In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.

In the state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection (FDI) attack. In this letter, to enforce the security of power networks, we propose a method of detecting an FDI attack. In the proposed method, an FDI attack is detected by randomly choosing sensors used in the state estimation. The effectiveness of the proposed method is presented by two examples including the IEEE 14-bus system.

In the state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection (FDI) attack. In this letter, to enforce the security of power networks, we propose a method of detecting an FDI attack. In the proposed method, an FDI attack is detected by randomly choosing sensors used in the state estimation. The effectiveness of the proposed method is presented by two examples including the IEEE 14-bus system.

In a power network, it is important to detect a cyber attack. In this paper, we propose a method for detecting false data injection (FDI) attacks in distributed state estimation. An FDI attack is well known as one of the typical cyber attacks in a power network. As a method of FDI attack detection, we consider calculating the residual (i.e., the difference between the observed and estimated values). In the proposed detection method, the tentative residual (estimated error) in ADMM (Alternating Direction Method of Multipliers), which is one of the powerful methods in distributed optimization, is applied. First, the effect of an FDI attack is analyzed. Next, based on the analysis result, a detection parameter is introduced based on the residual. A detection method using this parameter is then proposed. Finally, the proposed method is demonstrated through a numerical example on the IEEE 14-bus system.