Open AccessRelaxation‐based anomaly detection in cyber‐physical systems using ensemble kalman filter
Author(s) -
Karimipour Hadis,
Leung Henry
Publication year - 2020
Publication title -
iet cyber‐physical systems: theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 7
ISSN - 2398-3396
DOI - 10.1049/iet-cps.2019.0031
Subject(s) - kalman filter , anomaly detection , residual , computer science , ensemble kalman filter , relaxation (psychology) , anomaly (physics) , cyber physical system , detector , grid , smart grid , data mining , electric power system , power (physics) , artificial intelligence , extended kalman filter , algorithm , engineering , mathematics , physics , telecommunications , psychology , social psychology , operating system , condensed matter physics , geometry , quantum mechanics , electrical engineering
As power systems mature into smart grid entities, they face new challenges toward online monitoring and control of the system's behaviour. Burgeoning classes of cyber‐attacks are observed which may cause instability of the power grid and system blackouts if not identified. In this study, the authors propose an ensemble Kalman filter based anomaly detector using a relaxation‐based solution. Performance of the proposed method is tested with Chi‐Square detector and Largest Normalised Residual test. Results of simulations based on real‐world data, up to 5000 bus system, demonstrate the effectiveness of the proposed framework over traditional bad data detection in presence of false data injection attack.