Multiscale GIC Prediction Based on Improved CNN-BiLSTM Model and Geomagnetic Monitoring Data

The GIC generated by solar storms driving in power system networks can affect the safe operation of power equipment and systems, and even lead to major power outages. Predicting the level of GIC in power grids can provide an important reference for power system protection measures, but research in this area continues to be insufficient. In order to solve this problem, a multi-scale GIC prediction method for large-scale power grids is proposed by combining Convolutional Neural Networks (CNN), Bidirectional Long and Short Term Memory (BiLSTM), and attention mechanisms, using relevant monitoring information of spatial weather. Firstly, based on the analysis of the mechanism of GIC generated by solar storms, a GIC prediction model is constructed; Secondly, a dual-channel GIC prediction architecture based on CNN-BiLSTM is proposed: first, local geomagnetic disturbance information is captured using CNN, then the global characteristics of geomagnetic storm disturbance information are synthesized using BiLSTM, and finally, the geomagnetic information fragments that play a key role in GIC are comprehensively evaluated using the multi-head attention mechanism, achieving the prediction of the power grid GIC. Using monitoring data of the DED geomagnetic station and the QGZH geomagnetic station during the giant magnetic storm from 00:00 LT－20:00 LT on 8 November 2004, the proposed method was applied to regression prediction of the GIC of the 500 kV Ling’Ao substation. After 220 rounds of training, the relative error of GIC prediction is within 12%, the accuracy is higher than the prediction results of other models.