Spatial Correlation and Graph Neural Networks for Earth Fault Localisation on Energy Grids

abstract

Earth Faults are a common occurrence on Distribution Power Grids, and determining the exact fault location in a compensated MV grid structure is a cumbersome process, which can negatively impact grid reliability. It is imperative for the operators in this scenario to find out fast where an earth fault has occured on the grid. This paper proposes a correlation-based earth fault localisation (EFL), which makes a spatial comparision of stored RMS Voltage signature database from distributed measurement points with obtained measurements. Furthermore, we present another RMS-EFL approach based on Gated Graph Neural Networks, which consider the topological structure of the grid as an input to the model. The assessment of both approaches in a real MV grid topology with simulated faults shows that the proposed methods can provide accurate results and that those are robust with respect to parameters that are frequently hard to obtain in practical deployments.