A Machine Learning Approach to Detection of Geomagnetically Induced Currents in Power Grids

Abstract

Geomagnetically induced currents (GICs) in power grids are mainly caused by geomagnetic disturbances especially during solar storms. Such currents can potentially cause negative impacts on power grid equipment and even damage the power transformers resulting in a significant risk of blackouts. Therefore, monitoring GICs in power systems and developing solutions to mitigate their impacts before rising to a certain threatening level is urgently in need. Monitoring GICs is, however, quite a challenge and costly, as they usually appear in forms of dc components in the high voltage transmission lines, which are barely accessible through transformers. By examining the measured currents from the current transformers, this article proposes a framework to detect GICs in power transmission systems through a hybrid time-frequency analysis combined with machine learning technology. Simulated results verify that the proposed approach can promisingly estimate GICs in power systems during a variety of grid operating conditions.