Overload Failures Research Articles

The effects of load demand uncertainty on cascading overload failures in power systems

The effects of load demand uncertainty on cascading overload failures in power systems

Reliability of the traffic network against cascading failures with individuals acting independently or collectively

The domino-like cascading failures may deteriorate traffic network reliability for both system and users. As the microscopic origin of this dynamic process, route choice behavior should be essential for the pattern of reliability against cascading failures, but the mechanism has not been systematically investigated. To uncover this, we propose a two-level cascading failure model to observe route choice patterns during the evolution of traffic jams: (i) the upper-level depicts how cascading failures spread out in network topologies, while (ii) the lower-level models what paths travelers may select in the trade-off between user equilibrium (UE) and system optimum (SO). Given nearly-one million OD pairs on large-scale traffic networks, the influence mechanisms of four factors on two reliability indicators are explored. We find that under certain perturbations, staying at the identified critical tolerance is the most profitable for investment yet unstable for reliability. Surprisingly, phase transition patterns are found in different topologies of traffic networks, where the lattice network is the most unreliable. Network reliability is found to increase by modifying travelers’ route choices from independent UE toward collective SO, whereas, the reliability is found to decrease if travelers have stochastic perception errors of travel time or have risk preferences to probabilistic travel time. Furthermore, if the overload failures can be recovered from the failed states, a traffic network may become more reliable. Our finding may help to improve reliability management of future transportation with multiple states between UE and SO.

Electrical fire monitoring IoT framework for ancient architectural complex leveraging edge computing

SummaryElectrical fires caused by leakage, short circuit, overload, and other electrical failures occur frequently in residential areas, and failure to find and locate the fault location in time is the main reason for the evolution of circuit fault into electrical fire, especially in the ancient buildings with aging circuit facilities. Different from modern large‐scale buildings, ancient architectural complex is composed of a large number of scattered low buildings with inconsistent facility specifications and simple structure, so it does not have the conditions to deploy unified electrical lines and establish traditional large‐scale circuit monitoring system. In this study, we design an electrical fire monitoring IoT (EFM‐IoT) framework, which can realize early fire warning by monitoring the residual current of appliances and is easy to deploy in ancient buildings. EFM‐IoT adopts the data cleaning mechanism based on report form to filter redundant data, reducing computing and storage pressure in the cloud. Moreover, we analyze the energy consumption in the process of residual current and temperature data flow, and explore an effective energy‐saving mechanism for transmission. EFM‐IoT has been successfully applied in Chinese historical and cultural street—Pingjiang Road of Suzhou. Extensive evaluation and analysis results demonstrate that EFM‐IoT can effectively alleviate the data traffic and load in the cloud and reduce the response time of early warning.

An Interacting Negative Feedback Mechanism in a Coupled Extreme Weather–Humans–Infrastructure System: A Case Study of the 2021 Winter Storm in Texas

Extreme weather has long been a threat to human life and critical infrastructures. Previous studies have focused on the reliability and vulnerability of single or interdependent infrastructures under extreme weather threats. However, knowledge of the interactions between coupled real-world complex systems, especially the cascading failure process induced by external shocks, is essential, but the interactions receive less attention. Here, we took the historical winter storm of Texas that occurred in February 2021 as a case study and collected multisource data to explore the interaction between humans and the power system affected by extreme weather. A connectivity-based network was proposed to analyze the connectivity robustness and simulate the cascade of overload failures under random and malicious attacks. Results showed that this network presents higher robustness under random attacks in terms of network connectivity. However, a highly heterogeneous distribution of load was shown in this network, making it particularly vulnerable to attacks and easier to trigger cascading failures. An interacting negative feedback mechanism was discovered in this coupled extreme weather–humans–infrastructure system. Extreme weather events directly caused physical failure in infrastructures, while their impact on individuals stimulated the power demand for heat. An increase in demand further intensified the load on the power network, which induced functional failure in infrastructure systems and finally aggravated the adverse impact on people as end-users in return. This feedback loop inspired us to reconsider the relationship among natural disasters, critical infrastructure, and humans. Furthermore, even under the background of climate change, the impact of extremely cold weather on electric infrastructures is still worthy of attention since the fluctuation of yearly minimum temperature outstood in eastern Texas, where the majority of the population and electric transmission facilities are located. Thus, it is noteworthy to integrate the interaction between systems in the vulnerability assessment of infrastructure systems or the impact prediction of intense external shocks in future research.

Critical Propagation Path Identification for Cascading Overload Failures With Multi-Stage MILP

Cascading overload failures of transmission lines are one of the dominant factors that induce catastrophic blackouts. The reliability risk of cascading overload failures is significantly underestimated in traditional adequacy assessment studies. This paper proposes an evaluation model for the assessment of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-k</i> contingencies as cascading overloading failures. The interactions between outaged lines in the propagation path are revealed with a multi-stage optimization problem. The objective function is to maximize the probability of the propagation path of cascading overload failures. Several linearization techniques are proposed to convert the nonlinear evaluation model to mixed-integer linear programming. A rolling search algorithm is adopted to improve the capability of the proposed model to evaluate high-order contingencies. The case studies on the IEEE 118-bus systems show that the proposed model enables the identification of critical propagation paths with probabilities that are a few orders of magnitude larger than those with the traditional method.

A Variable Neighborhood Descent approach for electrical grids as an overload reduction method

Nowadays, there is a growing need to analyse systems using complex networks and graphs, especially in critical infrastructures. That includes transmission and distribution systems, where a single fault may cause power interruption for several consumers. A special approach to this problem uses Optimal Transmission Switching (OTS), where edges are comutated to change the network topology, and improves fault response. Because of its computational complexity, heuristics are proposed to the problem. This paper aims to introduce Variable Neighborhood Descent (VND) to the OTS problem, because of its local search feature, as well as the ability to deal with local minimuns. For that, the neighborhood structures and objetive function were adapted to address the peculiarities of the electrical grids, and a power redistribution algorithm was implemented. Failures and attacks were simulated, and the overload reduction was compared between the original topology and the one found by the VND (by line-switching). For power overload failures, results were better in intermediate overload levels, for both topologies. For node removal, best results were found in scale-free graphs, especially in intentional attacks, which shows that the local search phase, presented in VND, works well in a subset of edges limited to the proximity of the failure, especially with networks that have hubs. The computacional time shows the potential of the heuristic to be used in real time analysis.

Research on the robustness of interdependent supply networks with tunable parameters

A supply network is considered robust when it can maintain operations and connectedness despite disruptions, and building robust supply networks is one of the challenges in supply chain management. Typically, a supply network is an interdependent supply network (ISN) which consists of an undirected cyber-layer network and a directed physical-layer network. The cascading failure in an ISN can be caused by over-load and loss-dependency failures simultaneously, called hybrid cascading failure (HCF), which is limitedly studied in previous studies. In addition, ISNs are usually characterized by some tunable parameters such as the number of nodes, the number of inter-links, interconnecting patterns, and scaling exponents. However, a generic model that can generate ISNs with all of the aforementioned parameters is not proposed, and hence how these parameters affect the robustness of ISNs when considering HCF is still not clear. Therefore, in this paper, we first propose an ISN model to generate ISNs with the aforementioned tunable parameters, and then study how these parameters affect the robustness of the generated ISNs against random and targeted disruptions when considering HCF. The simulation results mainly show that: (i) an ISN is very vulnerable to disruptions in most cases, and removing a small number of nodes can make it collapse; (ii) the behavior of an ISN is very complex when considering HCF; and (iii) the robustness of an ISN can undergo first- and second-order phase transitions under proper parameters. Our work may be helpful for developing HCF-mitigating strategies and building robust supply networks.

Generation Post-COVID-19: The Time for Anti-Economics Has Come: Health, Minimalism and Rest

This paper makes the heterodox economics case of missing attention to health, minimization and rest in business, finance and economics. The COVID-19 pandemic has been addressed as a once-in-a-lifetime opportunity to a Great Reset. Started at the end of 2019, COVID-19 has been spreading around the world for over a year by now and no clear end is foreseeable yet. While vaccination and medication opportunities to cure the disease have improved impressively and steadily, the COVID-19 healthcare crisis also entails around 10 to over 30% of previously COVID-infected to be suffering from long haul symptoms. While our first understanding of post-COVID infection long haul symptoms, impetus and cure are still missing, there is hardening evidence that the newly emerging Generation COVID-19 Long Haulers may comprise of 0.3-1.659 billion previously infected with recurrent symptoms of fatigue, headaches and breathing problems as well as a set of debilitating memory fog and emotional distress. With this generation of COVID-19 Long Haulers, who are around 70-75% female and of a median age in their 30s and 40s, a dramatic shift to demand for health, minimalism and rest is predicted to emerge. Neoclassical ideas of business, finance and economic research have a limited understanding of health. Maximization pledges of productivity driven industries in business, finance and economics do not account for minimalism. Foremost behavioral economics started to address cognitive overload and decision making failures in a too complex world. There is no appreciation for rest in finance and economics. All these trends of attention to health, minimalism and rest the COVID-19 Long Haulers generation may change lastingly.

Cascading Failures Assessment in Renewable Integrated Power Grids Under Multiple Faults Contingencies

Cascading overload failures occurred in power systems due to higher penetration of renewable energy resources (RERs), which causes uncertainty in a grid. To overcome these cascading overload failures, proper assessment in the form of load flow balancing and transients stability is required in renewable integrated power grids (RIPGs). This problem becomes more critical in the occurrence of multiple intervals faults in multiple interconnected RIPGs, which causes the tripping of several RERs. Due to which outages occurred in various transmission lines, which lead the power system to cascading overload failures. To tackle this problem, hybrid probabilistic modeling is proposed in this paper for balancing load flow and an assessment of transients stability in multiple interconnected RIPGs. For balancing of load flow, a smart node transmission network topology is utilized along with integrating a unified power flow controller (UPFC), while transients instabilities are assessed through a UPFC alone. Contrary to the previously proposed algorithms, which are only suitable to compensate network instabilities in case of only a single interval fault, this work is supported by probabilistic modeling to compensate network instabilities under the occurrence of not only a single interval fault but also in case of more severe multiple intervals faults in multiple interconnected RIPGs that will lead the network to cascading failure outages. Simulation results verify that our proposed probabilistic algorithm achieved near an optimal performance by outperforming the existing proposed methodologies, which are only confined to mitigate the effect of network instabilities only in case of single interval fault and fails to address these network instabilities under the occurrence of severe multiple interval faults, which leads the network to cascading failure outages. These simulation results are also validated through an industrial case study performed on a western Denmark transmission network to show the superiority of our proposed algorithm.

Network endurance against cascading overload failure

Network endurance can be regarded as the upper limit of survival time before the system's complete breakdown, which is highly related to system resilience. Although network endurance against overload failure is critical for network design and operational management, the definition and corresponding evaluation method still remain challenging. In this paper, based on the load-dependent overload model, we define network endurance as the cascade duration at criticality before the complete network breakdown and develop an approach for endurance evaluation. We find that network endurance highly depends on initial disturbance intensity and cascade intensity. The network endurance with a uniform initial load distribution usually monotonically increases with decreasing initial disturbance intensity, while for other initial load distributions endurance behaviors are more complicated. We also provide theoretical analysis for the network endurance. Our findings may help to understand the network reliability mechanism against cascading overload failures and design a highly reliable network.

Modelagem física da deformação pós-sal em bacias invertidas

Este estudo analisou, em modelos físicos, uma inversão tectônica positiva de bacias com uma camada de sal na sequência pós-rifte. O intuito foi examinar a influência da variação da resistência à deformação da camada dúctil sobre a estruturação da sobrecarga, variando-se as espessuras do silicone (simulando sal) e da sobrecarga, e a velocidade de inversão. Os ensaios foram montados em caixas de areia com dimensões de 35 × 23,4 cm (comprimento × largura), nos quais o embasamento (a sequência pré-rifte) foi simulado por um pacote de areia. Após a fase de distensão e subsequente preenchimento da bacia recém-formada, depositou-se a sequência pós-rifte: um substrato de areia, uma camada de silicone e uma sobrecarga, também de areia. Cortes efetuados nos modelos úmidos após a deformação final de inversão revelaram que o número de falhas na sobrecarga variou significativamente em decorrência da variação da resistência à deformação tanto da camada dúctil quanto da sobrecarga rúptil. No caso da camada dúctil, de silicone, a resistência à deformação cresceu com o aumento da velocidade de deformação, enquanto para a sequência rúptil, de areia, a resistência cresceu quando se elevou a espessura da sobrecarga. Por outro lado, o aumento da espessura da camada dúctil produziu um decréscimo em sua resistência à deformação e acomodou por fluxo a deformação, internamente. A formação de estruturas rúpteis na sobrecarga foi associada ao desenvolvimento de falhas compressivas no pré- e sin-rifte, nucleadas durante a inversão. A reativação de falhas normais somente gerou falhas na sobrecarga quando caracterizadas por alto rejeito. Feições similares ocorrem na Bacia de Tucumán (Argentina).

Cascading overload failure analysis in renewable integrated power grids

A higher penetration of renewable energy resources (RERs) in network introduces uncertainty in a grid, which causes cascading overload failures. To mitigate these issues completely, a network must be able to simultaneously solve different complex problems at the same time. For this purpose, proper assessment of a renewable integrated power grid (RIPG) is required simultaneously for load flow balancing and transients stability enhancement. The problem becomes severe with the occurrence of a three phase (L-L-L) fault (TPF) in a network, which causes various line outages in the form of (N-1) and (N-1-1-1) contingencies, and thus lead the network to cascading overload failures. This work aims to develop a hybrid probabilistic load flow balancing model along with transients stability model to completely mitigate cascading overload failures in multiple interconnected RIPGs due to (N-1) and (N-1-1-1) contingencies. For this purpose, a unified power flow controller (UPFC) integrated smart transmission network topology is proposed, which provides an optimal load flow balancing and transients stability enhancement concurrently. Contrary to the previous studies, this work is backed by detailed probabilistic analysis to mitigate completely cascading overload failures in multiple interconnected RIPGs due to (N-1) and (N-1-1-1) contingencies.

Robustness of complex networks to cascading failures induced by Poisson fluctuating loads

Considering the fact that overload failures in real-world functional networks are usually caused by extreme values of temporally fluctuating loads that exceed the allowable range, we study the robustness of complex networks against cascading overload failures in such situation. In our model, at each cascading step the fluctuating load on a network is described by the total number of walkers, which is a random number that subjects to Poisson distribution. And a node fails when its overload probability is beyond its risk tolerance. Relative size of the giant component as a function of the cascade step is used to assess the robustness of networks against cascading overload.Comparing to the cases of stationary overload probability in Kishore et al., (2011, 2012) and previous study on temporally fluctuating (the total load reduced in accordance with the reduction of the network size during a cascade process) overload probability in Mizutaka and Yakubo, (2015), we find that the total fluctuating loads that subject to random distribution at each cascade process induce remarkable difference for extreme events probability distribution. The study shows that the change in the property of the load fluctuations may drastically alter the dependence degree of the overload probability and hence the robustness of networks. Furthermore, our results obtained through intensive simulations show that scale-free networks are more fragile against cascading overload failures than ER and WS networks. This conclusion is contrary to those previous prediction without considering random fluctuating loads and endogenous risk resistance ability.

Identification of Cascading Failure Initiated by Hidden Multiple-Branch Contingency

In past studies on cascading failure assessment, the risk of N – x initiating contingencies (ICs) regarding their initiating effect and hidden contingency risk (a high-risk x -component outage sequence within the IC) may not be fully investigated, which may underestimate the risk of IC-triggered cascading overload failures (COFs). In addition, the research on the efficient screening of N – x IC ( x ≥ 2) and the identification of the cascading with the largest system impact is limited. To undertake this challenge, this paper proposes a COF identification approach (CIA) which integrates the IC screening and the COF identification for finding the high-impact COFs initiated by N – x ICs with hidden risks. The CIA is validated by simulations on the IEEE 118-bus system. The simulation results demonstrate that the CIA can efficiently screen out the high-risk N – 2 and N – 3 ICs that have an optimal outage sequence and trigger COF chains. The results also show that the CIA can identify the IC-triggered high-impact COFs and quantifies their impacts. The approach can be used as either a cascade-triggering event screening tool (by applying the IC screening module) or a COF risk assessment tool for ensuring system reliability.

Onion-like networks are both robust and resilient

Tolerant connectivity and flow transmission within capacity are crucial functions as network. However, the threats to malicious attacks based on intelligent node selections and rapid breakdown by cascading overload failures increase more and more with large blackout or congestion in our contemporary networking systems and societies. It has been recently suggested that interwoven loops protect the network functions from such damages, but it is a computationally intractable combinatorial problem to maximize a set of necessary nodes for loops in order to improve the robustness. We propose a new method by enhancing loops in the incremental growth for constructing onion-like networks with positive degree-degree correlations, whose topological structure has the optimal tolerance of connectivity against attacks in the state-of-the-art. Moreover, we find out that onion-like networks acquire adaptive capacity in resilience by a change of routing policy for flow control to absorb cascading overload failures triggered by a single attack and simultaneous multi-attacks. The inhibitory effect is stronger than that in scale-free networks found in many real systems.

Impacts of Wind Power Uncertainty on Grid Vulnerability to Cascading Overload Failures

Higher penetration of renewables like wind power generation will introduce an unprecedented amount of uncertainty into the grid that might severely affect the grid vulnerability to cascading failures. In this study, we propose a mixed OPF-stochastic approach to analyze and simulate cascading failures in power grid and to evaluate the impact of wind generation in terms of its penetration and uncertainty level. The proposed approach incorporates both thermal stability model for transmission line outage and automatic power balance algorithms. Numerical simulation results on the IEEE 300 bus system indicate that uncertainty coming from wind energy has severe impacts on grid vulnerability to cascading overload failures under different contingency scenarios. Results also suggest that higher penetration levels of wind energy, if not managed appropriately, will add to this severity because higher uncertainties may be injected into weaker lines in a grid.

Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows

Cascading failures are a critical vulnerability of complex information or infrastructure networks. Here we investigate the properties of load-based cascading failures in real and synthetic spatially-embedded network structures, and propose mitigation strategies to reduce the severity of damages caused by such failures. We introduce a stochastic method for optimal heterogeneous distribution of resources (node capacities) subject to a fixed total cost. Additionally, we design and compare the performance of networks with N-stable and (N-1)-stable network-capacity allocations by triggering cascades using various real-world node-attack and node-failure scenarios. We show that failure mitigation through increased node protection can be effectively achieved against single-node failures. However, mitigating against multiple node failures is much more difficult due to the combinatorial increase in possible sets of initially failing nodes. We analyze the robustness of the system with increasing protection, and find that a critical tolerance exists at which the system undergoes a phase transition, and above which the network almost completely survives an attack. Moreover, we show that cascade-size distributions measured in this region exhibit a power-law decay. Finally, we find a strong correlation between cascade sizes induced by individual nodes and sets of nodes. We also show that network topology alone is a weak predictor in determining the progression of cascading failures.

Structural instability of large-scale functional networks.

We study how large functional networks can grow stably under possible cascading overload failures and evaluated the maximum stable network size above which even a small-scale failure would cause a fatal breakdown of the network. Employing a model of cascading failures induced by temporally fluctuating loads, the maximum stable size nmax has been calculated as a function of the load reduction parameter r that characterizes how quickly the total load is reduced during the cascade. If we reduce the total load sufficiently fast (r ≥ rc), the network can grow infinitely. Otherwise, nmax is finite and increases with r. For a fixed r(< rc), nmax for a scale-free network is larger than that for an exponential network with the same average degree. We also discuss how one detects and avoids the crisis of a fatal breakdown of the network from the relation between the sizes of the initial network and the largest component after an ordinarily occurring cascading failure.

Cascading failure model in power grids using the complex network theory

The massive power flow transferring triggers the node overload cascade, which further activates the protection system hidden failures. In this study, we propose a cascading failure model based on complex network theory by combining the node overload failures and hidden failures of transmission lines in blackouts together. The model concerns the electrical characteristics, which has not been involved in the existed cascading failure model when modelling node loads. The cascading failure simulations on the 500 kV Center China power grid and the IEEE-300-bus test system demonstrate that the proposed cascading failure model with node load built on electrical characteristics can bring better network invulnerability than the model with that constructed on pure structure topology. Meanwhile, the proposed model can better exhibit the negative aspects of hidden failures in the blackouts. The deliberate attacks based on high-risk nodes can decrease the invulnerability of power grids more seriously compared with those based on high-degree and high-load nodes. The results provide a new way to analyse the power grid cascading failure mechanism based on the complex network theory.

Prediction of Cascading Failures in Spatial Networks.

Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.