Consumer phase identification in distribution grids using Graph Neural Networks based on synthetic and measured power profiles

Energy and AI, vol. 22, pp. 100607

Abstract

Most distribution system operators may not accurately record or completely maintain the phase connections for the numerous LV customers. Different consumer phase identification (CPI) approaches based on voltages, powers or other measurements are proposed in the literature. Due to the technical challenges in collecting voltage measurements, power measurement based approaches are preferable. Hence, this paper proposes a novel power based CPI methodology applying Graph Neural Networks (GNNs). The CPI methodology generates synthetic transformer power profiles assuming random combinations of phases for the measured load profiles, which are used altogether to train the GNN model. The GNN model is then tested using measured transformer and load power profiles. The performance of the methodology is evaluated in a test low voltage grid of 55 loads under various conditions of Photovoltaic penetration, Photovoltaics under maintenance, measurement errors, unmetered consumption, uncertain grid asset parameters and inaccurate phase connections. Further tests on a real low voltage grid with 111 loads prove the scalability of the methodology. The attained results show that the GNN model can achieve accuracy above 90% in most cases, outperforming various state-of-the-art methods.

Authors 4

1. Chandra Sekhar Charan Dande corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH – Institute for Automation of Complex Power Systems, 52074 Aachen, Germany

2. Nikolaos A. Efkarpidis
   Affiliation as printed

   Secure Switzerland AG, 8048 Zurich, Switzerland

3. Mirko Ginocchi Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH – Institute for Automation of Complex Power Systems, 52074 Aachen, Germany

4. Antonello Monti Aachen

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Fraunhofer FIT, Center for Digital Energy, 52068 Aachen, Germany

   RWTH – Institute for Automation of Complex Power Systems, 52074 Aachen, Germany