An Approach to Diagnostics of Geomagnetically Induced Currents Based on Ground Magnetometers Data

Abstract

The geomagnetically induced currents (GICs) in extended grounded technological systems are driven by telluric electric fields induced by the rapid changes of the geomagnetic field. The paper is concerned with research on the approach to diagnostics of GIC in the power transmission lines in northwestern Russia based on data from IMAGE magnetometers. Based on the results of the statistical and correlation analysis of the objective function (the level of the GIC recorded at the Vykhodnoy transformer station) and geomagnetic data recorded by the nearby IMAGE magnetometers, the features that best characterize the target variable in a given region are distinguished. Using machine learning (ML) methods, the defined number of feature objects is used to develop the relationship for the GIC diagnostics. Evaluation of the coefficient of determination for a stack of various ML methods revealed that the regression approach and artificial neural networks (ANN) are the best solution for the problem under consideration. Verification tests have shown that ANN-based approach and regression methods provide nearly the same diagnostic accuracy for GIC (the mean square error 0.12 A2). However, ANN-based methods are less interpretable and require more computer resources.

Keywords

• geomagnetically induced currents
• geomagnetic field
• geomagnetic variations
• machine learning