TY  - JOUR
AU  - Jin, Shiyu
AU  - Yang, Wen
AU  - Yuan, Hongbo
AU  - Ding, Wenjie
AU  - Wu, Han
AU  - Wang, Jie
PY  - 2024
DA  - 2024/12/30
TI  - Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks
JO  - Chinese Journal of Information Fusion
T2  - Chinese Journal of Information Fusion
JF  - Chinese Journal of Information Fusion
VL  - 1
IS  - 3
SP  - 212
EP  - 225
DO  - 10.62762/CJIF.2024.740709
UR  - https://www.icck.org/article/abs/CJIF.2024.740709
KW  - state estimation
KW  - smart grid
KW  - data fusion
KW  - Kullback-Leibler divergence
KW  - cyber attack
AB  - Cyber security in power systems has become increasingly critical with the rise of network attacks such as Denial-of-Service (DoS) attacks and False Data Injection (FDI) attacks. These threats can severely compromise the integrity and reliability of state estimation, which are fundamental to the operation and control of power systems. In this manuscript, an estimation algorithm based on the fusion of information from multiple estimators is proposed to ensure that state estimation at critical buses can function properly in case of attacks. Our approach leverages a network of estimators that can dynamically adjust to maintain system stability and accuracy. Furthermore, a new detector is adopted based on Kullback-Leibler divergence to detect linear FDI attacks. To address stealthy attacks that may evade detection, we propose a novel weighting scheme that reduces the impact of attacks on estimation results. Numerical experiments demonstrate the effectiveness and accuracy of our proposed estimation algorithm under cyber attacks.
SN  - 2998-3371
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Jin2024Robust,
  author = {Shiyu Jin and Wen Yang and Hongbo Yuan and Wenjie Ding and Han Wu and Jie Wang},
  title = {Robust Distributed State Estimation in Power Systems: A Multi-Estimator Data Fusion Approach to Counteract Cyber-Attacks},
  journal = {Chinese Journal of Information Fusion},
  year = {2024},
  volume = {1},
  number = {3},
  pages = {212-225},
  doi = {10.62762/CJIF.2024.740709},
  url = {https://www.icck.org/article/abs/CJIF.2024.740709},
  abstract = {Cyber security in power systems has become increasingly critical with the rise of network attacks such as Denial-of-Service (DoS) attacks and False Data Injection (FDI) attacks. These threats can severely compromise the integrity and reliability of state estimation, which are fundamental to the operation and control of power systems. In this manuscript, an estimation algorithm based on the fusion of information from multiple estimators is proposed to ensure that state estimation at critical buses can function properly in case of attacks. Our approach leverages a network of estimators that can dynamically adjust to maintain system stability and accuracy. Furthermore, a new detector is adopted based on Kullback-Leibler divergence to detect linear FDI attacks. To address stealthy attacks that may evade detection, we propose a novel weighting scheme that reduces the impact of attacks on estimation results. Numerical experiments demonstrate the effectiveness and accuracy of our proposed estimation algorithm under cyber attacks.},
  keywords = {state estimation, smart grid, data fusion, Kullback-Leibler divergence, cyber attack},
  issn = {2998-3371},
  publisher = {Institute of Central Computation and Knowledge}
}