End-users are transiting towards more active, integrating new low-carbon (LC) technologies and bringing unpredictability to low-voltage (LV) distribution networks. Although smart meters have a great potential in increasing the observability, they are mostly being employed only for billing purposes, leaving many other possibilities unexploited, further complicating the many analyses required for effective operational planning and real-time (RT) operation. Detection of phase consumption of end-users is significantly difficult, due to the nonlinear relationships between obtained phase voltage measurements and aggregated end-user consumption. Machine learning (ML) is increasingly used for these and similar problems, and therefore, in this paper, a neural network (NN) – based model is developed to detect end-user consumption in an LV distribution network from available voltage measurements and aggregated end-user consumption. Furthermore, the influence of topology on the output values of the model is investigated and a graph neural network (GNN) – based model is created that considers both the structure and data of the distribution network elements. Both models are tested on the real-world LV distribution network with more than 150 end-users. The results showed the effectiveness of both models in determining the distribution of end-user consumption, with the GNN-based model showing significantly better results. Such a model can help the energy utilities to overcome this time-consuming problem and lay a good foundation for further analyzes required to enable operation and planning of distribution networks.
Keywords smart meters, distribution network, phase consumption, machine learning, graph neural network