The increasing integration of intermittent renewable generation in power networks calls for novel planning and control methodologies, which hinge on detailed knowledge of the grid.

However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying AC networks. The talk proposes an online learning procedure to estimate the admittance matrix of an AC network capturing topological information and line parameters.

We start off by providing a recursive identification algorithm that exploits phasor measurements of voltages and currents. With the goal of accelerating convergence, we subsequently complement our base algorithm with a design-of-experiment procedure, which maximizes the information content of data at each step by computing optimal voltage excitations.

Our approach improves on existing techniques and its effectiveness is substantiated by numerical studies.

First delivered at End of Semester Talks, 2020, Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland