Machine-learning-based Bayesian state estimation in electrical energy systems

In many algorithmic applications in electrical power grids, state estimation (SE) represents the first step of a
process chain. In SE, sensor measurements are processed to infer the most probable grid state. Classical methods
such as weighted least squares (WLSs) based approaches use statistical methods that can be based on sensor noise
and erroneous measurements. With these methods, only point estimates are made, which results in a lack of

knowledge about prediction uncertainties. In this study, machine-learning-based methods for determining the actual
state of the grid are proposed. Bayesian optimisation is applied to find the optimal hyperparameter configurations for

neural networks (NNs) for SE tasks. The application of Bayesian inference using Bayesian NNs is proposed, which
allows the prediction of point estimates as well as uncertainty intervals for the system states. The advantages of using
Bayesian approaches in comparison to classical SE methods like WLS are shown.