Cascading Failure Propagation Research Articles

Component-based modeling of cascading failure propagation in directed dual-weight software networks

Software vulnerabilities often lead to cascading failures, resulting in service unavailability and potential breaches of user data. However, existing models for cascading failure propagation typically focus solely on the static design’s calling relationships, disregarding dynamic runtime propagation paths. Moreover, current network topology models primarily consider function calling frequency while overlooking critical factors like internal failure probability and component failure tolerance rates. Yet, these factors significantly influence the actual propagation of software cascading failures. In this study, we address these limitations by incorporating internal failure probabilities and calling frequencies as node and edge weights, respectively. This forms the basis of our component-based directed dual-weight software network cascading failure propagation model. This model encompasses the evaluation of cascading failure propagation through intra-component and inter-component propagation probabilities, alongside the constraint of component failure tolerance rates. Through extensive experiments conducted on six real-world software applications, our model has demonstrated its effectiveness in predicting software cascading failure propagation processes. This method deepens our understanding of software failures and structures, equipping software testers with the knowledge to make well-informed judgments regarding software quality concerns.

Quantification of Cascading Failure Propagation in Power Systems

Quantification of Cascading Failure Propagation in Power Systems

Percolation behavior analysis on n-layer edge-coupled interdependent networks

The robustness of interdependent networks has always been studied for years and has attracted wide attention from researchers in different fields. Noting that not all networks are coupled by nodes, networks coupled by edge are also worth further studying. At present, most studies on the edge-coupled interdependent networks are built on double layers, however, researches on multi-layer edge-coupled interdependent networks have hardly been conducted. In this paper, an n-layer edge-coupled interdependent network model was proposed, and the cascading failure propagation mechanism caused by attacking a small number of edges was studied. Based on the self-consistent probability theory that can significantly simplify the mathematical analysis of such systems, the phase transition behavior was theoretically analyzed. The research results help to deepen the understanding of the robustness of multi-layer interdependent networks and provide some valuable advice for the protection and improvement of complex network systems.

A cascading failure propagation model for a network with a node emergency recovery function

Cascading failures are ubiquitous in physical networks such as power grids and water networks and have become a trending research topic in the field of complex networks. In real cases, once a node in a network fails, the entire system can completely collapse, causing considerable economic losses and casualties. Implementing an appropriate node repair strategy can effectively prevent system crashes in complex networks due to cascading failure. This paper presents a network cascading failure propagation model with a node emergency recovery function. Numerical simulations with typical case scenarios are conducted to analyze network cascading failure when the nodes have an emergency recovery probability. The results of the case study show that incorporating an emergency recovery probability for nodes can reduce the maximum failure probability of nodes in a network and reduce the risk of network failure. As the recovery probability increases, the network failure risk probability gradually decreases. The proposed network cascading failure propagation model with a node emergency recovery function can accurately reflect the dynamic behavior of complex network cascades and provide a reference for the control and prevention of cascading failures in actual networks.

Robustness analysis of interdependent network accounting for failure probability and coupling patterns.

The robustness of interdependent networks against perturbations is an important problem for network design and operation. This paper focuses on establishing a cascading failure dynamics model and analyzing the robustness for interdependent networks, in which the states of the nodes follow certain failure probability and various connectivity patterns. First, to describe the removal mechanism of an overloaded node, the failure probability associated with the load distribution of components was proposed. Then, we present the node capacity cost and the average capacity cost of the network to investigate the propagation of cascading failures. Finally, to discuss the impact of the configuration parameters on robustness, some numerical examples are conducted, where the robustness was analyzed based on the proposed method and different interdependence types. Our results show that, the larger the overload parameter, the more robust the network is, but this also increases the network cost. Furthermore, we find that allocating more protection resources to the nodes with higher degree can enhance the robustness of the interdependent network. The robustness of multiple-to-multiple interdependent networks outperforms that of one-to-one interdependent networks under the same coupling pattern. In addition, our results unveil that the impact of coupling strategies on the robustness of multiple-to-multiple interdependent networks is smaller than that of one-to-one interdependent networks.

An integrated approach to quantitative resilience assessment in process systems

Chemical process systems are becoming more automated and complex, which leads to increased interaction and interdependence between the human and technical elements of process systems. This urges the need for updating the safety assessment method by treating “safety” as an emergent property of a system. Uncertainty comes together with complexity. To enhance system ability of dealing with uncertain disruptions, this paper proposes a quantitative resilience assessment method by modeling the failure propagation (initiated by a disruption) across the functional units of a system. The Functional Resonance Analysis Method (FRAM) is utilized to model the system operation to represent the relationship among its function units and to consider the interactions among human-technical factors. Then, a Cascading Failure Propagation Model (CFPM) is developed to quantify the fault propagation process and reflect the system functionality changes over time for resilience assessment. The proposed method is applied to a propane-feeding control system. The results show that it can help practitioners understand the process of fault propagation and risk increase, identify potential ways to design a more resilient system to respond to uncertain disruptions/attacks, and provide a real-time dynamic resilience profile to support decision-making.

Dynamic risk assessment of chemical process systems using the System-Theoretic accident model and process approach (STAMP) in combination with cascading failure propagation model (CFPM)

To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.

Cascading failure in urban rail transit network considering demand variation and time delay

To study the cascading failure in real-world urban rail transit network, the cascading failure model is proposed by considering two essential characteristics of demand variation and time delay, which has rarely been considered in most existing studies. Specifically, the model utilizes a simulation-based approach where passengers in the rail transit network are abstracted as packets moving through the network. The movement of these packets, including their response to station failures, is planned to model the travel behavior of passengers. The Shanghai rail transit network is chosen as the case study for this research. Comparative experiments suggest that the proposed model may produce very different results for stations with high closeness and enough travel demand. The proposed model can depict the dynamic failure process and yield a longer and more dynamic failure process, which is beneficial for managers to identify the period for prevention. Besides, sensitivity analysis shows that higher travel demand is more likely to cause a cascading failure, while time delay slightly impacts the final cascade size but affects propagation duration. In summary, travel demand plays a vital role in cascading failures, and the presence of time delays complicates the cascading failure propagation process. The research results provide references and support for studying cascading failure in real complex environments.

Percolation behavior analysis of weighted edge-coupled interdependent networks

The robustness of interdependent networks has received extensive attention from the inter-disciplinary fields. In reality, many interdependent systems have interdependence relationships not only between nodes but also between edges. Different from previous studies focusing on edge-coupled interdependent networks without consideration on coupling strength, in this paper, a weighted edge-coupled interdependent network model with coupling strength is proposed. Then, the cascading failure propagation mechanism caused by attacking a small percentage of edges is studied. Based on the self-consistent probability theory, theoretical framework is established to provide the mathematical analysis of robustness of weighted edge-coupled interdependent networks under edge attacks. The current results can help to understand the vulnerability of interdependent networks and provide useful suggestions for the protection and optimization of complex networked systems.

Cascading Failure Propagation and Mitigation Strategies in Power Systems

In this article, we investigate the local and nonlocal failure propagation patterns in power systems and propose effective mitigation strategies. It is widely acknowledged that the sequence of failure events of component overloading is determined by power flow redistribution. To understand the power flow redistribution process, we analyze the prefault power flow condition using network flow theory and by clustering the system into several regions with balanced local power flow. We observe that faults triggering local power imbalance would lead to remote power transfer and faults causing no local power imbalance would lead to local power redistribution. The improved understanding of the failure process permits us to develop effective mitigating strategies consisting of adaptive power balance restoration and selective edge protection for cascading failure propagation. In addition, we propose a set of indexes to measure the local and nonlocal propagation risks of cascading failures. Numerical simulations on the IEEE 118- and 300-bus systems demonstrate that the proposed mitigation strategies can significantly reduce the blackout sizes and are tolerant to incorrect transmission line tripping.

A Novel Time Evolution-Based Cascading Failure Model Considering Auto-Reclosers

The auto-recloser (AR) contributes to the service restoration of the transmission lines tripped by overload and relay protection mal-operation, which may complicate cascading outage processes. This brief develops a novel model to investigate the impact of ARs on cascading failure (CF) propagation in power systems with the representation of temporal evolution. The cascading outage processes with and without ARs are first compared in a timeline manner to highlight the function of ARs. Then, combined with the inverse time and hidden failure characteristics of overcurrent protections, the CF model considering ARs is established to elaborate the cascading propagation and reveal the reclosing effect. Finally, the time-constrained optimal dispatching strategy is proposed to block CFs in time before the next line excision. Case studies on IEEE 118-bus and IEEE 300-bus systems demonstrate the feasibility and effectiveness of the proposed method.

System reliability under cascading failure models with different load effect modes

AbstractCascading failure is a phenomenon that minor disturbances trigger a chain reaction failure of the system, causing the disaster to quickly spread in the system and causing part or all of the system to collapse. Traditional cascading models that based on the assumption that the propagation time of cascading failures can be ignored are inadequate for modeling and analyzing all the practical cascading failure systems. It is necessary to develop a general methodology for the cascading failure systems with cascading failure propagation speed and different load effects. This paper derives new models which extend the load effect mode and consider the propagation speed of cascading failure. In the models, the time of the cascading failure propagation is considered. Three load effect modes are proposed that consider the number of remaining working components, the influence range of the failed parts, and the distance between the failed parts and the unfailed components. Additionally, the reliability of the systems with different types of cascading failures is analyzed, and formulas computing reliability indexes are derived. Numerical examples are provided to demonstrate the application of the proposed methodology.

Robustness of networks with dependency groups considering fluctuating loads and recovery behaviors

Dependency groups describe different characteristics of interactions among nodes, which provide an emerging way to explore the dynamical behaviors of complex networks. To study the effects of dependency groups on the robustness of flow networks, this paper proposes a cascading failure model of networks which combines fluctuating loads and dependency groups. Different from the previous hypothesis that the dependency group is completely invalid once one node in the same dependency group fails, in this paper, the failure and recovery mechanism of dependency groups under certain rules to prevent catastrophic collapses of networks is proposed. To describe the resistance of network to damage caused by cascading failures, we introduce the overload coefficient to characterize the overload state when the node handles excess loads. Considering the network cost should be controlled within a reasonable range while improving network robustness, the cost index based on the relationship between the load and capacity of the node is established. By theoretically analyzing the network cost, the relationship between the network robustness and network cost is discussed when the network cascading process happens. The proposed model is employed to study the dynamics of cascading failures evolution in BA network, ER network and two actual networks. Simulation results reveal the effects of traffic flows and dependency groups on the dynamic loads propagation of cascading failures.

A Method for Variance-Based Sensitivity Analysis of Cascading Failures

Cascading failures of relay operations in power systems are inherently linked with the propagation of wide-area power system blackouts. In this paper, we consider a power system cascading failure as an indicator matrix encoding: what power system relays operated within a cascading failure inherently capturing the component and the sequence of tripping events. We propose that this matrix may then be used with extended forms of variance-based sensitivity estimators to quantitatively rank how sensitive observed power system cascading failures are to power system variables, considering overall system cascading failures as well as cascading failures grouped by network area and relay types. We demonstrate our proposed method by investigating the sensitivity of cascading failures to relay parameters, system conditions, and fault location using a version of the IEEE 39 bus model modified to include protection relays, wind farms, and tap-changing transformers. Input power system variables included: system operational scenario, disturbance location, relay parameters or thresholds. The Case Studies' results confirm the method's utility by successfully generating relative rankings of input variables' importance with respect to cascading failure propagation. The results also show cascading failures' sensitivity to input variables to be high due to non-linear relationships between input variables and cascading failures.

Local Evolution Model of the Communication Network for Reducing Outage Risk of Power Cyber-Physical System

The deep integration of power grids and communication networks is the basis for realizing the complete observability and controllability of power grids. The communication node or link is always built according to the physical nodes. This step is alternatively known as “designing with the same power tower”. However, the communication networks do not form a “one-to-one correspondence” relationship with the power physical network. The existing theory cannot be applied to guide the practical power grid planning. In this paper, a local evolution model of a communication network based on the physical power grid topology is proposed in terms of reconnection probabilities. Firstly, the construction and upgrading of information nodes and links are modeled by the reconnection probabilities. Then, the power flow entropy is employed to identify whether the power cyber-physical system (CPS) is at the self-organized state, indicating the high probability of cascading failures. In addition, on the basis of the cascading failure propagation model of the partially dependent power CPS, operation reliabilities of the power CPS are compared with different reconnection probabilities using the cumulative probability of load loss as the reliable index. In the end, a practical provincial power grid is analyzed as an example. It is shown that the ability of the power CPS to resist cascading failures can be improved by the local growth evolution model of the communication networks. The ability is greater when the probability of reconnection is p = 0.06. By updating or constructing new links, the change in power flow entropy can be effectively reduced.

A Non-Scheduled Multi-Stage Decision-Making Approach to Control Cascading Failures

The control research on cascading failures is critical to ensure the reliability of power supply. A path-driven multi-stage corrective control model for the whole process of cascading failures is established to eliminate the risk of cascading failure. For the cascading failure process caused by overloading, the selection criterion for subsequent outage is defined according to the mechanism of propagation of cascading failures. The path-driven constraints and power relaxation constraints are extracted based on the selection criterion for a subsequent outage. A model coupled with non-scheduled multi-stage decision-making is designed by considering the flexibility of control action implementation to optimize the sum of control cost and load-shedding risk for elimination of cascading failures. The verification results show that the proposed method can reduce the probability of outages of tripped branches and successfully eliminate cascading failure.

Identification of vulnerable branches considering spatiotemporal characteristics of cascading failure propagation

A method of identification of a vulnerable branch considering the propagation characteristics of cascading failures is proposed. To determine vulnerable components that trigger or expand the propagation of cascading failures, a fault chain model of a power system is established by selecting the branch with maximum outage probability among all branches as the outage branch at the next step in a cascading failure process. The cascading failure process set can be compressed into a directed and weighted cascading failure graph by merging the repeated components in different cascading failure processes and accumulating the corresponding weight values between components: the cascading failure space–time graph can be generated according to the power grid partition. In terms of vulnerability cause and whether branch can promote the cascading failure process to spread across regions, vulnerable branches are classified and identified. The verification results show that the proposed method can accurately identify the vulnerable branches that trigger or expand the propagation of cascading failures and distinguish factors inducing different cascading steps.

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

The material transportation capacity under emergency conditions is an important guarantee for the country to deal with war, epidemic outbreak, and other crisis situations. Under emergency conditions, some nodes of the material transportation system may fail to work normally, which may lead to the collapse of the whole system. Based on analyzing the characteristics of the material emergency transportation system, this article builds a three-layer interdependent network model and uses the improved M-L model to describe the failure propagation mechanism of node damage in the three-layer network. Then, the network model is attacked randomly, and the relationship between invulnerability of the three-layer network and the three main indexes of network flow, average degree, and probability of interdependence are studied. Afterwards, the propagation of cascading failure among three subnetworks in the interdependent network is analyzed and compared. This article provides a theoretical basis for building an efficient and robust material emergency transportation system.

Assessing the Robustness of Cyber-Physical Power Systems by Considering Wide-Area Protection Functions

With the deepening deployment of information and communication technologies (ICT), the cyber network is playing an increasingly important role in determining the performance of a power system. In this paper, we assess the robustness of cyber-physical power systems and examine various impact factors on the system’s robustness. A model that integrates the operating characteristics of the physical network and the wide-area protection functions provided by the cyber network is proposed. Based on the model, cascading failure propagation processes in a cyber-physical power system triggered by initial failures are simulated. Two statistical metrics, the power outage risk and the cumulative power outage size distribution, are then used to quantify the robustness by processing numerous cascading failure simulation results. We conduct case studies on IEEE 57 Bus, IEEE 118 Bus, IEEE 145 Bus, and UIUC 300 Bus with the proposed method. Simulation results demonstrate the necessity to consider cyber coupling, the importance of developing advanced wide-area protection algorithms, and the threat posed by cyberattacks compromising the robustness of cyber-coupled power systems.

Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition

Reducing power grid cascading failure propagation by minimizing algebraic connectivity in edge addition