- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
False data injection (FDI) attacks are crucial security threats to smart grid cyber-physical system (CPS), and could result in cataclysmic consequences to the entire power system. However, due to the high dependence on open information networking, countering FDI attacks is challenging in smart grid CPS. Most existing solutions are based on state estimation (SE) at the highly centralized control center; thus, computationally expensive. In addition, these solutions generally do not provide a high level of security assurance, as evidenced by recent work that smart FDI attackers with knowledge of system configurations can easily circumvent conventional SE-based false data detection mechanisms. In this paper, in order to address these challenges, a novel distributed host-based collaborative detection method is proposed. Specifically, in our approach, we use a conjunctive rule based majority voting algorithm to collaboratively detect false measurement data inserted by compromised phasor measurement units (PMUs). In addition, an innovative reputation system with an adaptive reputation updating algorithm is also designed to evaluate the overall running status of PMUs, by which FDI attacks can be distinctly observed. Extensive simulation experiments are conducted with real-time measurement data obtained from the PowerWorld simulator, and the numerical results fully demonstrate the effectiveness of our proposal.
Smart grid cyber-physical system (CPS) is designed to facilitate highly efficient, accurate, and reliable power delivery as well as sustainable energy integration and utilization [22,40]. Despite the potential benefits of a smart grid CPS, there are underlying threats that could jeopardize the security of the system and consequently, have a cascading effect on the stability of the society [22,9,17,21] (see Fig. 1 the system view of a smart grid CPS).
In this paper, we proposed a novel DHCD method to identify and mitigate FDI attacks in smart grid CPS. Specifically, a rule specification based real-time collaborative detection system was designed to identify the anomalies of measurement data. In addition, a new reputation system with an ARU algorithm was presented to evaluate the overall running status of the PMUs, which can be used to identify compromised PMUs. We then demonstrated the utility of the proposed approach using simulations of the IEEE 39-bus power system.
As previously discussed, our method is designed to detect the malicious activities resulting in the anomaly of measurement data. Future work would include extending the proposed approach to capture power system faults (e.g., voltage disturbance, open circuit, and short circuit).