Robust Bayesian Regression Model of Centrality and Voltage Stability Index for Power Networks under Nodal Attack

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordElectrical node centrality for the power networks is an essential parameter to identify the critical nodes under attack. Topological analysis is vital for evaluating the network robustness while electrical characteristics have to be considered to make the analysis consistent for realistic power networks. However, the capacity limit of the power network changes under various nodal attacks. It is essential to find the relationship between the loading margin limit of the power network with the node centrality features, so that appropriate measures can be considered to improve the robustness of the power networks. Thus, voltage stability index (VSI) is defined for every node, and its centrality features are modelled. Robust Bayesian regression is used to model the nodes responsible for a change in loading margin and causing grid blackout. The method has been validated on benchmark complex power networks like reduced Great Britain network, IEEE 57-bus and IEEE 118-bus systems

Cyber-Physical Vulnerability Assessment in Smart Grids Based on Multilayer Complex Networks

This article belongs to the Special Issue Cybersecurity and Privacy-Preserving in Modern Smart GridIn the last decade, the main attacks against smart grids have occurred in communication networks (ITs) causing the disconnection of physical equipment from power networks (OTs) and leading to electricity supply interruptions. To deal with the deficiencies presented in past studies, this paper addresses smart grids vulnerability assessment considering the smart grid as a cyber-physical heterogeneous interconnected system. The model of the cyber-physical system is composed of a physical power network model and the information and communication technology network model (ICT) both are interconnected and are interrelated by means of the communication and control equipment installed in the smart grid. This model highlights the hidden interdependencies between power and ICT networks and contains the interaction between both systems. To mimic the real nature of smart grids, the interconnected heterogeneous model is based on multilayer complex network theory and scale-free graph, where there is a one-to-many relationship between cyber and physical assets. Multilayer complex network theory centrality indexes are used to determine the interconnected heterogeneous system set of nodes criticality. The proposed methodology, which includes measurement, communication, and control equipment, has been tested on a standardized power network that is interconnected to the ICT network. Results demonstrate the model’s effectiveness in detecting vulnerabilities in the interdependent cyber-physical system compared to traditional vulnerability assessments applied to power networks (OT).This research was funded by Fundación Iberdrola España, within the 2020 research support scholarship program

Nodal vulnerability to targeted attacks in power grids

Due to the open data policies, nowadays, some countries have their power grid data available online. This may bring a new concern to the power grid operators in terms of malicious threats. In this paper, we assess the vulnerability of power grids to targeted attacks based on network science. By employing two graph models for power grids as simple and weighted graphs, we first calculate the centrality metrics of each node in a power grid. Subsequently, we formulate different node-attack strategies based on those centrality metrics, and empirically analyse the impact of targeted attacks on the structural and the operational performance of power grids. We demonstrate our methodology in the high-voltage transmission networks of 5 European countries and in commonly used IEEE test power grids.Network Architectures and Service