Online Transient Frequency Safety Prediction Machine of Power System Based on Time-Feature Attention Module

Facing the frequency safety problem of renewable energy power systems, this paper proposes a Frequency Safety Prediction Machine (FSPM) based on a one-dimensional convolutional neural network (1D-CNN) and Time-Feature Attention Module (TFAM). To ensure the rapidity of online frequency safety prediction (FSP), the one-dimensional time series data (1D-TSD) at multiple moments are standardized and divided into multiple time channels and transmitted to FSPM at the same time, which speeds up the speed of data flow. The TFAM in FSPM realizes the function of focusing on key moments and key features. Make FSPM pay more attention to the system information at certain critical moments and certain key feature information that can fully represent the fault, thereby improving the prediction accuracy. In addition, FSPM achieves dual prediction of Frequency Danger Level (FDL) and Time Safety Margin (TSM) at the same time. Finally, the test results on the IEEE 39 bus system and the Illinois power grid both containing renewable energy show that the FSPM not only has good performance in both FDL and TSM prediction but also has low model complexity, good noise resistance, and robustness.