Operational anomaly detection for DC mini-grids through dynamic admittance estimation
File(s)
Accepted version
Author(s)
Saxena, Vardan
Junyent-Ferre, Adria
Barria, Javier
Type
Conference Paper
Abstract
This work combines model based and data driven approaches to detect and localize sensor anomalies in grid-side measurement units of an interconnected DC mini-grid system. It focuses on utilizing the inherent system admittance relation (Y matrix) from the measured voltages and currents using a pseudoinverse based least squares technique. This method was developed based on a
systematic pattern recognition in the relationship between various anomalies and their influence on the Y matrix variables. The deviations between the estimated and nominal values over time are used to detect their occurrence and determine the location of the affected sensor. The results are implemented through MATLAB SIMULINK under multiple classes of sensor offset anomalies which demonstrate accurate anomaly detection under well-informed conditions regardless of the network topology
systematic pattern recognition in the relationship between various anomalies and their influence on the Y matrix variables. The deviations between the estimated and nominal values over time are used to detect their occurrence and determine the location of the affected sensor. The results are implemented through MATLAB SIMULINK under multiple classes of sensor offset anomalies which demonstrate accurate anomaly detection under well-informed conditions regardless of the network topology
Date Acceptance
2026-01-19
Publisher
IET
Copyright Statement
Sbject to copyright. This paper is embargoed until publication. Once published the author’s accepted manuscript will be made available under a CC-BY License in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy).
License URL
Source
IET ACDC Europe 2026
Publication Status
Accepted
Start Date
2026-04-28
Finish Date
2026-04-29
Coverage Spatial
Berlin, Germany