Distributed energy resources challenge the situational awareness of power flows. Many distribution grid (DG) operators have not yet implemented state estimation (SE) due to the expense or privacy constraints of measurements that lead to an unobservable system, as well as inaccurate grid parameters. A key concern with the latter is the presence of medium- and low-voltage transformers with off-load tap changers, whose tap positions critically influence voltage levels across the network. Identifying transformers where the registered position is likely incorrect — and, when on-site verification is impractical, estimating a plausible tap setting — constitutes a valuable contribution to improving network observability and operational accuracy. Although operators could manually inspect each transformer, this is impractical — there are, for instance, up to 20,000 transformers in the Southern Netherlands.

To address these challenges, this thesis proposes a novel topology-aware framework for estimating state and transformer tap positions in unobservable DGs. The framework comprises two key components. First, a generative adversarial network (GAN) is used to train a generative model conditioned on the network topology and synthetic power flow data, generating realistic measurements. Second, an integrated model, referred to as the TapSEGNN model, is proposed for estimating state and transformer tap positions.

Both of these components employ a core model architecture which combines graph and simplicial complex neural networks to capture spatial dependencies between nodes, edges, and higher-order structures. To train these components, an industrial-grade data-processing pipeline was developed using a real DG topology and simulating the exact available measurement locations. The results show that balanced adversarial training of GAN accurately imputes the missing active power injection measurements, but produces high variance in imputations for voltage magnitude and active power flow measurements. The performance of the TapSEGNN model demonstrates at least tenfold higher accuracy for SE compared to conventional methods, and it predicts transformer tap positions with 100% accuracy in a computationally efficient manner. TapSEGNN also shows promising scalability for larger networks; however, it struggles with generalisability across similar real networks. Finally, the suboptimal performance of both components in certain aspects warrants further investigation, which is recommended as future work.

This thesis explores how accurate state estimation can support Distribution System Operators (DSO) in managing MV grids. As the energy transition drives increasing complexity in electricity networks, more precise and reliable data is necessary for planning and operational decisions. Although state estimation offers a way to enhance data, DSOs struggle to implement it. The main objective of this thesis is to develop a general strategy for placing measurements to achieve accurate state estimation using as few measurements as possible.

Multiple cases were designed to assess the impact of specific measurement placement. A reference ”perfect case” network load scenario was created, and the values were corrupted using normally distributed noise, with standard deviations reflecting the expected accuracy of each measurement type. These noisy measurements were fed into the PandaPower Weighted Least Squares (WLS) state estimation algorithm, implemented in Python. The resulting state estimates were compared to the perfect case values to evaluate the state estimator’s accuracy and the placement strategy’s effect.

The results show that Power Injection Measurements (PIMs) primarily improve accuracy at the node where they are placed. In contrast, Medium Voltage Measurement Units (MVMUs) offer broader improvements across the entire feeder where they are installed. One strategic measurement location was identified based on financial grounds. Results also indicate that improving the accuracy of an inaccurate node is possible without improving its measurement, but requires widespread deployment elsewhere, which is rarely justifiable economically. Retrofitting stations solely for measurement purposes is generally not considered worthwhile.

The main focus should be on identifying the substations with the poorest measurement accuracy, typically pseudo-measurements. As a consequence, overall pseudo-measurement accuracy will also improve. This makes it more likely that the state estimates will fall within predetermined limits. The number of measurements that need to be placed depends on this predetermined limit.