空间科学学报2024，Vol.44Issue(3)：488-499,12.DOI:10.11728/cjss2024.03.2023-0084
基于改进CNN-BiLSTM模型和地磁监测数据的多时间长度GIC预测
Multiscale GIC Prediction Based on Improved CNN-BiLSTM Model and Geomagnetic Monitoring Data
摘要
Abstract
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 re-search in this area continues to be insufficient.In order to solve this problem,a multi-scale GIC predic-tion 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 rele-vant 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 predic-tion 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 geomag-netic station during the giant magnetic storm from 00:00 LT-20:00 LT on 8 November 2004,the pro-posed 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.关键词
太阳风暴/GIC预测/卷积神经网络/地磁数据Key words
Solar storm/GIC prediction/Convolutional Neural Networks(CNN)/Geomagnetic data分类
天文与地球科学引用本文复制引用
蓝东亮,陈延云,吴影,赵淼,王亮,吴伟丽,黄冲..基于改进CNN-BiLSTM模型和地磁监测数据的多时间长度GIC预测[J].空间科学学报,2024,44(3):488-499,12.基金项目
国家电网科技项目(SGXJCJ00KJJS2100582)和合肥市关键共性技术研发项目(2021GJ039)共同资助 （SGXJCJ00KJJS2100582）
