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

Moussa Kafal 1 Nicolas Gregis 1 Jaume Benoit 1 Nicolas Ravot 1 
1 LFIC - Laboratoire Fiabilité et Intégration de Capteurs
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
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.
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Submitted on : Tuesday, January 26, 2021 - 11:24:43 PM
Last modification on : Thursday, February 17, 2022 - 10:08:06 AM
Long-term archiving on: : Tuesday, April 27, 2021 - 7:54:15 PM


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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, Institute of Electrical and Electronics Engineers, 2021, 21 (2), pp.1092-1099. ⟨10.1109/JSEN.2020.3000301⟩. ⟨cea-03122180⟩



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