Power Grid transient stability prediction method based on improved CNN under big data background

Abstract

In order to cure the crux of low accuracy of traditional PG transient stability prediction (PGTSP) methods, a PGTSP method based on improved Convolutional Neural Network(CNN) is proposed under big data. First, the theoretical framework of state assessment of Power Grid (PG) is constructed according to analysis of historical big data in PG. Based on data source, big data classification, big data cleaning and processing, PG status assessment is realized. Then, by selecting a variety of PG traits as the multi-input trait space of CNN model. By using multi-channel idea to independently analyze and fuse various features, a multi-channel multi-trait fusion CNN(MC-MF-FCNN) model is constructed and the accurate prediction of grid transient stability is achieved. Finally, the root mean square error(RMSE), false alarm rate, false alarm rate and accuracy rate of the proposed algorithm and the other two algorithms under the same conditions are compared and analyzed through simulation experiments. The results show that RMSE, false alarm rate and miss alarm rate of the proposed algorithm are the smallest and the accuracy is the highest. The highest accuracy rate within 9 cycles after the fault is 96.83 %, and the minimum RMSE, missed alarm rate and false alarm rate are 0.196, 2.15% and 1.32%, respectively. The performance is better than the other two comparison algorithms.