An effective method based on time reversal and optimization techniques for locating faults on power grids - Archive ouverte HAL Access content directly
Journal Articles IEEE Sensors Journal Year : 2021

An effective method based on time reversal and optimization techniques for locating faults on power grids

(1) , (1) , (1) , (1)
1

Abstract

Electromagnetic time reversal (EMTR) has recently emerged as a promising technique applied for locating faults in power networks. It directly transposes the idea of focusing energy back to its source introduced in original time-reversal (TR) methods. Accordingly, we present in this paper, FasTR, a method based on the tenets of TR, that estimates the fault location by employing optimization based algorithms for fetching the highest peak amplitude with maximum coherence in space and time. However, it uses an alternative approach for executing the cumbersome TR post-processing, thanks to a simplified analytical model capable of evaluating the voltage (or current) at any position and any instant of the tested network resulting from the back-injection of the recorded time-revered signals. FasTR is shown to accurately locate a fault in a complex network with just a basic knowledge of its topology in no more than a couple tens of seconds. More importantly is its ability to locate multiple faults in non-homogeneous networks. The performance of the proposed method is validated by numerical simulations as well as an experimental setup by making reference to a reduced-scale coaxial cable network where real faults are hardware-emulated.
Fichier principal
Vignette du fichier
article_MoussaKafal_SensorsJournal2021.pdf (808.21 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

cea-03122180 , version 1 (26-01-2021)

Identifiers

Cite

Moussa Kafal, Nicolas Gregis, Jaume Benoit, Nicolas Ravot. An effective method based on time reversal and optimization techniques for locating faults on power grids. IEEE Sensors Journal, 2021, 21 (2), pp.1092-1099. ⟨10.1109/JSEN.2020.3000301⟩. ⟨cea-03122180⟩
87 View
68 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More