A Systematic Literature Review of Machine Learning Approaches for Detecting Events and Disturbances in Smart Grid Systems

This study systematically reviews international peer-reviewed literature to show existing scientific approaches on how machine learning and deep learning methods can improve the detection of events and disturbances in smart grid systems. Smart grids can adapt and react flexibly to different situations. Different approaches can be exploited to protect the whole system more efficiently. Using an extended smart control center framework, we systematically structure our literature analysis, allowing us to identify unaddressed research gaps, guiding future research on contributing to ensuring and improving security in smart grids.