A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit

Mohammad Reza Shadi, Hamid Mirshekali, Rahman Dashti, Mohammad-Taghi Ameli and Hamid Reza Shaker
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Mohammad Reza Shadi: Department of Electrical Engineering, Shahid Beheshti University, Tehran 1983969411, Iran
Hamid Mirshekali: Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169113817, Iran
Rahman Dashti: Clinical-Laboratory Center of Power System & Protection, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr 75169113817, Iran
Mohammad-Taghi Ameli: Department of Electrical Engineering, Shahid Beheshti University, Tehran 1983969411, Iran
Hamid Reza Shaker: Center for Energy Informatics, University of Southern Denmark, DK-5230 Odense, Denmark

Energies, 2021, vol. 14, issue 19, 1-15

Abstract: Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.

Keywords: fault section; deep learning; GRU; smart feeder meter; distribution network; real time (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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