A Data-Driven Fault Location Algorithm Based on the Electromagnetic Time Reversal

Zhaoyang Wang, Zhe Chen, Mario Paolone

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    The classical electromagnetic time reversal (EMTR) fault location method in power systems requires multiple simulations in the backward step, assuming different guessed fault locations along the line. This process can be time consuming especially when a high location accuracy is desired, as it may require a significant number of guessed fault locations. To cope with this issue, the concept of EMTR in mismatched media has recently been introduced, allowing reducing the backward simulations to a single run and, thus, substantially improving the computation efficiency of the EMTR-based fault location technique. In this paper, we present a detailed study of the mismatched-media-based mirrored minimum energy property. This property has been applied in a few recent studies but never been theoretically studied and rigorously demonstrated. First, we infer a transfer function that relates the fault source to the voltage along the line resulting from back-injecting the time-reversed transients measured at a given observation point. We present a theorem according to which, at the fault switching frequency and its odd harmonics, the mirrorimage point of the fault location with respect to the line center corresponds to a local minimum of the squared modulus of the transfer function. Then, it is proved that the mirrored minimum energy property is a corollary of this theorem. Based on these theoretical findings, we propose an algorithm that utilizes the reversed-time voltage energy as a fault location metric in the frequency domain. We further advance a data-driven strategy to maximize the computation efficiency of the fault location procedure. The applicability and robustness of the proposed frequencydomain fault location metric are numerically and experimentally validated.
    Original languageEnglish
    JournalIEEE Transactions on Power Delivery
    Issue number5
    Pages (from-to)3709-3721
    Publication statusPublished - 2022


    • Data-driven methods
    • Electromagnetic time reversal
    • Fault location
    • Mismatched media
    • Power systems
    • Transmission lines


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