Cascading Failures Research Articles

Robustness analysis of large scientific facilities development network with different cascading failure modes

Developing a large scientific facility involves numerous sub-products and research organizations, which pose systematic risks of potential global cascading failure. The development process can be abstracted as an interdependent network, the Large Scientific Facility Development (LSFD) network, consisting of sub-networks of product and organization. This paper offers a comprehensive risk cascading model by integrating the Load-capacity and Epidemic models. It explores the risk cascading mechanism and robustness of the LSFD network under diverse assumption conditions. The results show that the robustness is controlled by network structure, coupling patterns, attack strategy, node load & capacity, and probability of infected & removed. Enlarging the network size leads to a decrease in robustness, and increasing the average degree of the product network can improve the robustness. The assortative coupled networks exhibit the worst robustness when encountering an intention attack. Moreover, adjusting capacity parameters can significantly impact cascade failures, highlighting the inevitability of risk cascade even with sufficient resistance capacity. The removed probability λ determines whether the network faces nearly collapse: if λ ≥ 0.5, over 90 % of nodes will fail. These simulation results provide practical implications for managing systematic risks and enhancing the robustness of the LSFD network.

Decentralized solutions for island states: Enhancing energy resilience through renewable technologies

Decentralized grid solutions could be a feasible alternative to improve resilience and mitigate cascading effects in island states. Our study explores approaches that reduce the risk of infrastructure failures and promote decentralized utility planning in islands. A novel framework is proposed to conduct a power system resilience assessment by integrating vulnerability assessments and energy system modelling approaches through network analysis. The framework is applied to an island context, where vulnerability to hydroclimatic hazards, geographic isolation, restricted access to energy sources, small population bases inadequate for substantial infrastructure investments, dependence on imported energy, lack of energy source diversification, and fragile ecosystems have exacerbated energy insecurity. As a case study, we have applied the framework to Cuba. We simulate disruptions in vulnerable network nodes in Cuba to determine the municipalities that are most impacted by the simulated cascading failures. We designed and optimized the lowest cost decentralized solutions to increase resilience either by acting as the baseload electricity source or as a complementary backup system to complement in case of a power outage. Then, the resilience of the designed system was assessed using power system resilience metrics. The study results show Regla municipality in Cuba as the most vulnerable hotspot for electricity distribution. Upon the different system comparisons, ancillary systems outperform backup systems in enhancing power system resilience, especially in the context of a disruptive event, supplying up to 53 MWh/day more, although they have higher investment costs. Based on this research, resource planners and policymakers can understand vulnerable node points and prioritize the necessary investments for the preferred system choice to alleviate impacts of energy insecurity on the Island States.

Mitigating cascading failure in power grids with deep reinforcement learning-based remedial actions

Power grids are susceptible to cascading failure, which can have detrimental consequences for modern society. Remedial actions, such as proactive islanding, generator tripping, and load shedding, offer viable solutions to mitigate cascading failure in power grids. The success of applying these solutions lies in the timeliness and the appropriate choice of actions during the rapid propagation process of cascading failure. In this paper, we introduce an intelligent method that leverages deep reinforcement learning to generate adequate remedial actions in real time. A simulation model of cascading failure is first presented, which combines power flow distribution and the probabilistic failure mechanisms of components to accurately describe the dynamic cascading failure process. Based on this model, a Markov decision process is formulated to address the problem of deciding on the remedial actions as the failure propagates. Proximal Policy Optimization algorithm is then adapted for the training of underlying policies. Experiments are conducted on representative power test cases. Results demonstrate the out-performance of trained policy over benchmarks in both power preservation and decision times, thereby verifying its advantages in mitigating cascading failure in power grids.

Performance of Dynamic Retry Over Static Towards Resilience Nature of Microservice

Objectives: The main objective of this study is to enhance the flexibility of microservice-based cloud applications, so they can cope with transient or short-term failure conditions more effectively, providing a higher throughput of cloud applications as a result. Methods: The comparison is done between the existing or manual resilient patterns such as circuit breaker and retry with a dynamically proposed retry patterns using a microservice application. Findings: The short-term or transient failures in cloud based microservice applications can occur for many reasons. Examples such as Network glitches, service failures, timeouts of requests etc. These failures can bring down the entire application resulting in a cascading of failures. Resiliency patterns are used to protect these applications from failures. The widely used patterns are circuit breaker and retry, with static configurations. It is possible that the static configurations used in resiliency patterns might not support all types of failures. Hence, a dynamic approach has been proposed to satisfy all these transient failures of cloud. The analysis proves that the application efficiency is balanced in dynamic retry than the static configurations of resiliency pattern. Hence, performance can be increased up to 34.3%, which means the availability can be ensured, in transient failure cases. Very little research has been carried out on this resiliency pattern, and there are no standards available for this pattern. When comparing with the existing adaptive retry pattern the performance is increased up to 88.52%, while using a microservice application. Novelty: A dynamically modified resilient pattern is been proposed by incorporating additional parameters such as execution time to the existing parameters of the static retry pattern. This is an individual resilience pattern, which can perform independently when compared with other dynamic resilience patterns. Keywords: Dynamic retry, Request time, Static circuit breaker, Resilience, Static retry

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.

Enhancing the robustness of interdependent networks by positively correlating a portion of nodes

Cascading failures caused by interdependencies make modern coupled systems extremely fragile to failures. In existing network robustness enhancing methods, maximizing interlayer degree-degree correlations has been proven to be an effective way to improve the robustness of interdependent networks under random failures. Here, we propose a portion of nodes positively correlated strategy (PNC) to improve network robustness by positively correlating a portion of nodes, in which the nodes that are positively correlated are selected in descending order of degree, starting at a cutoff value. Based on percolation theory, we verify the effectiveness of PNC on different networks. And find that, when the nodes with the highest degree are preferentially correlated, i.e. the cutoff value takes the maximum degree, this strategy achieves a state-of-the-art optimization effect. In particular, for interdependent scale-free networks with power-law exponent γ that satisfies 2<γ<3 , we theoretically demonstrate that the highest degree preferentially correlated mode can maximize network robustness by changing the coupling state of the near 0 proportion of nodes. For γ = 3, such a mode can make the network turn into a second-order phase transition at the collapse point. Finally, we discuss the relationship between the robustness of optimized networks and common links in real-world networks.

Cascading failures in bipartite networks with directional support links.

We study the cascading failures in a system of two interdependent networks whose internetwork supply links are directional. We will show that, by utilizing generating function formalism, the cascading process can be modeled by a set of recursive relations. Most importantly, the functions involved in these relations are solely dependent upon the choice of the degree distribution of ingoing links. Simulation results in the limit of very large networks based on different choices of degree distributions for outgoing links, e.g., Kronecker delta, Poisson and Pareto, are indeed identical and are in excellent agreement with the theory. However, for Pareto distribution with the shape parameter 1<α<2, the convergence is slow. In general, directional networks can be more vulnerable or less vulnerable than their bidirectional counterparts. For three special settings of interdependent networks, we analytically compare their vulnerability. For practical applications it is important to predict if a system responds to the size of the initial attack continuously or if there is catastrophic collapse of the system if the attack exceeds a specific transition size. We analytically show that systems with lower average degrees are more resilient against this abrupt transition. We also establish an equivalence of this transition with the liquid-gas transition in statistical mechanics. In the last section, we derive the set of recursive relation to describe the cascading process where the initial attack is not restricted to a single network.

Numerical investigation of hydro-morphodynamic characteristics of a cascading failure of landslide dams

Numerical investigation of hydro-morphodynamic characteristics of a cascading failure of landslide dams

A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.

Cascading failure modelling in global container shipping network using mass vessel trajectory data

Port plays a key role in maintaining traffic flows and the effectiveness of global maritime logistics. However, the vulnerability of the Global Container Shipping Network (GCSN) is likely to increase when a single port interruption entails failures in cascading when ports encounter situations like congestions, labor strikes or natural disasters. Such situations require the deployment of port protection measures and adjustments of shipping schedules. This paper introduces a cascading model, which employs extensive and worldwide vessel trajectory data to comprehensively analyze the occurrence of cascading failures within a GCSN. The principles behind the cascading failure model are that port failures are simulated and the maritime traffic is redistributed and equilibrated to other routes and ports. A Motter-Lai overload model is applied, complemented by a three-level balanced redistribution of the traffic flows according to the specific roles of the disrupted ports. Overall, this favors the analysis of the GCSN's vulnerability, reliability, potential risks, and possible impacts. It enables maritime authorities and decision-makers to optimize service routes and mitigate the GCSN's vulnerability.

Cascading failures in the global financial system: A dynamical model

In this paper, we propose a dynamical model to capture cascading failures among interconnected organizations in the global financial system and develop a framework to investigate under which conditions organizations remain healthy. The contribution of this paper is threefold: i) we develop a dynamical model that describes the time evolution of the organizations' equity values given nonequilibrium initial conditions; ii) we characterize the equilibria for this model; and iii) we provide a computational method to anticipate potential propagation of failures.

Scenario mapping for critical infrastructure failure under typhoon rainfall: A dependency and causality approach

The critical infrastructure system exhibits a high degree of instability because of the frequent occurrence of extreme natural disasters. This study proposes a method for exploring the cascading failures of critical infrastructure systems under such impacts, based on dependency relationships for importance analysis and probability assessment. With this method, a cascading failure scenario graph of critical infrastructure is established and empirical research is conducted using typhoon rainfall events as a case study. The main contributions of this study are summarized as follows: it proposes a method for the causal identification of the interrelationships among critical infrastructures; it sorts and evaluates the importance and cascading failure probability of critical infrastructure by using the Interpretative Structural Modeling (ISM) and Rank-order Centroid method (ROC); and it generates scenario graphs of critical infrastructure cascading failures to visualize event states, environmental characteristics, and evolutionary paths. This study provides a new method and tool for disaster risk management of complex systems involved in critical infrastructure.

Percolation transitions in partially edge-coupled interdependent networks with different group size distributions

In many systems, from brain neural networks to epidemic transmission networks, pairwise interactions are insufficient to express complex relationships. Nodes sometimes cooperate and form groups to increase their robustness to risks, and each such group can be considered a “supernode”. Furthermore, previous studies of cascading failures in interdependent networks have typically concentrated on node coupling connections; however, in many realistic scenarios, interactions occur between the edges connecting nodes rather than between the nodes themselves. Networks of this type are called edge-coupled interdependent networks. To better reflect complex networks in the real world, in this paper, we construct a theoretical model of a two-layer partially edge-coupled interdependent network with groups, where all nodes in the same group are functionally dependent on each other. We identify several types of phase transitions, namely, discontinuous, hybrid and continuous, which are determined by the strength of the dependency and the distribution of the supernodes. We first apply our developed mathematical framework to ErdsRnyi and scale-free partially edge-coupled interdependent networks with equally sized groups to analytically and numerically calculate the phase transition thresholds and the critical dependency strengths that distinguish different types of transitions. We then investigate the influence of the group size distribution on cascading failures by presenting examples of two different heterogeneous group size distributions. Our theoretical predictions and numerical findings are in close agreement, demonstrating that decreasing the dependency strength and increasing group size heterogeneity can increase the robustness of interdependent networks. Our results have significant implications for the design and optimization of network security and fill a knowledge gap in the robustness of partially edge-coupled interdependent networks with different group size distributions.

A Blocking Method for Overload-Dominant Cascading Failures in Power Grid Based on Source and Load Collaborative Regulation

Adjusting generator output and cutting off load can effectively solve the problem of continuous overload and disconnection of power lines caused by power flow transfer. To suppress large-scale power outages caused by overload-dominant cascading failures, a blocking method for overload-dominant cascading failures in the power grid based on source and load collaborative regulation is proposed. The shortest path algorithm was used to identify loads and generators with shorter electrical distances from overloaded lines. Under the premise of meeting the static safety constraints of the power grid, considering the regulation of generator output and the removal of interruptible loads, a multiobjective cascading failure blocking model is established with the minimum overall control cost of the system and the minimum probability coefficient of line failure. Use the NSGA-II algorithm to solve the model and obtain the optimal plan for adjusting generator output and cutting off load. Through example verification, the proposed method can effectively alleviate the phenomenon of line overload, thereby blocking the continuation of overload-dominant cascading failures.

Cascade failure modeling and resilience analysis of mine cyber physical systems under deliberate attacks

Guided by the intelligent construction of coal mines and integrating the concept of the Cyber-Physical System (CPS), a novel approach to a coal mine information-physical fusion system is proposed. This concept emerges from the advancement of intelligent mines and the growing need for enhanced safety in coal mine operations. From a system safety perspective, this approach aims to effectively evaluate security issues by better integrating the informational and physical layers of intelligent mines. Currently, research in areas such as power grids and water networks has widely adopted concepts of cascade failures and robustness. It is suggested that the CPS framework be adapted to the mining sector. Considering the critical role of coal mines in China's energy security and their vulnerability to cyber attacks, a mine-specific CPS system is designed. This system incorporates a model of mine information-physical fusion, including networks for mine operations, management, and control, tailored to the unique conditions of mine shafts and underground settings. Utilizing Markov process theory, the paper delves into the theoretical study of the cascade failure process under deliberate attack strategies. This analysis is essential to effectively assess the safety issues within the information-physical system, providing vital support for fault prediction and safety analysis. The model of cascading failures in the mine information-physical system is developed, and based on an analysis of resilience and robustness, the study identifies key factors influencing the security of mine information-physical systems. Critical factors affecting the reliability of these systems include the number of system nodes, node coupling, edge count, and the interconnection methods of the dual-layer dependency network. The paper concludes by discussing the challenges in analyzing the security of information-physical systems and suggesting directions for future research.

Numerical and theoretical analysis of multi-pillar instability under elastic beams

With the increase of mining scale and depth, the cascading pillar failure (CPF) disaster has gradually become one of the core technical challenges for safe mining. This paper studies the CPF disaster of multi-pillar (P1-2, P2-3, P3-4 and P4-5) at 848 m level of Alhada Lead-Zinc Mine based on the RFPA2D numerical simulation software and stiffness theory. The numerical simulation results indicate that the load transfer effect induces the domino instabilities of double pillar (P2-3 and P3-4) and double pillar (P1-2 and P4-5), which is characterized by the ‘2 + 2’ compound failure mode. The physical essence of load transfer effect is revealed through numerical simulation, which is the elastic rebound of surrounding rockmass (such as roof-floor). In addition, the theoretical model of rebound overload mechanism of roof-multi-pillar-floor system is established based on the catastrophe theory, and the instability criterion, sudden jump and energy release of roof-multi-pillar-floor system are derived. Finally, the main influencing factors of load transfer effect such as pillar spacing and damage fracture zone are quantitatively analyzed, and the load transfer law shows that the load transfer effect decreases with the increasing pillar spacing and is hindered by the structural plane in the surrounding rockmass.

The effect of strategic alliances on supply chain resilience under external shocks

ABSTRACT Enterprises opt for alliances to bolster supply chain resilience (SCR) against external shocks. Yet, studies on the impact of strategic alliances on SCR from an operations management perspective are still lacking. This study develops a supply chain network (SCN) cascading failure model that accommodates the presence of strategic alliances, drawing upon complex network theory and cascading failure theory. Using numerical simulations and empirical examples, the study delves into how enterprises can effectively enhance their SCR by forming strategic alliances and appropriately allocating redundant resources. The findings reveal that: (I) Strategic alliances enhance SCR; (II) When enterprises maintain a degree of redundancy, the strengthening effect of alliances on SCR is even more pronounced, particularly under significant external shocks; and (III) In the context of cascading failures, disruptions to smaller enterprises have a more pronounced effect on SCR. Notably, weak-weak alliances contribute more to SCR than strong-strong alliances.

Resilience-based post-disaster repair strategy for integrated public transit networks

The resilience of public transit systems has become a critical issue due to their essential role in urban mobility. However, the challenges of repair selection and sequencing in integrated public transportation networks (IPTN) have not been fully explored. Therefore, we propose a bi-level resilience-based repair strategy optimisation (RRSO) model to investigate the optimisation of resilience in IPTN. To consider trip selection and account for cascade failure processes, a lower-level traffic assignment model was integrated into a single-layer RRSO model. In the traffic assignment model, perceived travel time reflects the traffic impedance of public transportation. Additionally, to assess the performance of the RRSO model under emergency repair conditions, random variables were introduced to represent uncertain factors in the traffic environment. Finally, the results demonstrate that the RRSO model outperforms standard empirical repair strategies. This study provides theoretical insights into the management of urban public transportation.

Research on proactive defense and dynamic repair of complex networks considering cascading effects

Cascading effects can result in the nonlinear propagation of failures in complex networks, ultimately leading to network collapse. Research on the fault propagation principles, defense strategies, and repair strategies can help mitigate the effects of cascading failures. Especially, proactive defense and dynamic repair are flexible and effective methods to ensure network security. Most studies on the cascade of complex networks are based on the unprocessed initial information of the network. However, marginal nodes are a type of node that cloaks the initial information of the network. In this study, we rank the importance of nodes according to the intensity of network energy confusion after the removal of this node, clarify the meaning of marginal nodes and proposed two methods to screen marginal nodes. The results indicated that the proactive removal of marginal nodes can effectively reduce the effect of cascading failures without causing any negative disturbance to the energy flow of the network. In addition, network repair according to the proposed strategy can minimize the cascade effect in the repair process and improve repair efficiency.

Infection-induced cascading failures – impact and mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.