A physics‐informed learning technique for fault location of DC microgrids using traveling waves

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.