Cascading phenomena have been studied extensively in various networks. Particularly, it has been shown that the community structures in networks impact their cascade processes. However, the role of community structures in cascading failures in power grids have not been studied heretofore. In this paper, cascading failures in power grids are studied using interaction graphs. Key evidence has been provided that the community structures in interaction graphs bear critical information about the cascade process and the role of system components in cascading failures in power grids. Further, a centrality measure based on the community structures is proposed to identify critical components of the system, which their protection can help in containing failures within a community and prevent the propagation of failures to large sections of the power grid. Various criticality evaluation techniques, including data-driven, epidemic simulation-based, power system simulation-based and graph-based, have been used to verify the importance of the identified critical components in the cascade process and compare them with those identified by traditional centrality measures. Moreover, it has been shown that the loading level of the power grid impacts the interaction graph and consequently, the community structure and criticality of the components in the cascade process.

Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, a graph signal processing (GSP) framework is used to represent and analyze the inter-related smart grid measurement data for security and reliability analyses. The effects of various cyber and physical stresses in the system are evaluated in different GSP domains including vertex domain, graph-frequency domain, and the joint vertex-frequency domain. Two novel techniques based on vertex-frequency energy distribution, and the local smoothness of graph signals are proposed and their performance have been evaluated for detecting and locating various cyber and physical stresses. Based on the presented analyses, the proposed techniques show promising performance for detecting sophisticated stresses with no sharp changes at the onset, for detecting abrupt load changes, and also for locating stresses.

Understanding and analyzing cascading failures in power grids have been the focus of many researchers for years. However, the complex interactions among the large number of components in these systems and their contributions to cascading failures are not yet completely understood. Therefore, various techniques have been developed and used to model and analyze the underlying interactions among the components of the power grid with respect to cascading failures. Such methods are important to reveal the essential information that may not be readily available from power system physical models and topologies. In general, the influences and interactions among the components of the system may occur both locally and at distance due to the physics of electricity governing the power flow dynamics as well as other functional and cyber dependencies among the components of the system. To infer and capture such interactions, data-driven approaches or techniques based on the physics of electricity have been used to develop graph-based models of interactions among the components of the power grid. In this survey, various methods of developing interaction graphs as well as studies on the reliability and cascading failure analysis of power grids using these graphs have been reviewed.

While internetwork connections among private and public networks are inevitable and essential for enhancing the data rate and range of communication among networks, they introduce new vulnerabilities and risks of security breach to the interconnected networks. In this article, a novel information-theoretic model is proposed to analyze the tradeoff between network security and data-rate enhancement considering homogeneous and heterogeneous internetwork connections between two networks. Various patterns of internetwork connections among gateway nodes are studied. The implications of internetwork connections on data manipulation due to security breach by attackers are investigated by modeling the internetwork connections as a noisy communication channel. Specifically, the fundamental limit of the maximum achievable capacity afforded by internetwork connections is characterized, while considering the impact of security vulnerabilities that the internetwork connections introduce. For a nominal data-rate below the fundamental limit, the effect of security breach can be diminished to an arbitrarily low level by channel coding. Notably, any nominal data-rate above the fundamental limit is not achievable; hence, a packet-loss distortion is introduced to keep the data-rate below the fundamental limit. Moreover, the fundamental limit is used to find the optimal number of internetwork connections between two networks under prescribed data-rate and packet-loss distortion constraints.

Smart grids are large and complex cyber physical infrastructures that require real-time monitoring for ensuring the security and reliability of the system. Monitoring the smart grid involves analyzing continuous data-stream from various measurement devices deployed throughout the system, which are topologically distributed and structurally interrelated. In this paper, graph signal processing (GSP) has been used to represent and analyze the power grid measurement data. It is shown that GSP can enable various analyses for the power grid's structured data and dynamics of its interconnected components. Particularly, the effects of various cyber and physical stresses in the power grid are evaluated and discussed both in the vertex and the graph-frequency domains of the signals. Several techniques for detecting and locating cyber and physical stresses based on GSP techniques have been presented and their performances have been evaluated and compared. The presented study shows that GSP can be a promising approach for analyzing the power grid's data.