Iven Mareels

Sergey Iakovlev, Robin J. Evans, Iven Mareels

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.

Zhanghan Tang, Margreta Kuijper, Michelle Chong et al.

We address the problem of attack detection and attack correction for multi-output discrete-time linear time-invariant systems under sensor attack. More specifically, we focus on the situation where adversarial attack signals are added to some of the system's output signals. A 'security index' is defined to characterize the vulnerability of a system against such sensor attacks. Methods to compute the security index are presented as are algorithms to detect and correct for sensor attacks. The results are illustrated by examples involving multiple sensors.