  Increasing adoption of smart meters and phasor measurement units (PMUs) in
power distribution networks are enabling the adoption of data-driven/model-less
control schemes to mitigate grid issues such as over/under voltages and
power-flow congestions. However, such a scheme can lead to
infeasible/inaccurate control decisions due to measurement inaccuracies. In
this context, the authors' previous work proposed a robust measurement-based
control scheme accounting for the uncertainties of the estimated models. In
this scheme, a recursive least squares (RLS)-based method estimates the grid
model (in the form of voltage magnitude sensitivity coefficients). Then, a
robust control problem optimizes power set-points of distributed energy
resources (DERs) such that the nodal voltage limits are satisfied. The
estimated voltage sensitivity coefficients are used to model the nodal
voltages, and the control robustness is achieved by accounting for their
uncertainties. This work presents the first experimental validation of such a
robust model-less control scheme on a real power distribution grid. The scheme
is applied for voltage control by regulating two photovoltaic (PV) inverters
connected in a real microgrid which is a replica of the CIGRE benchmark
microgrid network at the EPFL Distributed Electrical Systems Laboratory.
