PhML-DyR: A Physics-Informed ML framework for Dynamic Reconfiguration in Power Systems. (arXiv:2206.06789v1 [eess.SY])

A transformation of the US electricity sector is underway with aggressive
targets to achieve 100% carbon pollution-free electricity by 2035. To achieve
this objective while maintaining a safe and reliable power grid, new operating
paradigms are needed, of computationally fast and accurate decision making in a
dynamic and uncertain environment. We propose a novel physics-informed machine
learning framework for the decision of dynamic grid reconfiguration (PhML-DyR),
a key task in power systems. Dynamic reconfiguration (DyR) is a process by
which …

arxiv framework ml ml framework physics power systems