Abstract
Modern intelligent energy grids enable energy supply and consumption to be efficiently managed while simultaneously avoiding a variety of security risks. System disturbances can be caused by both naturally occurring and human-made events. Operators should be aware of the different kinds and causes of disturbances in the energy systems to make informed decisions and respond accordingly. This study addresses this problem by proposing an attack detection model on the basis of deep learning for energy systems, which could be trained utilizing data and logs gathered through phasor measurement units (PMUs). Property or specification making is used to create features, and data are sent to various machine learning methods, of which random forest has been selected as the basic classifier of AdaBoost. Open-source simulated energy system data are used to test the model containing 37 energy system event case studies. In the end, the suggested model has been compared with other layouts according to various assessment metrics. The simulation outcomes showed that this model achieves a detection rate of 93.6% and an accuracy rate of 93.91%, which is greater compared to the existing methods.