期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 13, 期 8, 页码 1277-1286出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2018.5139
关键词
observers; Kalman filters; smart power grids; power system protection; smart grids; FDIA; cyber-physical attack detection; system integrity protection schemes; SIPS; false data injection attack isolation algorithm; false data injection attack detection performance; Kalman filter-based $\chi <^>2$2-detector detection techniques; unknown input observer-based detection method; unknown input observer-based isolation method; UIO-based isolation method; UIO-based detection method; stealthy characteristics; linearised error model
资金
- National Nature Science Foundation of China [61873228]
- Nature Science Foundation of Hebei Province [2016203311]
The emergence of cyber-physical attacks brings a key challenge to the existing system integrity protection schemes (SIPSs) of smart grids. As one of typical cyber-physical attacks, false data injection attacks (FDIAs) can bypass the existing Kalman filter-based $\chi <^>2$chi 2-detector detection techniques in SIPSs. To improve the detection performance against the FDIAs in SIPSs, this study proposes an unknown input observer (UIO)-based detection and isolation method. Taking the stealthy characteristics of FDIAs into account, this study presents a set of UIOs to detect the FDIA based on the internally physical dynamics. Furthermore, a UIO-based detection and isolation algorithm against the FDIAs is proposed based on the feature of residuals generated by UIOs. To detect the cyber attacks quickly and avoid missing detection, an adaptive threshold is designed to replace the precomputed threshold by taking the model linearised error and disturbance into account. Finally, comprehensive simulation results on the proposed algorithm are carried out, and the effectiveness of improving the detection performance in SIPSs is verified.
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