2020/03/13 by Emanuele Fabbiani, Fabbiani, Emanuele, Pulkit Nahata +6 · 1 citation
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Microgrid Control and Optimization #Optimal Power Flow Distribution #Power System Optimization and Stability #Systems and Control (eess.SY) #cs.LG #cs.SY #eess.SY #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2003.06210
openalex publication_date 2020/03/13 · arxiv created 2021/09/19 · arxiv updated 2021/09/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid. However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying electric distribution networks. This paper proposes an online learning procedure to estimate the network admittance matrix capturing topological information and line parameters. We start off by providing a recursive identification algorithm exploiting phasor measurements of voltages and currents. With the goal of accelerating convergence, we subsequently complement our base algorithm with a design-of-experiment procedure which maximizes the information content of data at each step by computing optimal voltage excitations. Our approach improves on existing techniques, and its effectiveness is substantiated by numerical studies on realistic testbeds.