Prevention Of Cascading Failures Research Articles

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.

A Double‐Layer Optimization Method for Cascading Failure Prevention Considering Transient Stability

In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double‐layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm.

Design ice and wind on overhead transmission lines in British Columbia

This paper describes BC Hydro's current design practice for determining ice and wind loads on overhead transmission lines based on the reliability-based design principles. Following important issues are discussed and clarified: selection of design reliability levels, prevention of cascading failures, determination of IceWind load case, inclusion of IceOnly load case, consideration of un-equal ice and broken wire load cases, consideration of wet snow and galloping. The BC Hydro standard practice has been developed based on the industry's best practice and BC Hydro's own successful experiences in the past. This paper may serve as a good reference for other utilities in designing their transmission lines.

Robustness of interdependent scale-free networks based on link addition strategies

It is well known that interdependent networks are more vulnerable to cascading failure than single and isolated networks. In this report, we propose a new scheme to improve the robustness of interdependent scale-free network under degree-based deliberate attacks by adding links to enhance the connectivity of the interdependent network. Our proposal details 14 link addition strategies using two link importance functions. To verify the feasibility of the proposed strategies, we synthesize three different types of two-layer interdependent Barabási–Albert networks where nodes from each layer are, after ranked by their degrees, bijectively inter-coupled by assortative coupling (AC), disassortative coupling (DC), or random coupling (RC). We find that when the number of attacked nodes in the system is small, the harmonic closeness (HS-IDD) link addition strategy has the highest efficiency. Among them, S indicates that the information fusion method of each layer of the network is addition, IDD refers to the degree difference of network. With the increase of the attack proportion, the degree (DS-IDD) link addition efficiency gradually increases, and the effect is more obvious in DC and RC. Besides, through comparing different strategies, under DC and RC, the link addition strategy for the product is more effective than the sum-based strategy. However, under AC, this phenomenon is not obvious. The results show that our proposed approach can enhance the robustness of interdependent networks, and that our method provides a valuable reference for the control and prevention of cascading failures in existing interdependent networks.

Compressible battery foams to prevent cascading thermal runaway in Li-ion pouch batteries

Lithium-ion battery packs require thermal management to achieve optimum life and safety. This is becoming crucial for battery packs composed of high-energy-density cells. Pouch cells themselves achieve highest packaging efficiency but require additional structural support and thermal management when grouped into modules, especially under abusive conditions such as thermal runaway. Novel foam battery pads have demonstrated to cushion volume changes of pouch cells and are reengineered in this study to mitigate cell-to-cell thermal runaway propagation. The compressible pads are made of polyurethane foams incorporating flame-retardant additives or coatings, including intumescent and fire wall materials. Their performances were evaluated by conducting nail penetration tests on modules composed of pouch cells at 100% state of charge (SOC), with the foams placed in between the cells. Experimental results show cascading thermal runaway was considerably delayed by polyurethane foams incorporating flame-retardant additives or coatings. Complete prevention of cascading failure was achieved with dense polyurethane foams with multilayered coatings of both fire wall and intumescent materials.

Probabilistic Vulnerability Assessment of Transmission Lines Considering Cascading Failures

The prevention of cascading failures and large-scale power outages of power grids by identifying weak links has become one of the key topics in power systems research. In this paper, a vulnerability radius index is proposed to identify the initial fault, and a fault chain model of cascading failure is developed with probabilistic attributes to identify the set of fault chains that have a significant impact on the safe and stable operation of power grids. On this basis, a method for evaluating the vulnerability of transmission lines based on a multi-criteria decision analysis is proposed, which can quickly identify critical transmission lines in the process of cascading failure. Finally, the proposed model and method for identifying vulnerable lines during the cascading failure process is demonstrated on the IEEE-118 bus system.

Protection strategies of active defense in cyber-physical power systems

Cascading failure triggered by tiny failure iteratively propagates between physical power grid and communication network, causing severe damage on cyber-physical power systems. In this paper, we study the protection strategies of active defense to improve the robustness of systems. Based on analysis of cascading failure in systems, we classify failure into three types, structural failure, overload failure, and interdependent failure. Due to the severe damage caused by overload failure, we propose four protection strategies of active defense, through interdicting propagation path of cascading failure, to prevent overload failure. Compared with traditional redundancy protection and structure optimization, the protection strategies are economical and efficient, and can defend from an unknown attack. In simulation analysis, the protection strategies of active defense are proved to be effective in prevention of cascading failure.

Research on a Power Grid Cascading Failure Prevention and Control Method considering WSN

The practical application of wireless sensor networks (WSNs) in hot fields is summarized. It is found that compared with traditional monitoring methods, it has better adaptability to complex environments and low cost. It is suitable for monitoring power grid operation parameters. Therefore, this paper combines the above network and cascading failures, analyzes its 24‐hour continuous and dynamic monitoring of the operation parameters of the power grid, and considers how to use the obtained parameters to analyze the disturbance of the remaining lines after the initial fault of the power grid. To prevent cascading failures in the power grid, a preventive control model considering safety and economy is proposed, and the model is solved by nondominated sorting genetic algorithm II and particle swarm optimization (NSGA2‐PSO). Finally, the rationality of this method is verified in the IEEE39 node system.

Nonlinear model of cascade failure in weighted complex networks considering overloaded edges

Considering the elasticity of the real networks, the components in the network have a redundant capacity against the load, such as power grids, traffic networks and so on. Moreover, the interaction strength between nodes is often different. This paper proposes a novel nonlinear model of cascade failure in weighted complex networks considering overloaded edges to describe the redundant capacity for edges and capture the interaction strength of nodes. We fill this gap by studying a nonlinear weighted model of cascade failure with overloaded edges over synthetic and real weighted networks. The cascading failure model is constructed for the first time according to the overload coefficient, capacity parameter, weight coefficient, and distribution coefficient. Then through theoretical analysis, the conditions for stopping failure cascades are obtained, and the analysis shows the superiority of the constructed model. Finally, the cascading invulnerability is simulated in several typical network models and the US power grid. The results show that the model is a feasible and reasonable change of weight parameters, capacity coefficient, distribution coefficient, and overload coefficient can significantly improve the destructiveness of complex networks against cascade failure. Our methodology provides an efficacious reference for the control and prevention of cascading failures in many real networks.

Overview of precaution and recovery strategies for cascading failures in multilayer networks

In real life, most of the infrastructure networks closely related to the national economy and people's livelihood do not exist independently, but are interconnected with or dependent on each other, so the multilayer network model is proposed to study the independent complex systems and infrastructures. When the nodes in the multilayer network suffer initial failure or attack, the cascade occurs due to the interaction between the “intra-layer” and “inter-layer”, and the failure can propagate in the network layer and across the layers iteratively, so that the scale of the failures is enlarged gradually. As a result, many multilayer networks are more fragile than single networks. The cascading failure of multilayer network usually brings very serious catastrophes to our society. So, conducting the research on preventing the multilayer network from cascading failure and recovering is of great significance. As far as the prevention of cascading failure is concerned, what are mainly included are the strategies such as the fault detection, the protection of important nodes, the optimization of the coupling method of networks, and the backup of nodes. As for the recovery of multi-layer network, included mainly are the strategies such as common boundary node recovery, the idle connected link recovery, the link addition, the priority recovery of important nodes, the topology perturbation, and the repairing of localized attack and adaptive link.

Eradicating abrupt collapse on single network with dependency groups.

The dependency among nodes has significant effects on the cascading failures of complex networks. Although the prevention of cascading failures on multilayered networks in which the failures of nodes in one layer affect the functioning of nodes in other layers has been widely investigated, the prevention of catastrophic cascade has rarely been addressed to single-layer networks where nodes are grouped and nodes within the same group are dependent on each other. For such networks, we find that it is already enough to prevent abrupt catastrophic collapses by randomly reinforcing a constant density of nodes. More importantly, we give the analytical solutions to the proportion of needed reinforced nodes for three typical networks, i.e., dependent Erdős-Rényi (ER), random regular (RR), and scale-free (SF) networks. Interestingly, the density of reinforced nodes is a constant 0.1756, which holds true for ER networks with group size 2 regardless of average degree, RR, and SF networks with a large average degree. Also, we find the elegant expression of the density with any group size. In addition, we find a hybrid phase transition behavior, which is present in RR and SF networks while absent in ER networks. Our findings might shed some new light on designing more resilient infrastructure networks.

Over-Voltage Suppression Methods for the MMC-VSC-HVDC Wind Farm Integration System

In this brief, we study the cascading failure mitigation methods in the system where the remote large-scale wind farm is integrated to the bulk ac power system through high-voltage direct current (HVDC) transmission based on voltage source converter (VSC) in modular multilevel converter (MMC) topology. We first analyze the over-voltage formation mechanism in the MMC-VSC-HVDC wind farm integration system after the short-circuit fault of an ac feed line, and find that the saturation of the integers in the PI controller are the main causes. Based on the analytical conclusions derived, we propose two improvement control strategies implemented in the MMC controller to suppress the over-voltage. We do simulations on a test case with PSCAD. Simulation results verify the efficacy of the proposed methods in the suppression of over-voltage as well as the prevention of cascading failure in power systems.

A mitigation strategy for the prevention of cascading trust failures in social networks

In the past decade, we have seen a massive growth in social networks and a day to day increase in their users. Loose constraints and almost no limitation in the propagation of information in these networks have resulted in a lot of false information, spam and inappropriate messages being exchanged among users. This has caused the creation of a trust crisis in which trust relationships turn into distrust. But, the real threat to the general integrity of social networks occurs when the removed trust edges in the social network result in more and more connections to turn into distrust and thus be removed from the corresponding trust network. This phenomenon is called cascading trust failure.The primary purpose of this research is to build upon the dynamic modeling of cascading failures due to the occurrence of a trust crisis within a unidirectional or bidirectional social network. After the model is created, the next step would be on the detection of neighboring edges that can be influenced by the trust crisis and may be removed because of it. The second purpose of the research is to propose a mitigation strategy for the prevention of cascading failures in trust relationships. This will be beneficial for the honest and free expression of opinions and experiences without their privacy getting compromised or the trust relationships being affected. The proposed model has four steps: (1) trust calculation and evaluation, (2) propagation, (3) updating and (4) the filtration process. In the model, important parameters such as changes in topology, cascading times and failure as well as the connectivity ratio are considered. The impact of these parameters is also investigated in the Facebook’s sparse network and Twitter’s ripple network. The performed evaluations show that the strength of trust relationships not only depend on profile similarity measures but also on the subjective, propagative, dynamic, event sensitive and the asymmetrical characteristics of trust. Also, it is shown that the sensitivity of users toward the change in their network topology is also a considerable factor in the occurrence and subsequent prevention of cascading trust failures. Based on the performed evaluations, the proposed mitigation strategy is capable of maintaining 95% and 92% of the trust relations when a trust crisis targets the highest trust values in the Facebook and Twitter’s networks respectively. This is a considerable increase from the 52% and 2% remaining edges in the occurrence of trust crisis in the previously proposed approaches. Also, the proposed approach has an average of 50% increase in reducing the number of cascading failures as well as their impact on the network.

Robustness analysis of complex networks with power decentralization strategy via flow-sensitive centrality against cascading failures

Most existing cascading failure mitigation strategy of power grids based on complex network ignores the impact of electrical characteristics on dynamic performance. In this paper, the robustness of the power grid under a power decentralization strategy is analysed through cascading failure simulation based on AC flow theory. The flow-sensitive (FS) centrality is introduced by integrating topological features and electrical properties to help determine the siting of the generation nodes. The simulation results of the IEEE-bus systems show that the flow-sensitive centrality method is a more stable and accurate approach and can enhance the robustness of the network remarkably. Through the study of the optimal flow-sensitive centrality selection for different networks, we find that the robustness of the network with obvious small-world effect depends more on contribution of the generation nodes detected by community structure, otherwise, contribution of the generation nodes with important influence on power flow is more critical. In addition, community structure plays a significant role in balancing the power flow distribution and further slowing the propagation of failures. These results are useful in power grid planning and cascading failure prevention.

Distributed monitoring for the prevention of cascading failures in operational power grids

Electrical power grids are vulnerable to cascading failures that can lead to large blackouts. The detection and prevention of cascading failures in power grids are important problems. Currently, grid operators mainly monitor the states (loading levels) of individual components in a power grid. The complex architecture of a power grid, with its many interdependencies, makes it difficult to aggregate the data provided by local components in a meaningful and timely manner. Indeed, monitoring the resilience of an operational power grid to cascading failures is a major challenge.This paper attempts to address this challenge. It presents a robustness metric based on the topology and operative state of a power grid to quantify the robustness of the grid. Also, it presents a distributed computation method with self-stabilizing properties that can be used for near real-time monitoring of grid robustness. The research thus provides insights into the resilience of a dynamic operational power grid to cascading failures during real-time in a manner that is both scalable and robust. Computations are pushed to the power grid network, making the results available at each node and enabling automated distributed control mechanisms to be implemented.

AC power flow importance measures considering multi-element failures

Quantifying the criticality of individual components of power systems is essential for overall reliability and management. This paper proposes an AC-based power flow element importance measure, while considering multi-element failures. The measure relies on a proposed AC-based cascading failure model, which captures branch overflow, bus load shedding, and branch failures, via AC power flow and optimal power flow analyses. Taking the IEEE 30, 57 and 118-bus power systems as case studies, we find that N-3 analyses are sufficient to measure the importance of a bus or branch. It is observed that for a substation bus, its importance is statistically proportional to its power demand, but this trend is not observed for power plant buses. While comparing with other reliability, functionality, and topology-based importance measures popular today, we find that a DC power flow model, although better correlated with the benchmark AC model as a whole, still fails to locate some critical elements. This is due to the focus of DC-based models on real power that ignores reactive power. The proposed importance measure is aimed to inform decision makers about key components in complex systems, while improving cascading failure prevention, system backup setting, and overall resilience.

Reinforcement learning approach for congestion management and cascading failure prevention with experimental application

This article proposes a method based on the reinforcement learning (RL) for preventing cascading failure (CF) and blackout in smart grids by acting on the output power of the generators in real-time. The proposed research work utilizes the Q-learning algorithm to train the system for the optimal action selection strategy during the state-action learning process by updating the action values based on the obtained rewards. The trained system then is able to relieve congestion of transmission lines in real-time by adjusting the output power of the generators (actions) to prevent consecutive line outages and blackout after N-1 and N-1-1 contingency conditions. The proposed RL-based control is validated through experimental implementation as well as simulation studies on the IEEE 118-bus test system for different contingency case studies. The results obtained from the experimental and simulation studies show the accuracy and robustness of the proposed approach in preventing cascading failure and blackout.

Invulnerability of power grids based on maximum flow theory

Abstract The invulnerability analysis against cascades is of great significance in evaluating the reliability of power systems. In this paper, we propose a novel cascading failure model based on the maximum flow theory to analyze the invulnerability of power grids. In the model, node initial loads are built on the feasible flows of nodes with a tunable parameter γ used to control the initial node load distribution. The simulation results show that both the invulnerability against cascades and the tolerance parameter threshold α T are affected by node load distribution greatly. As γ grows, the invulnerability shows the distinct change rules under different attack strategies and different tolerance parameters α respectively. These results are useful in power grid planning and cascading failure prevention.

Real-time intelligent control and cascading failure prevention in microgrid systems based on neural network algorithm: an experimental approach

This paper presents an intelligent control method based on artificial neural network (ANN) to prevent cascading failures and blackout in microgrid systems after N1 contingency condition. Microgrids have low inertia as compared to the utility power grids which makes their control very challenging. The main contribution of this work is to utilise the machine learning structure of ANN to prevent blackout and make microgrids more reliable and resilient. This method is able to relieve the congestion on lines by adaptive power re-dispatch to prevent consecutive line outages. The proposed ANN control approach is tested on an experimental test system. Experimental results show that the ANN approach provided accurate and robust control and management of the microgrid system by preventing a total system collapse. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN showed a faster and better response than the MAS.

Analysis on Power Grid Vulnerability Considering Cascading Failure of Branch

This paper analyzes the network features of power grid with buses’ cascading failures based on the theories and methods of complex networks. And it concludes detailed vulnerability analysis of buses in Huazhong power grid under three forms of attack (maximum load attack, maximum degree attack and random attack) .It provides technical means for the prevention of cascading failure .