Improved FDIA threat protection mechanisms in smart grid

Abstract

Smart grid uses various communication technologies to enhance the reliability and efficiency of the power grid. It allows bi-directional flow of electricity and information, about the current status of the grid and requirements of customers, among diverse groups in the grid, i.e., connect generation, distribution, transmission, and consumption subsystems. Thus, a smart grid reduces the power losses and increases the efficiency of electricity generation and distribution. Although the smart grid enhances the services of the grid, it endangers the grid to the cyber security threats that communication networks suffer from in addition to other novel threats because of the nature of the power grid. For instance, the electricity consumption messages sent from consumers to the utility company via the wireless network may be captured, modified, or replayed by adversaries. As a result, security and privacy concerns are major challenges in the smart grid. State estimation in the electric power system is the process that describes the condition of the grid by estimating the state variables using the data obtained by sensors placed in numerous parts of the grid. The continuous operation of the grid requires the state estimation to be done using the right data. To detect the existence of any bad data that may mislead state estimation, the renowned Bad Data Detection Test (BDD) is used. However, the false data injection attack (FDIA) bypasses the BDD test effortlessly. Several kinds of research have been conducted to detect the presence of FDIA. This paper presents two Convex Optimization-based Robust Principal Component Analysis (RPCA) algorithms that use and#119897;1 norm as convex surrogate replacing the non-convex lo norm, for solving this problem. The first technique uses a proximal gradient algorithm that is directly applied to the primal problem. The second technique uses a gradient algorithm applied to the conjugate transpose problem. newline