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• W4306407732 startingPage "4791" @default.
• W4306407732 abstract "Fast and accurate fault location in DC power systems is of particular importance to ensure their reliable operation. One of the approaches for implementing a fast-tripping protection scheme is to use Traveling waves (TW) initiated by a fault scenario. This paper proposes a physics-informed machine learning approach that utilizes TWs for fault location in DC microgrids. TWs are extracted by the so-called multiresolution analysis which identifies the TW's wavelet coefficients for multiple frequency ranges. This paper deploys Parseval's theorem to find the energy of wavelet coefficients as a quantitative metric for describing TWs. The hypothesis of this paper is that once the Parseval energy curves for a specific cable are extracted, they can be utilized to locate faults along with that cable regardless of the DC system in which the cable is deployed. The fault location algorithm uses Parseval energy curves to train a Gaussian Process (GP) estimator. With the Parseval energy values of measured current at the protection device location, the GP estimator is able to estimate fault locations with high accuracy. The effectiveness of the proposed algorithm is verified by simulating a DC microgrid system in PSCAD/EMTDC." @default.
• W4306407732 created "2022-10-17" @default.
• W4306407732 creator A5034246711 @default.
• W4306407732 creator A5052042982 @default.
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• W4306407732 date "2022-10-17" @default.
• W4306407732 modified "2023-09-30" @default.
• W4306407732 title "A physics‐informed learning technique for fault location of DC microgrids using traveling waves" @default.
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• W4306407732 doi "https://doi.org/10.1049/gtd2.12642" @default.
• W4306407732 hasPublicationYear "2022" @default.
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