Identification of AC Networks via Online Learning

With the advent of renewable energy resources, generation in power networks is drifting from the classical centralized paradigm to an increasingly distributed scenario. While offering many advantages, renewable-based generation can compromise grid reliability, due to its intermittent nature and creation of reverse power flows. In order to guarantee the safe operation of power systems and avoid dangerous phenomena like blackouts, innovative and efficient control algorithms are necessary. Nevertheless, advanced algorithms necessitate grid identification, that is, the knowledge of grid topology and line parameters. Most works on the identification of electric networks focus on topology verification, assuming a known initial topology and aiming at detecting sparse changes, such as line trips or switch activations [1, 2]. More recently, attention has shifted to the estimation of network topology and line parameters without any apriori information. Two main branches of research have appeared. On the one hand, works like [3, 4] propose learning algorithms that exploit the statistical properties of nodal measurements to determine the operational structure and the line impedances. These approaches have the major advantage of accounting for buses with no available measurements (hidden nodes) [4], although restrictive assumptions are required, e.g.