Vulnerability Analysis Of Power Grid Research Articles

Power grid vulnerability analysis using complex network theory: A topological study of the Nordic transmission grid

Power grid vulnerability analysis using complex network theory: A topological study of the Nordic transmission grid

Smart power grid vulnerability analysis in composite system through power grid modelling

<p>Though the number of traditional literature reviews most of the researchers in this area have concluded that modern intelligent electric network a referred standard new model which is considering for proposed power flow model analysis of Smart power grid vulnerability through composite network. By the theorem of Max-Flow and complex power network theorems that are represented the advance vulnerability indices for pick out endangered lines of the smart grid network. In other hand, the power flow model and existing models simulation are examined on the IEEE 39-bus test model. The results of the simulation introduced that the proposed concept of grid model, estimation and the index application are more adequate in smart grid weakness power and efficiency analysis. In this paper briefly summarize the methods and probabilities of a vulnerability index which was explained in specific in references. Therefore, it is imperative to further implement new models and new tools so as to reach the novel state and moderate the huge or massive potential shutoff.</p>

Generative adversarial network-enabled learning scheme for power grid vulnerability analysis

Generative adversarial network-enabled learning scheme for power grid vulnerability analysis

Generative adversarial network-enabled learning scheme for power grid vulnerability analysis

Real measurements of power grids are usually limited for research and modelling of extreme events such as the impact of typhoons due to confidentiality concerns. To overcome the dearth of valuable, trustworthy data, this paper proposes an adaptive learning method based on the generative adversarial network. To obtain informative examples, the falsely classified examples together with examples that are correctly classified with low confidence are used to train a GAN for producing synthetic examples to reinforce the learning. The new power grid examples are selected according to the likelihood of the true data distribution. An evaluation was conducted with data acquired by the China Southern Power Grid in Hainan. Most significantly, the performance of detecting the occurrence of a power grid fault under the impact of typhoons is greatly improved. It was demonstrated that the proposed method improved the performance of predicting power grid fault in extreme events by 8.9%. Using the modulated GAN network, the synthetic data closely follows the distribution of the real data as indicated by large p-values. Our method takes minutes to complete training a model, which enables an efficient response to disasters with modern computing facilities such as edge computing.

Multi-period vulnerability analysis of power grids under multiple outages: An AC-based bilevel optimization approach

This paper describes a methodology for the N-k contingency analysis of bulk power systems. The method encompasses the evaluation of contingencies’ effects on the power system over a range of system demand levels. The proposed model is inherently a multi-period bilevel optimization problem. Unlike the conventional bilevel optimization problems for the N-k contingency analysis, the proposed model considers the effects of reactive power dispatch, losses, and voltage profile. In doing so, the problem is formulated as a multi-period AC-based bilevel mixed-integer nonlinear programming (MINLP) problem. To guarantee the global optimality of the solution, this paper linearizes and then transforms it into a one-level mixed-integer linear programming (MILP) problem using different linearization techniques and the duality theory. The simulation results on the annual load profile of the IEEE Reliability Test System (RTS) verify the effectiveness of the proposed model.

Graph-theoretic algorithms for cyber-physical vulnerability analysis of power grid with incomplete information

A key focus recently has been in assessing the risk of a coordinated cyber-physical attack and minimizing the impact of a successful attack. Most of the cyber-attackers will have limited system information and conventional power grid N − 1 security analysis cannot be extended to assess the risk. Centrality measures are widely used in the network science and an attacker with incomplete information can use it to identify power system vulnerabilities by defining the system as a complex network but without real-time system measurements. This paper presents a graph theory based centrality indices for vulnerability assessment of the power system due to various bus and branch contingencies using limited system information and provides a preliminary defense mechanism to prevent such an attack. Proposed work answers the fundamental question of possible attack scenarios by balancing risk (limited information with low risk to get caught or high risk attack to access more system information) and impact (identifying contingencies with maximal impact on system operation). Statistical comparisons are made between the graph theory measures compared to the corresponding DC power flow based N − X linear sensitivity measures. A unified N − X centrality based performance index is proposed and validated against the AC power flow based performance index by doing the real-time simulations of an N − 3 attack scenario. Defensive mechanisms using topology-based performance indices are also presented.

Vulnerability analysis of power grid with the network science approach based on actual grid characteristics: A case study in Iran

The vulnerability analysis of power grids has long been of paramount interest to researchers and authorities. Network vulnerability analysis refers to beforehand evaluation of the impact of local failures on the network as a whole so that proper measures could be adopted before occurrence of any major crisis. Previous studies on power grid vulnerabilities that are based on network science concept have been mostly concentrated on topological measures and unweighted networks. In this paper, grid vulnerability identification is carried out by combined use of grid network topology and centrality measures along with real and physical characteristics of power grid. Namely line load and failure rate, with the help of Weighted PageRank algorithm. The main advantage of this approach is that it eliminates complex and time-consuming differential calculations, which results in reduced computation time, and allows real-time updating of the result based on changes in the actual grid characteristics. The proposed model was validated by implementation over a section of Iranian 400 kV and 230 kV power grids. The striking accuracy of the achieved result was confirmed by comparison against results that obtained through calculations by Iran’s national dispatching body.

The Structural Vulnerability Analysis of Power Grids Based on Second-Order Centrality

The Structural Vulnerability Analysis of Power Grids Based on Second-Order Centrality

A power flow based model for the analysis of vulnerability in power networks

Abstract An innovative model which considers power flow, one of the most important characteristics in a power system, is proposed for the analysis of power grid vulnerability. Moreover, based on the complex network theory and the Max-Flow theorem, a new vulnerability index is presented to identify the vulnerable lines in a power grid. In addition, comparative simulations between the power flow based model and existing models are investigated on the IEEE 118-bus system. The simulation results demonstrate that the proposed model and the index are more effective in power grid vulnerability analysis.

The Structural Vulnerability Analysis of Power Grids Based on Overall Information Centrality

The Structural Vulnerability Analysis of Power Grids Based on Overall Information Centrality

An improved framework for power grid vulnerability analysis considering critical system features

In recent years the rapid development of complex network theory has provided a new angle on the vulnerability analysis of a power grid. However, current analysis models are usually general ones that may ignore some specific features of power systems. In order to address the issue, this paper proposes an improved framework for the vulnerability analysis of power grids. Firstly, the traditional topology based graph model is improved by depicting a power grid as a weighted graph based on the reactance matrix. Secondly, the concept of load is redefined by using power angle information. Thirdly, the power flow constraints are adopted instead of the shortest path based flow scheme. Based on the proposed framework, an improved dynamic analysis model is developed. In addition, numerical simulations for both a general traditional model and the proposed model are investigated based on the IEEE 118-bus system respectively. The comparison demonstrates that the improved model is more effective and efficient for the vulnerability analysis of a power grid.

Vulnerability Analysis of Power Grids With Line Switching

Vulnerability analysis of a power grid, especially in its static status, is often performed through solving a bi-level optimization problem, which, if solved to optimality, yields the most destructive interdiction plan with the worst loss. As one of the most effective operations to mitigate deliberate outages or attacks, transmission line switching recently has been included and modeled by a binary variable in the lower level decision model. Because this bi-level (or an equivalent min-max) problem is a challenging nonconvex discrete optimization problem, no exact algorithm has been developed, and only a few recent heuristic procedures are available. In this paper, we present an equivalent single-level reformulation of this problem, and describe a column-and-constraint generation algorithm to derive the global optimal solution. Numerical study confirms the quality of solutions and the computational efficiency of the proposed algorithm. Discussion and analysis of the mitigation effect of line switching are presented.

Optimization Strategies for the Vulnerability Analysis of the Electric Power Grid

Identifying small groups of lines, whose removal would cause a severe blackout, is critical for the secure operation of the electric power grid. We show how power grid vulnerability analysis can be studied as a bilevel mixed integer nonlinear programming problem. Our analysis reveals a special structure in the formulation that can be exploited to avoid nonlinearity and approximate the original problem as a pure combinatorial problem. The key new observation behind our analysis is the correspondence between the Jacobian matrix (a representation of the feasibility boundary of the equations that describe the flow of power in the network) and the Laplacian matrix in spectral graph theory (a representation of the graph of the power grid). The reduced combinatorial problem is known as the network inhibition problem, for which we present a mixed integer linear programming formulation. Our experiments on benchmark power grids show that the reduced combinatorial model provides an accurate approximation, to enable vulnerability analyses of real-sized problems with more than 16,520 power lines.