Overcoming the Limits of the Charge Transient Fault Location Algorithm by the Artificial Neural Network

Roberto Benato, Giovanni Rinzo and Michele Poli
Additional contact information
Roberto Benato: Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Giovanni Rinzo: Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Michele Poli: Terna Rete Italia, 00156 Roma, Italy

Energies, 2019, vol. 12, issue 4, 1-15

Abstract: In this paper, two algorithms for single-ended fault location are presented with reference to the unearthed sub-transmission Italian grid (with a voltage level of 60 kV). Both algorithms deal with the correlation between the ground capacitance charging frequency of sound phases and the fault position. In the former, the frequency response of a lumped parameter circuit in the Laplace domain is linked to the fault distance. With such a simplified lumped parameter circuit, the average error in locating a phase-to-ground (PtG) short circuit is 5.18% (total overhead line length equal to 60 km). Since this error is too high, another approach is presented. In this second algorithm, the frequency spectra of the transient current waveforms are used as a database for the training of an Artificial Neural Network (ANN). With this approach, the average error decreases significantly up to 0.36%. The fault location accuracies of the two proposed methods are compared in order to reveal their strengths and weaknesses. The developed procedures are applied to a single-circuit overhead line and to a double-circuit one, both modelled in the EMTP-rv environment, whereas the fault location algorithms are implemented in the MATLAB environment (for the ANN-based algorithm, the Deep Learning toolbox is used).

Keywords: Artificial Neural Network; charge transient; fault location; lumped parameters circuit (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/4/722/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/4/722/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:4:p:722-:d:208189

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().