Identification of High-Risk Scenarios for Cascading Failures in New Energy Power Grids Based on Deep Embedding Clustering Algorithms

At present,the proportion of new energy in the power grid is increasing,and the random fluctuations in power output increase the risk of cascading failures in the power grid.In this paper,we propose a method for identifying high-risk scenarios of interlocking faults in new energy power grids based on a deep embedding clustering(DEC)algorithm and apply it in a risk assessment of cascading failures in different operating scenarios for new energy power grids.First,considering the real-time operation status and system structure of new energy power grids,the scenario cascading failure risk indicator is established.Based on this indicator,the risk of cascading failure is calculated for the scenario set,the scenarios are clustered based on the DEC algorithm,and the scenarios with the highest indicators are selected as the significant risk scenario set.The results of simulations with an example power grid show that our method can effectively identify scenarios with a high risk of cascading failures from a large number of scenarios.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.

Cascading failures in local-world evolving networks

The local-world (LW) evolving network model shows a transition for the degree distribution between the exponential and power-law distributions, depending on the LW size. Cascading failures under intentional attacks in LW network models with different LW sizes were investigated using the cascading failures load model. We found that the LW size has a significant impact on the network's robustness against deliberate attacks. It is much easier to trigger cascading failures in LW evolving networks with a larger LW size. Therefore, to avoid cascading failures in real networks with local preferential attachment such as the Internet, the World Trade Web and the multi-agent system, the LW size should be as small as possible.

Mitigation Strategy against Cascading Failures on Social Networks

Cascading failures are common phenomena in many of real-world networks,such as power grids,Internet,transportation networks and social networks.It's worth noting that once one or a few users on a social network are unavailable for some reasons,they are more likely to influence a large portion of social network.Therefore,an effective mitigation strategy is very critical for avoiding or reducing the impact of cascading failures.In this paper,we firstly quantify the user loads and construct the processes of cascading dynamics,then elaborate the more reasonable mechanism of sharing the extra user loads with considering the features of social networks,and further propose a novel mitigation strategy on social networks against cascading failures.Based on the realworld social network datasets,we evaluate the effectiveness and efficiency of the novel mitigation strategy.The experimental results show that this mitigation strategy can reduce the impact of cascading failures effectively and maintain the network connectivity better with lower cost.These findings are very useful for rationally advertising and may be helpful for avoiding various disasters of cascading failures on many real-world networks.

Cascading failures in congested complex networks with feedback

In this article, we investigate cascading failures in complex networks by introducing a feedback. To characterize the effect of the feedback, we define a procedure that involves a self-organization of trip distribution during the process of cascading failures. For this purpose, user equilibrium with variable demand is used as an alternative way to determine the traffic flow pattern throughout the network. Under the attack, cost function dynamics are introduced to discuss edge overload in complex networks, where each edge is assigned a finite capacity （controlled by parameter α）. We find that scale-free networks without considering the effect of the feedback are expected to be very sensitive to α as compared with random networks, while this situation is largely improved after introducing the feedback.

Science Letters:Model for cascading failures in congested Internet

Cascading failures often occur in congested networks such as the Internet. A cascading failure can be described as a three-phase process： generation, diffusion, and dissipation of the congestion. In this account, we present a function that represents the extent of congestion on a given node. This approach is different from existing fimctions based on betweenness centrality. By introducing the concept of ＇delay time＇, we designate an intergradation between permanent removal and nonremoval. We also construct an evaluation fimction of network efficiency, based on congestion, which measures the damage caused by cascading failures. Finally, we investigate the effects of network structure and size, delay time, processing ability and packet generation speed on congestion propagation. Also, we uncover the relationship between the cascade dynamics and some properties of the network such as structure and size.

Recovery of coupled networks after cascading failures

With society＇s increasing dependence on critical infrastructure such as power grids and communications systems, the robustness of these systems has attracted significant attention.Failure of some nodes can trigger a cascading failure, which completely fragments the network, necessitating recovery efforts to improve robustness of complex systems. Inspired by real-world scenarios, this paper proposes repair models after two kinds of network failures, namely complete and incomplete collapse. In both models, three kinds of repair strategies are possible, including random selection（RS）, node selection based on single network node degree（SD）, and node selection based on double network node degree（DD）. We find that the node correlation in each of the two coupled networks affects repair efficiency. Numerical simulation and analysis results suggest that the repair node ratio and repair strategies may have a significant impact on the economics of the repair process. The results of this study thus provide insight into ways to improve the robustness of coupled networks after cascading failures.

Analysis of cascading failures of power cyber-physical systems considering false data injection attacks

This study considers the performance impacts of false data injection attacks on the cascading failures of a power cyber-physical system,and identifies vulnerable nodes.First,considering the monitoring and control functions of a cyber network and power flow characteristics of a power network,a power cyber-physical system model is established.Then,the influences of a false data attack on the decision-making and control processes of the cyber network communication processes are studied,and a cascading failure analysis process is proposed for the cyber-attack environment.In addition,a vulnerability evaluation index is defined from two perspectives,i.e.,the topology integrity and power network operation characteristics.Moreover,the effectiveness of a power flow betweenness assessment for vulnerable nodes in the cyberphysical environment is verified based on comparing the node power flow betweenness and vulnerability assessment index.Finally,an IEEE14-bus power network is selected for constructing a power cyber-physical system.Simulations show that both the uplink communication channel and downlink communication channel suffer from false data attacks,which affect the ability of the cyber network to suppress the propagation of cascading failures,and expand the scale of the cascading failures.The vulnerability evaluation index is calculated for each node,so as to verify the effectiveness of identifying vulnerable nodes based on the power flow betweenness.

Model for cascading failures with adaptive defense in complex networks

This paper investigates cascading failures in networks by considering interplay between the flow dynamic and the network topology, where the fluxes exchanged between a pair of nodes can be adaptively adjusted depending on the changes of the shortest path lengths between them. The simulations on both an artificially created scale-free network and the real network structure of the power grid reveal that the adaptive adjustment of the fluxes can drastically enhance the robustness of complex networks against cascading failures. Particularly, there exists an optimal region where the propagation of the cascade is significantly suppressed and the fluxes supported by the network are maximal. With this understanding, a costless strategy of defense for preventing cascade breakdown is proposed. It is shown to be more effective for suppressing the propagation of the cascade than the recent proposed strategy of defense based on the intentional removal of nodes.

Cascading failures of overload behaviors using a new coupled network model between edges

With the development of network science,the coupling between networks has become the focus of complex network research.However,previous studies mainly focused on the coupling between nodes,while ignored the coupling between edges.We propose a novel cascading failure model of two-layer networks.The model considers the different loads and capacities of edges,as well as the elastic and coupling relationship between edges.In addition,a more flexible load-capacity strategy is adopted to verify the model.The simulation results show that the model is feasible.Different networks have different behaviors for the same parameters.By changing the load parameters,capacity parameters,overload parameters,and distribution parameters reasonably,the robustness of the model can be significantly improved.

Cascading Failures in Power Grids: A Load Capacity Model with Node Centrality

Power grids,due to their lack of network redundancy and structural interdependence,are particularly vulnerable to cascading failures,a phenomenon where a few failed nodes—having their loads exceeding their capacities—can trigger a widespread collapse of all nodes.Here,we extend the cascading failure(Motter-Lai)model to a more realistic perspective,where each node’s load capacity is determined to be nonlinearly correlated with the node’s centrality.Our analysis encompasses a range of synthetic networks featuring small-world or scale-free properties,as well as real-world network configurations like the IEEE bus systems and the US power grid.We find that fine-tuning this nonlinear relationship can significantly enhance a network’s robustness against cascading failures when the network nodes are under attack.Additionally,the selection of initial nodes and the attack strategies also impact overall network robustness.Our findings offer valuable insights for improving the safety and resilience of power grids,bringing us closer to understanding cascading failures in a more realistic context.

Numerical and theoretical study of load transfer behavior during cascading pillar failure

To further study the load transfer mechanism of roofemulti-pillarefloor system during cascading pillar failure(CPF),numerical simulation and theoretical analysis were carried out to study the three CPF modes according to the previous experimental study on treble-pillar specimens,e.g.successive failure mode(SFM),domino failure mode(DFM)and compound failure mode(CFM).Based on the finite element code rock failure process analysis(RFPA^(2D)),numerical models of treble-pillar specimen with different mechanical properties were established to reproduce and verify the experimental results of the three CPF modes.Numerical results show that the elastic rebound of roofefloor system induced by pillar instability causes dynamic disturbance to adjacent pillars,resulting in sudden load increases and sudden jump displacement of adjacent pillars.The phenomena of load transfer in the roofemulti-pillarefloor system,as well as the induced accelerated damage behavior in adjacent pillars,were discovered and studied.In addition,based on the catastrophe theory and the proposed mechanical model of treble-pillar specimen edisc spring group system,a potential function that characterizes the evolution characteristics of roof emulti-pillarefloor system was established.The analytical expressions of sudden jump and energy release of treble-pillar specimenedisc spring group system of the three CPF modes were derived according to the potential function.The numerical and theoretical results show good agreement with the experimental results.This study further reveals the physical essence of load transfer during CPF of roof emulti-pillarefloor system,which provides references for mine design,construction and disaster prevention.

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

A cascading failure of landslide dams caused by strong earthquakes or torrential rains in mountainous river valleys can pose great threats to people’s lives,properties,and infrastructures.In this study,based on the three-dimensional Reynoldsaveraged Navier-Stokes equations(RANS),the renormalization group(RNG)k-εturbulence model,suspended and bed load transport equations,and the instability discriminant formula of dam breach side slope,and the explicit finite volume method(FVM),a detailed numerical simulation model for calculating the hydro-morphodynamic characteristics of cascading dam breach process has been developed.The developed numerical model can simulate the breach hydrograph and the dam breach morphology evolution during the cascading failure process of landslide dams.A model test of the breaches of two cascading landslide dams has been used as the validation case.The comparison of the calculated and measured results indicates that the breach hydrograph and the breach morphology evolution process of the upstream and downstream dams are generally consistent with each other,and the relative errors of the key breaching parameters,i.e.,the peak breach flow and the time to peak of each dam,are less than±5%.Further,the comparison of the breach hydrographs of the upstream and downstream dams shows that there is an amplification effect of the breach flood on the cascading landslide dam failures.Three key parameters,i.e.,the distance between the upstream and the downstream dams,the river channel slope,and the downstream dam height,have been used to study the flood amplification effect.The parameter sensitivity analyses show that the peak breach flow at the downstream dam decreases with increasing distance between the upstream and the downstream dams,and the downstream dam height.Further,the peak breach flow at the downstream dam first increases and then decreases with steepening of the river channel slope.When the flood caused by the upstream dam failure flows to the downstream dam,it can produce a surge wave that overtops and erodes the dam crest,resulting in a lowering of the dam crest elevation.This has an impact on the failure occurrence time and the peak breach flow of the downstream dam.The influence of the surge wave on the downstream dam failure process is related to the volume of water that overtops the dam crest and the erosion characteristics of dam material.Moreover,the cascading failure case of the Xiaogangjian and Lower Xiaogangjian landslide dams has also been used as the representative case for validating the model.In comparisons of the calculated and measured breach hydrographs and final breach morphologies,the relative errors of the key dam breaching parameters are all within±10%,which verify the rationality of the model is applicable to real-world cases.Overall,the numerical model developed in this study can provide important technical support for the risk assessment and emergency treatment of failures of cascading landslide dams.

Risk-Balanced Routing Strategy for Service Function Chains of Cyber-Physical Power System Considering Cross-Space Cascading Failure

Cyber-physical power system(CPPS)has significantly improved the operational efficiency of power systems.However,cross-space cascading failures may occur due to the coupling characteristics,which poses a great threat to the safety and reliability of CPPS,and there is an acute need to reduce the probability of these failures.Towards this end,this paper first proposes a cascading failure index to identify and quantify the importance of different information in the same class of communication services.On this basis,a joint improved risk-balanced service function chain routing strategy(SFC-RS)is proposed,which is modeled as a robust optimization problem and solved by column-and-constraint generation(C-CG)algorithm.Compared with the traditional shortest-path routing algorithm,the superiority of SFC-RS is verified in the IEEE 30-bus system.The results demonstrate that SFC-RS effectively mitigates the risk associated with information transmission in the network,enhances information transmission accessibility,and effectively limits communication disruption from becoming the cause of cross-space cascading failures.

MODELING AND SIMULATION OF THE VULNERABILITY OF INTERDEPENDENT POWER-WATER INFRASTRUCTURE NETWORKS TO CASCADING FAILURES

Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading failures from the static perspective of network topology structure. This paper develops a more realistic cascading failures model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent power-water networks. In this model, the critical tolerance threshold is originally proposed to indicate the vulnerability of network to cascading failures. In addition, some key parameters that are important to network vulnerability are identified and quantified through numerical simulation. Results show that cascading failures can be prevented when the values of tolerance parameter are above a critical tolerance threshold. Otherwise interdependent networks collapse after attacking a critical fraction of power nodes. Interdependent networks become more vulnerable with the increase in interdependence strength, which implies the importance of protecting those interconnected nodes to reduce the consequences of cascading failures. Interdependent networks are most vulnerable under high-load attack, which shows the significance of protecting high-load nodes.

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks

Analyzing the vulnerability of power systems in cascading failures is generally regarded as a challenging problem. Although existing studies can extract some critical rules, they fail to capture the complex subtleties under different operational conditions. In recent years, several deep learning methods have been applied to address this issue. However, most of the existing deep learning methods consider only the grid topology of a power system in terms of topological connections, but do not encompass a power system’s spatial information such as the electrical distance to increase the accuracy in the process of graph convolution. In this paper, we construct a novel power-weighted line graph that uses power system topology and spatial information to optimize the edge weight assignment of the line graph. Then we propose a multi-graph convolutional network(MGCN) based on a graph classification task, which preserves a power system’s spatial correlations and captures the relationships among physical components. Our model can better handle the problem with power systems that have parallel lines, where our method can maintain desirable accuracy in modeling systems with these extra topology features. To increase the interpretability of the model, we present the MGCN using layer-wise relevance propagation and quantify the contributing factors of model classification.

Research on large-scale cascading failure of power systems using synergistic effect

This paper discusses the primary causes from the point of synergistic effects to improve power system vulnerability in the power system planning and safety operation. Based on the vulnerability conception in the complex network theory the vulnerability of the power system can be evaluated by the minimum load loss rate when considering power supply ability.Consequently according to the synergistic effect theory the critical line of the power system is defined by its influence on failure set vulnerability in N-k contingencies.The cascading failure modes are proposed based on the criterion whether the acceptable load curtailment level is below a preset value.Significant conclusions are revealed by results of IEEE 39 case analysis weak points of power networks and heavy load condition are the main causes of large-scale cascading failures damaging synergistic effects can result in partial failure developed into large-scale cascading failures vulnerable lines of power systems can directly lead the partial failure to deteriorate into a large blackout while less vulnerable lines can cause a large-scale cascading failure.

Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

An LCOR model for suppressing cascading failure in weighted complex networks

Based on the relationship between capacity and load, cascading failure on weighted complex networks is investigated, and a load-capacity optimal relationship （LCOR） model is proposed in this paper. Compared with three other kinds of load- capacity linear or non-linear relationship models in model networks as well as a number of real-world weighted networks including the railway network, the airports network and the metro network, the LCOR model is shown to have the best robustness against cascading failure with less cost. Furthermore, theoretical analysis and computational method of its cost threshold are provided to validate the effectiveness of the LCOR model. The results show that the LCOR model is effective for designing real-world networks with high robustness and less cost against cascading failure.

Load-redistribution strategy based on time-varying load against cascading failure of complex network

Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.