Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

False data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and stable operation of power systems. To mitigate this issue, a rapid monitoring and defense approach is proposed to defend against cyber attacks. First, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. In TFCN, the spectrum layer extracts the frequency information of the input data, and then the time-frequency block can be formed. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57 % compared with traditional neural networks. The defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which shows better performance in faster stability restoration.