WAMS Operations in modern power systems: A median expectation-based state estimation approach for grid resilience towards cyber attacks
Year: 2025
Type: article
Abstract: Modern power grids require phasor measurement unit (PMU)-based infrastructure for real-time monitoring. This is due to the integration with renewable energy (RE) systems, which play a vital role in the distribution network through voltage and reactive power support. However, the increasing digital communication traffic makes these modern grids vulnerable to data injection attacks, commonly called false-data injection (FDI) attacks, which could potentially compromise power transmission and distribution network reliability. This work proposes a median-based state prediction (MSP) approach that provides immunity against cyber-attacks through time-delay-based state representation, which is crucial for services like 1) frequency regulation and 2) stability control. The proposed method employs a two-step approach: 1) initial state initialization to assess latency during attacks and 2) median-based state prediction and estimation for reconstructing compromised states. The proposed scheme was further validated on IEEE 13 and 37 Bus-test feeder systems under various disturbances and severe injection attacks. The proposed scheme demonstrates 1) accurate state estimation capabilities with 2) faster computation times while successfully 3) identifying attack corruption and 4) generating residual error measurements.
Institutions: DHA Suffa University, National University of Sciences and Technology, University of Dubai, University of Johannesburg, Rochester Institute of Technology - Dubai, Rochester Institute of Technology, Lyon 1 Université, École Centrale de Lyon, Centre National de la Recherche Scientifique, Laboratoire Ampère, Institut National des Sciences Appliquées de Lyon, Higher Colleges of Technology, United Arab Emirates University, University of Liverpool
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
OpenAlex ID: W4412544636
