Cascading Failures Research Articles

Voltage-Coupled Cascading Trip-off within Permanent Magnet Synchronous Generator Based Wind Farm during Low Voltage Ride Through

Abstract With the increasing penetration of renewable energy, power electronic interfaced devices have become dominant in power system. It is necessary to investigate the dynamic process of the power grid in a smaller time scale. In case of failing in low voltage ride through (LVRT), the traditional method that treats the response of permanent magnet synchronous generator (PMSG) based wind farm as a one-time trip-off cannot accurately model the cascading failures in such a new-type power system. This paper focuses on this issue and conducts a detailed study of the dynamic process in a PMSG wind farm during LVRT. Firstly, we reveal an evolution mechanism of voltage-coupled cascading trip-offs in PMSG wind farm. Then, the wind farm based on PMSG is modeled in DIgSILENT/PowerFactory. The model takes into account the voltage losses on the power collection line inside the wind farm, and each wind turbine includes an independent control model. Finally, by simulation, we obtain the principle on cascading failures of internal wind turbines in the wind farm, and confirm the interaction mechanism between turbines as well as the detailed process of cascading failures.

Identification of Key Risk Nodes and Invulnerability Analysis of Construction Supply Chain Networks

The construction supply chain confronts interruption risks that raise significant concerns regarding industry safety and stability. Consequently, exploring risk management strategies from both enterprise and supply chain network perspectives is crucial. This study employs complex network theory and the cascade failure model to propose a methodology tailored to the unique characteristics of the construction supply chain, facilitating the identification of key risk nodes and the conduct of invulnerability analyses. By evaluating the importance of construction enterprise nodes and their risk propagation ability during cascade failures, this method enables the comprehensive identification of key risk node enterprises within the construction supply chain network. Furthermore, this study examines and discusses strategies for enhancing network invulnerability by taking into account node capacity, load, and resilience. Empirical results indicate that the key nodes and risk nodes in the construction supply chain network are mainly located upstream and downstream, displaying specific distribution patterns. In addition to core enterprises, key risk nodes comprise some strong suppliers at the intermediary and lower tiers of the supply chain. Adjustments to node enterprise parameters like capacity, load, and resilience have diverse impacts on the invulnerability of the construction supply chain network. This study clarifies the distribution patterns of key risk nodes within the construction supply chain network and the variations in network invulnerability under particular conditions, providing valuable insights for risk management decision-making.

Link interaction for K-terminal network cascading failures subject to saturating branching process

When improving a multi-component system reliability, we often replaced or maintained a component while ignoring the reliability of another component due to the budget cost constraint. Thus, the interaction effects regarding system reliability between two components should be analyzed. However, conventional research efforts concerning component interactions have two gaps: (i) they are unsuitable for systems with complex structures, such as K-terminal network (for short network), only pertaining to multi-component systems with simple structures; and (ii) they fail to consider component interactions in the context of cascading failure. To fill these gaps, this paper analyzes the effects of link interactions regarding network reliability in the context of link cascading failures. Under this context, we characterize the total number of link failures by a saturating branching process, based on which a network reliability model is established. According to the reliability model, we introduce the joint reliability importance (JRI) measure of two links to analyze link interactions. To evaluate the JRI, we derive several equations and construct a numerical algorithm from the theoretical and numerical standpoints, respectively. Theoretically, we find the conditions under which the scale and sign of the JRI is decided, so that the extent and type of interactions for two links can be analyzed. Numerically, an experiment is conducted to demonstrate how to apply JRI to evaluate link interactions in the circumstance of link cascading failures, which further confirms the theoretical analysis.

Cascade Failure-Based Identification and Resilience of Critical Nodes in Automotive Supply Chain Networks

In the case of cascade failure, due to the close connection of the automobile supply chain network, the chain reaction caused by it should not be ignored; therefore, to find out the important nodes in the automobile supply chain network, to reduce the damage of cascade failure on the supply chain network, and to improve the destruction resistance of the automobile supply chain network is a problem that we should focus on. This paper takes Tesla’s new energy automotive supply chain network as an example to study the impact of cascade failure on the destructive resistance of the automotive supply chain network. From the analysis of the identification results, it is found that the key nodes in the automobile supply chain network with strong influence on risk propagation are mostly charging pile enterprises, motor enterprises, and electronic control enterprises at the core, such as Hengdian Electromagnetics, Wanma Stocks, etc. Meanwhile, Changxin Science and Technology, as a central control panel manufacturer with a large number of indirect suppliers, is also in the top position. Through the proposed key node identification method, it has good practical application value for preventing risk transmission in the automotive supply chain.

Multi Organ Failure (MOF) and a Promising Novel Device

Acutely ill hospitalized patients are at risk of cascading failure of vital organs leading to prolonged stay in the hospital, persistent dysfunction of vital organs and higher probability of death. It is estimated that 50 to 75% of ICU patients have failure of one or more vital organs and as more organs are involved the risk of death increases [1, 2]. For example in Acute on Chronic Liver Failure (AOCLF) the 28 day probability of death increases from 20% with one vital organ failure to 90% or higher if 3 or more organs are involved. Dysfunction of brain, lungs, heart and kidneys are most commonly seen, kidney involvement is usually poor prognostic sign.

Adaptive Scheduling Method for Passenger Service Resources in a Terminal

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method’s effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced.

Cascading blackout severity prediction with statistically-augmented graph neural networks

Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Traditional power-flow-based tools for assessing cascading blackout risk are too slow to properly explore the space of possible failures and load/generation patterns. We add to the growing literature of faster graph-neural-network (GNN)-based techniques, developing two novel techniques for the estimation of blackout magnitude from initial grid conditions. First we propose several methods for employing an initial classification step to filter out safe ‘non-blackout’ scenarios prior to magnitude estimation. Second, using insights from the statistical properties of cascading blackouts, we propose a method for facilitating non-local message-passing in our GNN models. We validate these two approaches on a large simulated dataset, and show the potential of both to increase blackout size estimation performance.

Control of cascading failures using protective measures

Cascading failures, triggered by a local perturbation, can be catastrophic and cause irreparable damages in a wide area. Hence, blocking the devastating cascades is an important issue in real world networks. One of the ways to control the cascade is to use protective me‌asures, so that the agents decide to be protected against failure. Here, we consider a coevolution of the linear threshold mo‌del for the spread of cascading failures and a decision-making game based on the perceived risk of failure. Protected agents are less vulnerable to failure and in return the size of the cascade affects the agent’s decision to get insured. We find at what range of protection efficiency and cost of failure, the global cascades stop. Also we observe that in some range of protection efficiency, a bistable region emerges for the size of cascade and the prevalence of protected agents. Moreover, we show how savings or the ability of agents to repair can prevent cascades from occurring.

Relatively important node identification for cyber–physical power systems based on relatively weighted entropy

In recent years, a large number of scholars have paid attention to the identification of vital nodes of cyber–physical power systems. However, in addition to these vital nodes, other relatively important nodes in the system have important research significance. For example, when a vital node in the system has been damaged, avoiding further damage is particularly important. We can obtain useful information from these vital nodes and find out which nodes are relatively important for these nodes to protect, which can prevent further cascading of failures to some extent. In this paper, we aim to identify the relatively important nodes and define the connection tightness index of any two nodes. By combining these indexes with relative entropy, we can correctly measure the degree of difference between any two nodes and sort them to determine the relative importance of nodes. The results show that the proposed strategy can evaluate relatively important nodes without distinguishing between power network and communication network. It is also possible to identify nodes that are relatively important to the network on one side of the cyber–physical power system by the importance of the network nodes on the other side, and this is superior to other strategies.

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

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.

Investment planning framework for mitigating cascading failures

Critical component outages can lead to widespread cascading propagation, which is however typically ignored in existing investment planning approaches. To address this gap, this paper seamlessly integrates advanced cascading failure analysis into resilient investment planning. It first deploys a stochastic simulator to generate spatiotemporal high-impact low-probability (HILP) events, which are then assessed using a cascading failure model, generating various cascading quantification metrics (CQMs). The framework explicitly quantifies tail risks (i.e., HILP events) using Conditional Value-at-Risk (CVaR) with a confidence level determined by unsupervised clustering, instead of using a predetermined confidence level. This enables the more tailored identification of a set of worst-case scenarios for the system under investigation, improving its practicality. An optimization model then utilizes the outputs of the cascading analysis and the defined CVaR confidence level to identify investment portfolios that provide a hedge against cascading failures. The proposed work is demonstrated on the IEEE 39-bus system, revealing reduced cascading propagation.

A new adaptive droop control strategy for improved power sharing accuracy and voltage restoration in a DC microgrid

In a DC Microgrid, accurate power sharing and voltage restoration are two primary control goals to guarantee power quality and reliable operation. Inaccurate power sharing is a significant concern due to discrepancies in feeder line resistance, faulty equipment, lack of monitoring and control, and nonlinear load. Moreover, inaccurate power sharing may lead to overloading of converters and cause a cascade of failures throughout the entire system. This study proposes a new adaptive droop control strategy to address these challenges. To enhance accurate power sharing, error current sharing is formulated by considering bus current and total rated current. This is regulated by the adaptive controller to adjust droop resistance. Additionally, the impact of droop voltage because of feeder line resistance is considered in the proposed strategy. The primary control loop regulates the output voltage of converter utilizing the proposed observer-based optimum sliding mode control (OOSMC). The controller gain of the OOSMC is optimized using a gradient-based method to enhance transient and steady state response of the converter output. In the secondary loop, the twin-delayed deep deterministic policy gradient (TD3) for voltage restoration is employed with a new reward function to optimally tune the proportional-integral (PI) controller. Finally, the superiority of the proposed strategy is evaluated through rigorous testing scenarios, including the use of constant power load (CPL) and sudden failure in the converters. Moreover, the proposed strategy is validated by simulations and laboratory-based experiments. The results show that the OOSMC outperforms GA and PSO while the TD3 has better performance than traditional PI. Furthermore, when the equivalent power-rated converters are implemented in the system, the proposed strategy can transfer identical power sharing of 4 W to the load and provide accurate current sharing of 0.167 A compared to conventional droop control while the DC bus voltage is maintained at the 24 V reference value. In the case of different power-rated converters, the proposed strategy can achieve power sharing accuracy. The first, second and third converters supply 2 W, 4 W, and 6 W, respectively, to the load and provide accurate current sharing. Meanwhile the DC bus voltage can be kept at the reference voltage of 24 V. In the scenario of various kinds of disturbances, the proposed strategy can achieve accurate power and current sharing when the system is tested under load and input voltage variations. Meanwhile the DC bus voltage always returns to the voltage reference. Moreover, the proposed strategy can share power and current accurately when the converter occurs a sudden failure.

Research on defense strategies for power system frequency stability under false data injection attacks

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF, and improve the stability of LREPS.

Identification of vulnerable lines in power grids with wind power integration based on topological potential

Vulnerable transmission lines are one of the weak links in the operation of the power system, as their damage can trigger widespread blackouts and cascading failures. The integration of large-scale wind power generation into the grid has resulted in significant changes to both the topology and distribution patterns of power flows inside the system, exacerbating line vulnerabilities. Identifying these lines can enhance power system safety through vigilant monitoring or augmenting their carrying capacity. In this paper, a method for identifying vulnerable lines based on the theory of topological potential is proposed. The method is based on the N-1 fault model of optimal DC power flow, and a corresponding network is constructed for the power system. The task of pinpointing weak points in a complicated network is converted into an assessment problem of important nodes. The proposed index comprehensively considers the in-degree and out-degree topological potentials of nodes, and uses the entropy weight method for weight allocation. Meanwhile, the TLS distribution is used to fit the wind power prediction error. Finally, simulation experiments are conducted on the IEEE 39-bus system. Simulation results indicate that the most vulnerable transmission lines tend to be located at the hubs of the system’s topological network and in close proximity to wind farms.

Impact of land-use change on ecological vulnerability in the Yellow River Basin based on a complex network model

The Yellow River Basin in China, which is characterized by significant soil erosion and arid to semi-arid conditions, has experienced severe ecological issues due to prolonged excessive development and land-use changes. Studies on ecosystem vulnerability have become a major basis for supporting the sustainable and healthy development of watershed ecosystems. Based on the land-use data of 2000, 2005, 2010, 2015, and 2020, this study used a complex network model to identify the key types of land-use transformation in the Yellow River Basin. The cascade failure model was used to evaluate the impact of land-use transformation on ecological vulnerability in the Yellow River Basin of China. The land-use changes in the Yellow River Basin from 2000 to 2020 exhibit significant heterogeneity in both quantity and direction. Among them, the total area of agricultural land showed a downward trend as a whole, and the area rebounded slightly from 2000 to 2015. The growth trend of construction land area is obvious, with a year-on-year increase of 46.81 % in 20 years. Through network identification, we found that ecological land types such as forest, grassland, and dryland are the primary factors contributing to land area outflow. Conversely, artificial land types including urban land, rural residential land, and other built-up land predominantly serve as factors for land area inflow. The Yellow River Basin experiences frequent land-use transitions, resulting in the poor stability of the land transfer network. However, the study reveals that the ecosystem service value increases as the areas of paddy fields, dryland, urban land, rural residential land, and bare land decrease; this trend contributes to mitigating ecological vulnerability. To ensure the overall stability of the ecosystem and implement healthy and sustainable development, the maximum reduction in the areas of dryland, forest, paddy, and grass should be limited to 40 %, 30 %, 50 %, and 50 %, respectively, in future planning and design processes. In conclusion, the land-use transformation in the Yellow River Basin of China has profound implications for ecological vulnerability. The research findings can provide valuable references for future ecological research on areas facing similar challenges in protecting ecological fragility.

Effect of interlayer dependence strength on continuous to discontinuous transitions in multi-layer networks

Studies on multilayer networks have aroused great interest in recent years because they allow a deeper description of the complexity present in many real systems. In this study, we introduce a dependency strength controller into multilayer interdependent networks, refining the traditional models that often inadequately represent cascading failures in real-world systems like urban transportation networks. This refined model enables a more realistic depiction of failure dynamics, where a node’s failure leads to its dependent node’s failure with a probability λ or survival with 1−λ. We derive exact equations that describe this process in a multilayer network and its structural evolution, and solve them numerically. We found the existence of a critical transition from continuous to discontinuous percolation as the strength of the dependence between layers increases. The critical percolation thresholds for these transitions are numerically derived for two-layer Erdős-Rényi networks and two-layer scale-free networks. This enhanced understanding of complex systems, incorporating initial disturbances and varying dependency strengths, contributes to designing more resilient multilayer complex systems.

Robustness of prefabricated construction supply chain network against underload cascading failure

Robustness of prefabricated construction supply chain network against underload cascading failure

Spread of parking difficulty in urban environments: A parking network perspective

AbstractSpread of parking difficulty can be regarded as a special cascading failure process of urban parking systems. A comprehensive understanding of this process can be greatly helpful to build a more robust parking system. Parking network, a specified complex network, is proposed to model, simulate, and analyse the failure process of urban parking systems in this paper. This model is applied to the analysis of parking systems in an abstract city grid and the downtown area of Luohu, Shenzhen. The results demonstrate that the parking network can capture subtle variations among various parking cruising behaviours or strategies from a network perspective. To enhance the utility of the parking network, an auxiliary indicator named “Parking Difficulty Index” is introduced to help assess the failure degree of urban parking system, estimate the optimal timing for parking guidance intervention, and evaluate the effectiveness of various guidance strategies in mitigating parking difficulties.

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.