Fast Graphical Learning Method for Parameter Estimation in Large-Scale Distribution Networks

Abstract

In distribution systems with growing distributed energy resources, accurate estimation of network parameters is crucial to feeder modeling, monitoring and management. Al-though existing state-of-the-art parameter estimation algorithms such as physics-informed graphical learning (GL) have accurate estimation, they can potentially suffer from scalability issues due to slow training in larger networks. In this paper, we propose an upgraded graphical learning method called fast graphical learning (FGL) to improve the computational efficiency and scalability while preserving the merits of GL. In FGL, we develop faster alternative algorithms to replace the fixed-point-iteration-based FORWARD and BACKWARD algorithms in GL. These alternative algorithms are based on fast power flow calculation of the current injection method and more efficient state initialization by the linearized power flow model. A comprehensive numerical study on IEEE test feeders and large-scale real-world distribution feeders shows that FGL improves the computational efficiency by as much as 60 times in larger distribution networks while attaining the accuracy of the state-of-art algorithms.