Cyber-Risk Assessment of Wide Area Measurement Systems in Smart Grids Using Electrical Centrality Metrics

Wide Area Measurement System (WAMS) may be considered as the most important application in smart transmission grids. State Estimation has been considered as the kernel of WAMS since such a package extracts creditable states from the raw and noisey data. Indeed, WAMS creates data flow in order to achieve reliable and efficient energy flow. More data flow benefits the system operation but at the same time increases the cyber risk of the system. This study aims to assess the cyber risk of elements of WAMS. To do this, cyber dependency is modeled as the dependency graph and then such a graph is examined by two centrality metrics (i.e. eigenvector and betweenness centralities). Two classes of nodes (i.e. State and Intermediate nodes) are detected in WAMS and simulation results show that the aforementioned centrality metrics are able to assess such classes of nodes and the proposed method has the ability of designing resilient WAMS.