Biblio

In power networks, it is important to detect a cyber attack. In this paper, we propose a detection method of false data injection (FDI) attacks. FDI attacks cannot be detected from the estimation error in power networks. The proposed method is based on the distributed state estimation, and is used the tentative estimated state. The proposed method is demonstrated by a numerical example on the IEEE 14-bus system.

In 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 attack. In this paper, to enforce security of power networks, we propose a method of detecting a false data injection attack. In the proposed method, a false data injection attack is detected by randomly choosing sensors used in state estimation. The effectiveness of the proposed method is presented by two numerical examples including the IEEE 14-bus system.

Harmonic distortions come into existence in the power system not only due to nonlinear loads of consumers but also due to custom power devices used by power utilities. These distortions are harmful to the power networks as these produce over heating of appliances, reduction in their life expectancy, increment in electricity bill, false tripping, etc. This paper presents an effective, simple and direct approach to identify the problematic cause either consumer load or utility source or both responsible for harmonics injection in the power system. This technique does not require mathematical model, historical data and expert knowledge. The online methodology is developed in the laboratory and tested for different polluted loads and source conditions. Experimental results are found satisfactory. This proposed technique has substantial potential to determine the problematic cause without any power interruption by plug and play operation just like CCTV.

In this paper we conduct an empirical study with the purpose of identifying common software weaknesses of embedded devices used as part of industrial control systems in power grids. The data is gathered about the devices and software of 6 companies, ABB, General Electric, Schneider Electric, Schweitzer Engineering Laboratories, Siemens and Wind River. The study uses data from the manufacturersfi online databases, NVD, CWE and ICS CERT. We identified that the most common problems that were reported are related to the improper input validation, cryptographic issues, and programming errors.