Improved DC Microgrid Cybersecurity via Distributed State Estimation Control: A Comparative Analysis
Year: 2026
Type: conference-paper
Abstract: In this paper, we compare centralized and distributed state estimation and control strategies for improving cybersecurity in direct current microgrid systems (DCMS). DCMS stability and control can be compromised by false data injection (FDI) attacks on sensors. We examine the effectiveness of two Kalman filtering schemes-centralized Kalman filter (CKF) and distributed Kalman filter (DKF) via consensus-as cybersecurity techniques against identical FDI attack scenarios. The resilience-accuracy-complexity (RAC) Cube framework analyzes and evaluates estimation accuracy, attack robustness, and implementation complexity trade-offs. Simulation results show that under a single sensor FDI attack, the CKF suffers significant losses in global estimation accuracy, with the root mean square error (RMSE) increasing from 0.5% to over 2.5%. By limiting errors locally via the consensus algorithm, the DKF framework keeps global RMSE below 1.0% under the same attack. Despite its superior resilience and estimation accuracy, DKF requires high implementation complexity due to its communication network and consensus algorithm. The work provides empirical support for the RAC Cube framework and practical guidance for developing robust state estimation strategies to improve DCMS cybersecurity.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204752228