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The increasing number of distributed generators connected to distributiongrids requires a reliable monitoring of such grids. Economic considerationsprevent a full observation of distribution grids with direct measurements.First approaches using a limited number of measurements to monitor such gridsexist, some of which use artificial neural networks (ANN). The currentANN-based approaches, however, are limited to static topologies, only estimatevoltage magnitudes, do not work properly when confronted with a high amount ofdistributed generation and often yield inaccurate results. These stronglimitations have prevented a true applicability of ANN for distribution gridmonitoring. The objective of this paper is to overcome the limitations ofexisting approaches. We do that by presenting an ANN-based scheme, whichadvances the state-of-the-art in several ways: Our scheme can cope with a verylow number of measurements, far less than is traditionally required by thestate-of-the-art weighted least squares state estimation (WLS SE). It canestimate both voltage magnitudes and line loadings with high precision andincludes different switching states as inputs. Our contribution consists of amethod to generate useful training data by using a scenario generator and anumber of hyperparameters that define the ANN architecture. Both can be usedfor different grids even with a high amount of distributed generation.Simulations are performed with an elaborate evaluation approach on a realdistribution grid and a CIGRE benchmark grid both with a high amount ofdistributed generation from photovoltaics and wind energy converters. Theydemonstrate that the proposed ANN scheme clearly outperforms state-of-the-artANN schemes and WLS SE under normal operating conditions and differentsituations such as gross measurement errors when comparing voltage magnitudeand line magnitude estimation errors.

Distribution System Monitoring for Smart Power Grids with Distributed Generation Using Artificial Neural Networks