Power system operation is increasingly reliant on Information and Communication Technologies (ICTs), which are essential for enhancing the resilience, reliability, and security of the future electricity supply. The advancement of ICTs has tightly integrated power grids with communication networks, giving rise to Cyber-Physical power Systems (CPS). However, this growing digitalization also increases system complexity, heightens vulnerability to cyber attacks, and alters traditional operational patterns. Consequently, this trend underscores the critical need for continued exploration and innovation in CPS to address emerging challenges. In this context, the availability of reliable test cyber-physical systems is crucial. The test CPS models must enable realistic analyses without exposing sensitive information about critical infrastructures, allowing researchers to thoroughly investigate newly introduced vulnerabilities and ensure the reliability and cyber security of CPS. To this end, we are motivated to have the following research focus: the synthetic network generation and vulnerability assessment of cyber-physical power systems...@en

Cyber security risks are emerging in Cyber-Physical power Systems (CPS) due to the increasing integration of cyber and physical infrastructures. Critical component identification is a crucial task for the mitigation and prevention of catastrophic blackouts. In this paper, we propose a novel method using graph data mining for critical CPS components identification named GraphCCI. First, it defines two categories of component correlations to reveal the cascading features of CPS. GraphCCI maps cascading failure datasets under time-varying operational states into weighted cascading graphs and constructs a graph database for graph data mining. By adopting graph data mining techniques, frequent subgraphs are identified to construct the Cascading Characteristics Graph (CC-Graph). Finally, the Node Criticality Index (NC-Index) is proposed to quantify the criticality of each CPS component. The experimental results on the IEEE 39-bus system verify the effectiveness of the proposed method and present an in-depth analysis of the CPS cascading features.@en

This article offers a novel perspective on identifying the critical branches under load redistribution (LR) attacks. Compared to the existing literature that is largely disruption-driven and based on dc state estimation, we propose to address the threat from LR attacks on a more fundamental level by modeling and analyzing the circulation of false data within the cyber network resulting from the coordinated branch and node measurement manipulation based on ac state estimation. We reveal the underlying mechanism that disturbing the coordinated and reconciled interactions among false data injections can effectively sever the completeness and consistency of the LR attack, thus reducing its damaging effect. We then develop a scalable and computationally efficient critical branch identification approach that evaluates and ranks branches in terms of their criticality according to the graph model of the false data circulation. Case studies are conducted on IEEE 14-, 39-, 118-bus systems and several large-scale models to validate the effectiveness and computational efficiency of the proposed approach. Simulation results show that the proposed approach scales well with the size of the system and can effectively mitigate the damaging effects of the LR attack in terms of operation cost and load shedding.

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Synthetic networks aim at generating realistic projections of real-world networks while concealing the actual system information. This paper proposes a scalable and effective approach based on graph neural networks (GNN) to generate synthetic topologies of Cyber-Physical power Systems (CPS) with realistic network feature distribution. In order to comprehensively capture the characteristics of real CPS networks, we propose a generative model, namely Graph-CPS, based on graph variational autoencoder and graph recurrent neural networks. The method hides the sensitive topological information while maintaining the similar feature distribution of the real networks. We used multiple power and communication networks to prove and assess the effectiveness of the proposed method with experimental results.@en

A topology attack, as a special class of false data injection attacks, tampers with topology information of a system to mislead the decision of the control center. This article conducts an in-depth study on topology attacks that aim to interfere with the judgment in topology information and pose potential damage by tampering with measurement data and protection information on branches, namely, protection-branch measurements-based topology attacks (PBT attacks). To achieve PBT attacks in actual networks, we study the protection settings and mechanisms in term of branches including transformers and transmission lines. Then, for the first time, we develop a bilevel model based on the protection configuration from the perspective of security-constrained economic dispatch. Meanwhile, since a bilevel model is constructed against dc state estimation, a conversion method in constructing attack vectors under PBT attacks against ac power system is proposed, which makes PBT attacks more suitable for actual power systems and more concealed. In a set of case studies on an IEEE 14-bus system, the simulation results verify the effectiveness of the model we proposed, analyze the vulnerability of network under PBT attacks, and then identify some critical branches that are defended to cope with PBT attacks. In addition, the comparison between PBT attacks and traditional cyber-overloaded attacks also shows a stronger threat of the studied attacks.@en

This letter develops a new bilevel optimization model to construct false data injection attack based on pre- and post- dispatch. In order to enhance the attack concealment, the proposed bilevel model can minimize the variation of uploaded measurements between pre- and post-attack before dispatching, after which the attack can lead the system to an uneconomic and insecure operating state after dispatching. Simulation results validate the effectiveness of the proposed bilevel model in term of operating cost and network overloads.

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The security issues of Cyber-Physical power Systems (CPS) have attracted widespread attention from scholars. Vulnerability assessment emerges as an effective method to identify the critical components and thus increase the system resilience. While efforts have been made to study the vulnerability features of power systems under the occurrence of a single, discrete disturbance or failure at a specific time instant, this paper focuses on identifying the critical components of the cyber-physical system considering time-varying operational states. To investigate the potentially ever-changing CPS vulnerability features, in this paper we construct a database of cascading failure chains using quasi-dynamic simulations to capture the vulnerability relationships among components under time-varying operational states. Then, by adopting sequential mining algorithms, we mine the most frequent cascading failure patterns and identify the critical components based on the data mining results. Simulation studies are conducted on IEEE 39-bus and IEEE RTS-96 systems to evaluate the effectiveness of the proposed method for the identification of critical components at both cyber and physical layers.

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