  One of the significant challenges linked with the massive integration of
distributed energy resources (DER) in the active distribution grids is the
uncertainty it brings along. The grid operation becomes more arduous to avoid
voltage or thermal violations. While the Optimal Power Flow (OPF) algorithm is
vastly discussed in the literature, little attention has been given to the
robustness of such centralised implementation, such as the provision of
redundant control solutions during a communication failure. This paper aims to
implement a machine learning-based algorithm at each Intelligent Electronic
Device (IED) that mimics the centralised OPF used during communication failures
using IEC 61850 data models. Under normal circumstances, the IEDs communicate
for centralised OPF. In addition, the system is trained offline for all
operational conditions and the individual look-up tables linking the actual
voltages to the DER setpoints are sent to the respective controllers. The
regression models allow for the local reconstruction of the DER setpoints,
emulating the overall OPF, in case of a communication failure. In addition to
the regression control, the paper also explains an offline learning approach
for periodic re-training of the regression models. The implementation is
experimentally verified using a Hardware-in-the-loop test setup. The tests
showed promising results compared to conventional control strategies during
communication failures. When properly trained and coordinated, such an
intuitive local control approach for each DER could be very beneficial for the
bulk power system. This machine learning-based approach could also replace the
existing Q(V) control strategies, to better support the bulk power system.
