Cascading Failure Model Research Articles

Cascading failure of complex networks based on load redistribution and epidemic process

Diseases spreading over humans and animals is a ubiquitous process that has attracted much attention recently. To deeply explore the dynamics of the disease spread process on physical-contact networks, considering immunity exists in the population and dynamical behavior of viral transmission that have similar characteristics to cascading failure model based on complex network, the cascading failure model with load distribution parameters and epidemiological SIR model is proposed based on the local characteristic of nodes. Using this model, we investigate the virus outbreak conditions of Erdős–Rény (ER) and Albert–László Barabási (BA) networks, the formulas of critical values for networks recovery are derived. With numerical simulations, we obtain the relationship between the resilience of the networks against risk and the node redundancy factor. Furthermore, the intrinsic link between virus propagation rate and redundancy coefficient is obtained with the dynamical analysis, which is crucial for keeping networks robust against perturbations. These findings may help expand the understanding of the dynamic interactions between viral transmission and cascading failure, encouraging further study on the control and prevention of infectious disease spread.

Cascading expert failure

AbstractRecent research has shown how experts may fail in their duty as advisors by providing advice that leads to a worse outcome than that anticipated by the user of expert opinion. However, those models have focused on the immediate effects of the failure on experts and nonexperts. Using a cascading network failure model, I show how expert failure can cascade throughout multiple sectors, even those not necessarily purchasing the expert opinion. Consequently, even relatively small failures end up having outsized aggregate effects. To provide evidence of my theory, I look at two case studies of COVID expert advice to show how one seemingly minor failure ended up contributing to the pandemic. I conclude with a discussion on institutional frameworks that can prevent such cascades.

The study of transmission line fault probability fuzzy model based on the ice disaster climate

Power grid's weak links influence the safe operation of power system directly, this article will use the transmission line Fault probability fuzzy model based on the ice disaster climate to evaluate transmission lines weak links, so as to reduce the line fault. Firstly, the paper introduces the Fault probability fuzzy model; Then adopt the Pearson-III distribution curve to introduces the Fault probability fuzzy model under the ice disaster climate. The method in which the index of coherent value at risk is combined with the index of value at risk to evaluation the power gridfs weak links and the cascading failure model; Finally, the simulative results of IEEE 39-bus system show the reliability of the method. Through analysisCit is known that the method is better for power network planning and formulating preventive measures.

Risk assessment of cascading failures in power systems with increasing wind penetration

The ever-growing penetration of renewable based generation is leading to significant increases in the risk of cascading failures in low-inertia, and interconnection-rich, power systems. This paper proposes a framework for quantifying the risk of cascading failures in renewable-rich power systems with fast frequency response (FFR) services. This is achieved by developing a novel time-based dynamic model of cascading failures to capture the transient behaviours of the system response and quantify the risk of blackout. This dynamic cascading failure model is complemented by unit commitment for representative dispatch and includes the provision of FFR. The proposed framework is illustrated using a modified version of the Illinois 200-bus synthetic system. As key outputs, the results emphasize the importance of accurate modelling of ancillary services associated with frequency regulation in cascading failure analysis, and indicate that increased wind penetration can lead to a higher probability of power outages. Adopting FFR services can help to mitigate the cascading risk but may introduce rotor angle instability issues.

An Event-Triggered Hybrid System Model for Cascading Failure in Power Grid

Cascading failure models are important for understanding the mechanism of blackouts and evaluating the control strategies to prevent the failure propagation. The evolution of cascading failure in actual power grid is a continuous dynamic process triggered by discrete events, such as initial disturbances and physical responses. In this paper, we develop an event-triggered hybrid system model to describe the dynamic process of cascading failure. In the model, the evolution of continuous states of power grid is described by differential algebraic equations and the discrete events are defined as transitions between discrete states of power grid. The model also integrates multiple physical responses including relay protection, frequency regulation and dispatching action. Based on the developed model, we propose an event-triggered simulation method of cascading failure to accelerate the simulation process. Compared with the DC power flow model, hidden failure model and topological model, the simulation results of our model are more accurate because the statistical distribution of demand loss in our model is closer to historical blackouts data. The efficiency of the proposed event-triggered method is demonstrated by comparing our model with the time-driven model and three existing models. The experimental results show that our model can trade off the simulation accuracy and time consumption. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper focuses on modeling the dynamic process of cascading failure with multiple physical responses in power grid. We develop an event-triggered hybrid system model for cascading failure. In the model, the continuous dynamics of power grid and discrete events triggering the evolution of cascading failure are all described by the framework of hybrid system, which is a good example of modeling the hybrid system for automation researchers and engineers. By this way, the model is more accurate in describing the actual characteristics of cascading failure in power grid, and thus supporting the design and evaluation of control strategies for improving the stability of power grid. Based on the developed model, we propose an event-triggered simulation method of cascading failure, which aims to improve simulation accuracy while potentially reducing time consumption. In practice, the model can make fast control strategies to prevent the failure propagation.

Cascading failure in networks with dynamical behavior against multi-node removal

Complex systems in reality can be characterized as networks on which the diverse dynamical behaviors take place. The cascading failure that may cause the disasters triggered by minor events in these dynamic systems is a critical issue needed to reveal. To this end, we introduce a cascading failure model which considers the activity overload when a fraction of nodes is removed in a random way. The average activity and the giant component are derived to evaluate the performance of the network against the cascading failure. The results show the dynamical behavior is a critical factor on the robustness of networks. We found the different characteristics that the network is more robust with biochemical(ℬ), birth-death (ℬD) and regulatory (ℛ) dynamics as the homogeneity of the network increases, but as the heterogeneity of the network increases, the network with epidemic (ℰ) dynamics is more robust. Remarkably, the dynamics with ℬD and ℛ are more sensitive than the dynamics with ℬ and ℰ to the sensitivity factor ρ. It is helpful for the engineer to develop a more robust network with proper measures, especially with limited resources.

Dynamics of node cascading model with source-sink edges

The load is a very important physical quantity for cascading failures in critical infrastructure networks. However, in previous studies, regardless of the node cascading failure model or the edge cascading failure model, all load in the network is born on the nodes ignoring the possibility of load pouring into the network from edges. For example, almost all vehicles from communities, units, parking lots, etc. are flooding into the transportation network from roads. To this end, considering that the starting point and the destination of the transferred load are located on the “edge”, we give a new method to calculate the initial load on the node and construct a node cascading model with source-sink edges. According to the information about the degree of two nodes connecting the edge, we define the edge weight and give the preferential mechanism of load destination selection. In addition, different from previous models, the load transmitted between two edges will be evenly distributed on all nodes of the shortest paths. By removing the node with the highest load in some artificial networks and two real networks, we study the cascading dynamics. We find the interesting phenomenon of the capacity paradox that the higher capacities of all nodes do not mean that the network has the higher robustness. By two metrics to quantify the network robustness, we further evaluate the robust mode of the network under different parameters, and study the optimal cost to suppress cascading failures in some networks.

Sequential recovery of cyber-physical power systems based on improved Q-learning

Cascading failures are one of the most severe threats to cyber-physical power systems. Most studies explore the cascading failure problem from the attacker’s perspective while recovering the system from cascading failures is still lacking. This paper proposes a sequential recovery analysis process built within the framework of the cascading failure model, which considers the coupling mechanism of cyber-physical power systems and cascading failures. Then the improved Q-learning based on candidate room and dynamic factor idea of simulated annealing is employed to achieve faster and more efficient identification of optimal recovery sequence for failed lines under resource-limited conditions. Finally, the load recovery performance of four recovery strategies in the single-layer and double-layer systems are compared. The simulation results prove the effectiveness of the proposed sequential recovery process and the recovery strategy based on improved Q-learning.

Robustness analysis of chemical process systems based on complex network non‐linear load capacity model

AbstractIn the complex network of chemical process systems, if a node fails, it may trigger cascading failures and affect normal operation. To enhance the ability of chemical process systems to maintain normal operation after the cascading failure, this paper presents cascading failure modelling and robustness analysis of chemical process systems based on the complex network non‐linear load capacity model. First, based on complex network theory, a complex network model of the chemical process is constructed; then, three cascading failure models are constructed using a combination of linear and non‐linear load capacity models and initial load and initial residual capacity redistribution strategies; and finally, the nodes with the maximum node degree are deliberately attacked to analyze the robustness of the chemical process system in response to cascading failure. The case study shows that the proposed models are valid and feasible, and the robustness of the chemical process system is enhanced as the load and capacity parameters are increased. By reasonably setting the initial load and adjusting the model parameters, the robustness can be effectively improved, providing a theoretical reference for improving the robustness of the actual chemical process system in response to cascading failure.

Cascading Failure Analysis of Hierarchical Industrial Wireless Sensor Networks under the Impact of Data Overload

As industrialization accelerates, the industrial sensor network environment becomes more complex. Hierarchical multi-cluster wireless sensing network topology is generally used due to large-scale industrial environments, harsh environments, and data overload impact. In industrial wireless sensor networks, the overload of some nodes may lead to the failure of the whole network, which is called cascading failure. This phenomenon has incalculable impact on industrial production. However, cascading failure models have mainly been studied for planar structures, and there is no cascading failure model for hierarchical topologies in industrial environments. Therefore, this paper built a cascading failure model for hierarchical industrial wireless sensor networks (IWSNs) for realistic industrial network topologies. By establishing an evaluation mechanism considering the efficiency of the network and the viability of nodes, the network communication efficiency that is not considered in the traditional evaluation mechanism is solved. In addition, aiming at the problem of network topology changes caused by node failure, dynamic load distribution methods (ADD, SLD) are used to improve network invulnerability. Theoretical analysis and experimental results show that the traditional allocation method (SMLD) does not apply in hierarchical topologies; when the general cluster head node capacity is moderate, increasing the capacity of single-hop cluster head nodes can prevent cascading failures more effectively.

Evaluating the dynamic resilience of the multi-mode public transit network for sustainable transport

Improving multi-mode public transit resilience becomes crucial as urban traffic infrastructure gradually completes. This paper aims to build a dynamic resilience evaluation method and explore the resilience evolution process for multi-mode public transit. For establishing the resilience evaluation method, we considered both structural and functional indicators of the network. Then, we built a nonlinear load-capacity model and a cascading failure model to reflect the dynamic feature of resilience. Next, attack scenarios were designed according to the actual characteristics of the multi-mode public transit. Using the passenger flow data from Xi'an, we tested the impact of station capacity on the dynamic resilience results and analyzed the resilience evolution process. The results indicate that the network's resilience performance was the worst when the subway station was destroyed. Through adjusting the capacity control parameters, we found the best station capacity control parameter is α=0.5,β=0.4. Moreover, the sharply reducing intervals found in the results could help traffic managers improve transport efficiency while controlling costs.

Cascading failure in coupled networks of transportation and power grid

The large-scale application of electric vehicles (EVs) makes the power grid and transportation network deeply coupled, resulting in the propagation of random disturbances and failures between the two systems. This paper proposes an analysis method of cascading failure in the coupled networks of transportation networks and power grids. The cascading failure model of a power grid with large-scale EV charging loads is established by using a direct current power flow based on the frequency response (DFR). The behavior characteristics of EVs and transportation states under failure conditions are analyzed by combining the travel decisions of users with the traffic network equilibrium. Considering the coupling relationship between the power grid and transportation networks at charging station nodes, the load–capacity model is adopted to describe the propagation of failures between the two networks. Finally, the propagation characteristics of failures considering the primary frequency modulation and rationality degree of EVs are simulated and analyzed. The simulation results show that the application of EVs makes the power grid and transportation mutually dependent, and the primary frequency modulation characteristic and bounded rationality of EVs can weaken the effect of cascading failure in the coupled networks.

Modelling Underload Cascading Failure and Mitigation Strategy of Supply Chain Complex Network in COVID-19

The outbreak of COVID-19 has caused problems such as shortage of workforce, cost increase, cash flow tension, and uncertainty of supply chain. It has a specific negative impact on the raw material supply, procurement management, production resumption, logistics, and market of the supply chain, which can trigger cascading failures in supply chain networks. Aiming at the failure of upstream/downstream firms in supply chain networks due to the decreased product demand/material supply under the COVID-19, the present study adopted an underload cascading failure model for the supply chain networks. In this model, the hierarchical supply chain networks were constructed based on the Erdos Renyi (ER) model and Barabasi Albert (BA) model. The validity of the model was verified under random attack and target attack. In the random attack mode, the influences of model parameters were studied, and in the target attack mode, the influence of target protection and random protection measures on enhancing network invulnerability was also studied. Simulation results showed that the initial load and capacity lower bound of nodes impact cascading failure size. The former has a positive correlation with cascading failure size, while the latter negatively correlates with cascading failure size. Furthermore, random protection measures are more practical to prevent cascading failures.

A model for cascading failures with the probability of failure described as a logistic function

In most cascading failure models in networks, overloaded nodes are assumed to fail and are removed from the network. However, this is not always the case due to network mitigation measures. Considering the effects of these mitigating measures, we propose a new cascading failure model that describes the probability that an overloaded node fails as a logistic function. By performing numerical simulations of cascading failures on Barabási and Albert (BA) scale-free networks and a real airport network, we compare the results of our model and the established model describing the probability of failure as a linear function. The simulation results show that the difference in the robustness of the two models depends on the initial load distribution and the redistribution of load. We further investigate the conditions of our new model under which the network exhibits the strongest robustness in terms of the load distribution and the network topology. We find the optimal value for the parameter of the load distribution and demonstrate that the robustness of the network improves as the average degree increases. The results regarding the optimal load distribution are verified by theoretical analysis. This work can be used to develop effective mitigation measures and design networks that are robust to cascading failure phenomena.

Invulnerability Analysis and Optimization Strategy of Sector Network Using Cascading Failure Model

Resolving the challenge of flight delays caused by air traffic congestion renders it necessary to explore the mode of congestion propagation. By applying complex network theory, this article establishes a complex network structure where airspace sectors act as nodes, and the edges represent traffic flow relationships between sectors. In addition, a cascading failure model is proposed to analyze the airspace sector network’s invulnerability. The critical threshold and the sector abnormality rate are determined according to the cascading failure measurement indexes. Based on the remaining capacity of sector nodes, two optimization strategies are proposed: adjacent load redistribution strategy and local load redistribution strategy. The simulation results demonstrate that the local load redistribution strategy greatly improves the airspace sector network’s invulnerability to cascading failure.

Modeling and Analysis of Cascading Failures in Cyber-Physical Power Systems Under Different Coupling Strategies

The cyber-physical power systems(CPPS) may experience cascading failures due to damage to power transmission lines. This paper focuses on modeling and analysis of CPPS with grid-line random failure under different coupling strategies to reveal the coupling mechanisms between the power grid and the information network. The CPPS mainly includes the power grid, information network, and the coupling connections between them. An uncertain failure mechanism is introduced in the cascading failures model to determine the failed power lines. The DC optimal power flow calculates and distributes the power flows to determine the tripping rates of power lines. A new comprehensive metric is proposed to assess the critical nodes, which considers both topology and state information of the power grid. According to some assessment metrics, the nodes in the power grid and information network are ranked. The CPPS model is constructed by coupling the power and information nodes in specific sequences. The cascading failures of CPPS are simulated when the power lines suffer random attacks to obtain the most robust coupling method for typical coupled CPPSs.

Percolation on interdependent networks with different group size distributions under targeted attack

Traditional research studies on interdependent networks with groups ignore the relationship between nodes in dependency groups. In real-world networks, nodes in the same group may support each other through cooperation and tend to fail or survive together. In this paper, based on the framework of group percolation, a cascading failure model on interdependent networks with cooperative dependency groups under targeted attacks is proposed, and the effect of group size distributions on the robustness of interdependent networks is investigated. The mutually giant component and phase transition point of networks with different group size distributions are analyzed. The effectiveness of the theory is verified through simulations. Results show that the robustness of interdependent networks with cooperative dependency groups can be enhanced by increasing the heterogeneity between groups under targeted attacks. The theory can well predict the numerical simulation results. This model provides some theoretical guidance for designing robust interdependent systems in real world.

Cascading vulnerability analysis of unsafe behaviors of construction workers from the perspective of network modeling

PurposeThe construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.Design/methodology/approachAn unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.FindingsComplex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.Originality/valueThis research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.

Cascade-risk-informed transmission expansion planning of AC electric power systems

Latest standards for transmission system planning require investigation of the performance during the simultaneous loss of two elements and an assessment of the risk of cascading outages. Consequently, transmission system expansion must also be supported by in-depth security analyses. In this study, we propose a risk-informed transmission expansion planning method. At the core of the method is an AC based cascading failure model that simulates the power system responses after contingencies. The method aims to provide an optimal expansion plan striking the balance between the cost of expansion and the risk of systemic failures. Addressing this challenge requires a simulation based multi-objective optimization, which we tackle using a meta-heuristic algorithm. The tool developed for transmission expansion planning is applied to the IEEE 118-bus system and compared with state-of-the-art transmission expansion approaches. Remarkably, the proposed TEP procedure produces solutions that improve system security at comparable expansion costs.

Supply crisis propagation in the global cobalt trade network

Cobalt is widely recognized as a strategic mineral especially for current energy and low carbon revolution. Nonetheless, the geographically concentrated supply makes reliable access to cobalt more of a strategic concern. Understanding the systemic impacts of epicenter economies, and how crises propagate if these economies were hit by negative shocks, are thus important for policy makers. To explore this issue, we construct a global cobalt trade network using the data of 2019, upon which a cascading failure model is established. Accordingly, we simulate the avalanche process, and an avalanche network is derived to analyze the structural features of crisis propagation dynamics. Results show that (1) When the ratio r ≥ 8, avalanche sizes of major epicenter economies increase rapidly from 22 to 36; (2) Democratic Republic of Congo, Mainland of China, Germany, and United Kingdom have systemic impacts in the global cobalt trade network; (3) Out-degree, in-strength, out-strength, and betweenness show influences on the dynamics of supply crisis propagation, while in-degree and eigencentrality do not show clear influences on the propagation dynamics; (4) Indirect cascades account for a significant fraction of the avalanche sizes. These findings contribute to more efficient supply management of the strategic raw material for policy makers.