Geomagnetically Induced Currents (GICs) are quasi-DC signals that are induced in the ground during geomagnetic disturbances (GMDs) and pose a large threat to power grid infrastructure. The power industry currently attempts to mitigate GIC effects by utilizing 1-D ground electrical conductivity based electric field predictions, though conductivity can vary in all 3 spatial dimensions by at least 3 orders of magnitude. These variations can cause predictions to deviate from measured values greatly, which suggests that the resulting GIC predictions can be highly incorrect in areas of conductive heterogeneity. 3-D computational techniques, such as solving Maxwell’s equations for electric fields associated with GMDs, can improve prediction accuracy, though they are currently too computationally intensive and slow for industrial use. A computationally light algorithm that implements 3-D magnetotelluric data is proposed and compared to the industry standard method for both electric field and GIC predictions. The algorithm, which is called the Cascading Linear Filter Algorithm (CLFA), utilizes concurrent magnetic time series data from publicly available NSF EarthScope Program sites and United States Geological Survey magnetic observatories to construct observatory-to-site transfer functions. These transfer functions project real-time magnetic field observatory data into predictions for sites within the EarthScope array. Real-time site magnetic field predictions are then projected through 3-D site impedances to yield real-time electric field predictions, which can be interpolated onto points representing the tested power system. GIC based parameters for the power system, and the corresponding electric field at points along the system, are then input into power flow solvers to obtain GIC predictions. Electric field predictions for the CLFA and industry standard method are compared to measured site values to assess the accuracy of both techniques, and limitations are addressed. GIC predictions resulting from both electric field prediction methods are also compared for three test cases to estimate the impact of implementing the CLFA method on power grid resilience.
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