2018/09/18 by Sergey A. Iakovlev, Robin J. Evans, Iakovlev, Sergey +3
Engineering · #Computational Engineering #Electricity Theft Detection Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Finance #Optimal Power Flow Distribution #Optimization and Control (math.OC) #Power Quality and Harmonics #Power System Optimization and Stability #Smart Grid Energy Management #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1809.06657
openalex publication_date 2018/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Under conditions of high penetration of renewables, the low-voltage (LV) distribution network needs to be carefully managed. In such a scenario, an accurate real-time low-voltage power network model is an important prerequisite, which opens up the possibility for application of many advanced network control and optimisation methods thus providing improved power flow balancing, reduced maintenance costs, and enhanced reliability and security of a grid. Smart meters serve as a source of information in LV networks and allow for accurate measurements at almost every node, which makes it advantageous to use data driven methods. In this paper, we formulate a non-linear and non-convex problem, solve it efficiently, and propose a number of fully smart meter data driven methods for line parameters estimation. Our algorithms are fast, recursive in data, scale linearly with the number of nodes, and can be executed in a decentralised manner by running small algorithms inside each smart meter. The performance of these algorithms is demonstrated for different measurement accuracy scenarios through simulations.