Detection of power grid disturbances and cyber-attacks based on machine learning

Abstract

Modern intelligent power grid provides an efficient way of managing energy supply and consumption while facing numerous security threats at the same time. Both natural and man-made events can cause power system disturbance. Therefore, it is important for operators to identify the specific causes and types of disturbance in the power system to make decisions and respond appropriately. In order to address this problem, this paper proposes an attack detection model for power system based on machine learning that can be trained by using information and logs collected by phasor measurement units (PMUs). We carry out feature construction engineering, and then send the data to different machine learning models, in which random forest is chosen as the basic classifier of AdaBoost. The model is evaluated using open-source simulated power system data, which consists of 37 power system event scenarios. Finally, we compare the proposed model with other models by using different evaluation metrics. As the experimental results demonstrate that this model can achieve accuracy rate of 93.91% and detection rate of 93.6%, higher than eight recently developed techniques.