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.

中文翻译：

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一种检测电网中地磁感应电流的机器学习方法

电网中的地磁感应电流 (GIC) 主要由地磁扰动引起，尤其是在太阳风暴期间。此类电流可能对电网设备造成负面影响，甚至损坏电力变压器，导致重大停电风险。因此，迫切需要监控电力系统中的 GIC 并制定解决方案以在其影响上升到某个威胁水平之前减轻其影响。然而，监测 GIC 是一项相当大的挑战且成本高昂，因为它们通常以直流分量的形式出现在高压传输线路中，几乎无法通过变压器进行访问。通过检查来自电流互感器的测量电流，本文提出了一个框架，通过结合机器学习技术的混合时频分析来检测电力传输系统中的 GIC。模拟结果验证了所提出的方法可以在各种电网运行条件下有希望地估计电力系统中的 GIC。