  The increasing number of distributed generators connected to distribution
grids requires a reliable monitoring of such grids. Economic considerations
prevent a full observation of distribution grids with direct measurements.
First approaches using a limited number of measurements to monitor such grids
exist, some of which use artificial neural networks (ANN). The current
ANN-based approaches, however, are limited to static topologies, only estimate
voltage magnitudes, do not work properly when confronted with a high amount of
distributed generation and often yield inaccurate results. These strong
limitations have prevented a true applicability of ANN for distribution grid
monitoring. The objective of this paper is to overcome the limitations of
existing approaches. We do that by presenting an ANN-based scheme, which
advances the state-of-the-art in several ways: Our scheme can cope with a very
low number of measurements, far less than is traditionally required by the
state-of-the-art weighted least squares state estimation (WLS SE). It can
estimate both voltage magnitudes and line loadings with high precision and
includes different switching states as inputs. Our contribution consists of a
method to generate useful training data by using a scenario generator and a
number of hyperparameters that define the ANN architecture. Both can be used
for different grids even with a high amount of distributed generation.
Simulations are performed with an elaborate evaluation approach on a real
distribution grid and a CIGRE benchmark grid both with a high amount of
distributed generation from photovoltaics and wind energy converters. They
demonstrate that the proposed ANN scheme clearly outperforms state-of-the-art
ANN schemes and WLS SE under normal operating conditions and different
situations such as gross measurement errors when comparing voltage magnitude
and line magnitude estimation errors.
