Systems. 17 (2019) 1803-13.
[22] S. Tasoujian, B. Ebrahimi, K. Grigoriadis, M. Franchek. Parameter-varying loop-shaping for delayed air-fuel ratio control in lean-burn SI engines. Dynamic Systems and Control Conference. American Society of Mechanical Engineers2016. p. V001T01A9.
[23] S. Tasoujian, S. Salavati, K. Grigoriadis, M. Franchek. Real-time cubature Kalman filter parameter estimation of blood pressure response characteristics under vasoactive drugs administration. 2020 American Control Conference (ACC). IEEE2020. pp. 3355-62.
[24] M. Zakershahrak, A. Sonawane, Z. Gong, Y. Zhang. Interactive plan explicability in human-robot teaming. 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE2018. pp. 1012-7.
[25] M. Zakershahrak, Z. Gong, N. Sadassivam, Y. Zhang. Online explanation generation for human-robot teaming. arXiv preprint arXiv:190306418. (2019).
[26] M.R.C. Qazani, H. Asadi, S. Nahavandi. A New Gantry-Tau-Based Mechanism Using Spherical Wrist and Model Predictive Control-Based Motion Cueing Algorithm. Robotica. (2019) 1-22.
[27] S. Datta-Barua, R. Parvizi, E. Donarski, S. Stevanovic, N. Wang, K. Herron, et al. Great Lake Surface Characterization with GNSS Reflectometry. Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016)2016. pp. 872-80.
[28] R. Parvizi, J. Henry, N. Honda, E. Donarski, B.S. Pervan, S. Datta-Barua. Coordination of GNSS Signals with LiDAR for Reflectometry. Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)2017. pp. 342-3433.
[29] R. Parvizi, H.S. Zadeh, L. Pan, B. Pervan, S. Datta-Barua. Multi-sensor Study of Lake Michigan’s Surface using GNSS-Reflectometry. Proceedings of the 31st International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2018)2018.
[30] R. Parvizi, S. Datta-Barua. De-noising GNSS-Reflectometry Measurements from a Freshwater Surface. 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC). IEEE2019. pp. 1-.
[31] S. Ghayouraneh, S.M. El-Ghazaly, J.M. Rankin. Dynamic addressing for on-demand mobility. 2018 Aviation Technology, Integration, and Operations Conference2018. p. 4152.
[32] A. Ghadimi, S. Asadi. Modelling of composite right/left?handed active multiconductor transmission lines (AMCTL) in time domain. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields. 31 (2018) e2257.
[33] D.T. Feeders. IEEE PES distribution system analysis subcommittee. OnlineAvailable: http://www ewh ieee org/soc/pes/dsaco m/testfeeders/index html. (2011).
[34] M. Dashtdar. Fault location in distribution network based on fault current analysis using artificial neural network. Mapta Journal of Electrical and Computer Engineering (MJECE). 1 (2018) 18-32.
[35] M. Dashtdar, R. Dashti, H.R. Shaker. Distribution network fault section identification and fault location using artificial neural network. 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE). IEEE2018. pp. 273-8.
[36] M. Dashtdar, M. Dashtdar. Fault location in the transmission network using a discrete wavelet transform. American Journal of Electrical and Computer Engineering. 3 (2019) 30-7.
[37] M. Dashtdar, M. Esmailbeag, M. Najafi. Fault Location in the Transmission Network based on Zero-sequence Current Analysis using Discrete Wavelet Transform and Artificial Neural Network. (2019).
[38] M. Dashtdar, M. Dashtdar. Fault Location in Distribution Network Based on Phasor Measurement Units (PMU). The Scientific Bulletin of Electrical Engineering Faculty. 19 (2019) 38-43.
[39] M. Dashtdar, M. Dashtdar. Detecting the fault section in the distribution network with distributed generators based on optimal placement of smart meters. The Scientific Bulletin of Electrical Engineering Faculty. 19 (2019) 28-34.
[40] M. Dashtdar, M. Dashtdar. Fault location in the transmission network based on extraction of fault components using wavelet transform. The Scientific Bulletin of Electrical Engineering Faculty. 19 (2019) 1-9.
[41] M. Dashtdar, M. Esmaeilbeig, M. Najafi, M.E.N. Bushehri. Fault Location in the Transmission Network Using Artificial Neural Network. Automatic Control and Computer Sciences. 54 (2020) 39-51.
Section
Electrical Engineering
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How to Cite

Dashtdar, M., & Dashtdar, M. (2020). Fault Location in Distribution Network Based on Fault Current Profile and The Artificial Neural Network. Mapta Journal of Electrical and Computer Engineering (MJECE), 2(1), 30-41. https://doi.org/10.33544/mjece.v2i1.121