Data-guided learning in power systems: analysis and design

Power grids have been growing in complexity, making it increasingly difficult to derive mathematical models that can explain the physics of the system in detail.
This is an area where data-driven models, data-driven control, and approaches based on machine learning can play a significant role. Methods that have been pioneered by the SMS group will be further expanded to synthesize control policies for the power network.