Distributed state estimation: a learning-based framework

The present-day electricity grid is rapidly evolving towards a complex interconnection of distributed modules equipped with a broad range of heterogenous sensing and decision-making functionalities. The proliferation of highly intermittent small energy resources and changing customer needs highlight more adaptive and responsive grid operability in terms of decision making and control. Conventional grid-controlling techniques are unable to cope with such dynamics, as manifested by the increasing reliance on faster time-scale control (such as FACTs devices which enable power electronics-based switching [1]) and system sampling techniques (such as PMUs [2]) to mitigate rapid system fluctutions. It is hard to overemphasize the role of reliable system state estimation on the efficient operability of current and future grid-control techniques.