Forecasting of GICs in Local Power Grid by Comprehensive Analysis Based on Hilbert-Huang Transform and Deep Learning Methods

The Geomagnetic Induced Currents (GICs) generated in power grid by large-scale solar events may lead to electrical equipment damage and widespread power outages. Consequently, it is significant to achieve effective GIC prediction and risk assessment for local grids. The Hilbert-Huang Transform (HHT) is used to analyze geomagnetic components and GICs monitoring data. By extracting the responses to storms, the instantaneous amplitudes are synthesized as equivalent GIC disturbance indices. A combined prediction model based on deep learning methods is proposed. Concretely, the observation data of geomagnetic components and GIC are decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the Intrinsic Mode Functions (IMFs) are predicted by a combination model constructed by Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) with the attention mechanism. Then, the predicted sequences of IMFs are accumulated to obtain the final forecasting. In addition, the GIC potential risk of local power grid is evaluated comprehensively by considering the solar wind energy injection, geomagnetic disturbances, equivalent GIC indices and the forecasting based on observations.