Cascading Faults Research Articles

Cyclic mining of alarm and operator action events for enhanced process operation

This paper addresses the challenge of fault propagation in industrial facilities, where a fault in one process variable can lead to cascading faults in other variables. As a result, the propagation of alarms corresponding to these faulty variables occurs, leading operators to potentially receive an excessive number of alarm notifications that could significantly impact their decision-making capabilities. To address this issue, a systematic method is proposed to investigate potential fault propagation paths to provide decision support in response to alarm notifications, in order to minimize industrial process failures. The contributions of this paper are twofold. Firstly, it involves enhanced dependency analysis that captures dependent alarms and identifies both weak and strong dependencies among alarm variables using historical alarm and event (A&E) logs that are generated by industrial control systems. Secondly, it offers comprehensive visualization of fault propagation in response to single and multiple alarms, including extracting crucial timing information, identifying the shortest, longest, and critical paths, and determining effective operator actions. The proposed method is designed to enhance process operation and provide essential decision support for industrial operators. The effectiveness of the proposed approach is validated through a case study using real industrial data.

Modeling the resilience of liner shipping network under cascading effects: Considering distance constraints and transportation time

The Liner Shipping Network (LSN) is a crucial component of the maritime supply chain (MSC) but remains susceptible to disruptions caused by natural disasters or emergencies, given its intricate structure and high level of interdependence. In this study, we present a novel cascading failure model that accounts for the heterogeneity of distances and transportation times between nodes, coupled with actual shipping lines. This comprehensive approach aims to thoroughly evaluate the resilience of the LSN. The study’s results reveal noteworthy variations in the cascading effects across diverse load distribution distances and transportation times. Furthermore, it delves into load distribution strategies to improve the resilience of the LSN and investigates the impact of capacity parameters on cascading effects. The results indicate that the distribution strategy proposed in this paper, integrating transportation time and redundancy levels, effectively suppresses the spread of cascading faults and improves network resilience. The outcomes of this research contribute to a deeper understanding of strategies for enhancing resilience and managing risks in shipping networks, providing valuable insights for management and decision-making in maritime operations.

Review and prospect on the construction method of security region for integrated natural gas and power systems

Natural gas and power systems are both important components of modern energy systems. As an efficient and clean peak-shaving power source, natural gas power generation is crucial for ensuring the safe operation of a high-penetration renewable energy power system —supporting China's strategic goal of "carbon peaking and carbon neutrality." However, in recent years, there have been frequent incidents of cascading faults between the gas and power grids. Studying the security region of the integrated electricity-gas system (IEGS) and identifying the system's security margin online is the key to actively preventing such accidents. In view of this, this paper first introduced the typical coupling forms of electricity-gas networks and summarizes the security analysis and evaluation methods from the perspectives of power systems and natural gas systems, respectively. Then, the basic model of the IEGS security region considering the interconnection of gas and electricity networks was extracted, and various methods for solving IEGS security boundaries and dealing with uncertain disturbance factors were compared. Finally, the possible development directions of the construction method of the security region for IEGS were proposed.

Research on engine multiple fault diagnosis method based on cascade model

The engine is the core component of the power system, and the health status of the components of the engine is very important for the normal operation of the power system. Most of the exist-ing fault diagnosis methods diagnose the engine fault type without further analysis of the sever-ity of the fault. Different fault severity requires different maintenance measures. Therefore, this paper proposes a cascading fault diagnosis model based on Gated Recurrent Unit (GRU) to di-agnose the fault type and the severity of the corresponding fault type.Firstly, the effective fea-tures are extracted from the vibration signals, and then the features are input into the GRU for fault type diagnosis to obtain the sub-fault diagnosis model. After training, one fault type diag-nosis model and four fault severity diagnosis models are obtained. Then the obtained model is cascaded to obtain the total fault diagnosis network. Fault type diagnosis is located at the first level, and four fault severity diagnosis is located at the second level. The effectiveness of the proposed method is verified by experimental data

A branch-and-cut approach to solve the Fault Diagnosis Problem with Lazy Spread and imperfect system information

This paper presents a new approach to solving the Fault Diagnosis Problem with Lazy Spread (FDPL) that arises in many fault-tolerant real-world systems with few opportunities for maintenance during their operations and significant failure interactions between the subsystems/components. As opposed to cascading faults that spread to most of the system almost instantaneously, FDPL considers fault-resistant systems where the spread of failures is relatively slow (lazy), i.e., only a small fraction of the components are faulty at the time of inspection, and accurate diagnosis of the faulty components is of critical importance to restore system performance and stop further damage. Introducing an extra level of difficulty, FDPL needs to be solved under imperfect (missing and wrong) system information in most real-world settings. To address this challenging prediction problem, we use graph theory concepts to develop an integer programming formulation and devise an efficient branch-and-cut algorithm for its solution. Extensive numerical experiments on realistic problem instances attest to the superior performance of our approach in terms of both computational efficiency and prediction accuracy compared to the state-of-the-art in the literature.

Supershear triggering and cascading fault ruptures of the 2023 Kahramanmaraş, Türkiye, earthquake doublet.

On 6 February 2023, two large earthquakes (moment magnitude 7.8 and 7.6) shocked a vast area of southeastern Türkiye and northern Syria, leading to heavy casualties and economic loss. To investigate the rupture process over multiple fault segments, we performed a comprehensive analysis of local seismic and geodetic data and determined supershear ruptures on the initial branch and the Pazarcık and Erkenek segments and subshear ruptures on the Amanos segment of event 1. The bilateral rupture of event 2 also presents distinct sub- and supershear velocities. The dynamic stress of the branch fault rupture triggered the Pazarcık segment initial rupture at a point 9 kilometers west of the junction of these two faults, boosting the supershear rupture of the Pazarcık segment of the main fault. The geometry and prestress level of multiple segments controlled the rupture behaviors and influenced the ground shaking intensity.

Identifying critical nodes of cyber–physical power systems based on improved adaptive differential evolution

The identification and protection of critical nodes have a significant influence on the reliability and robustness of the cyber–physical power system (CPPS), which can effectively reduce the risk of cascading failures. Firstly, this paper establishes a one-to-one coupled cascading fault model, which considers different topological characteristics of power and cyber networks. Based on the interdependence between the two networks, the cascading fault propagation mechanism after node failures in CPPS is analyzed. Then, a critical node identification method based on improved adaptive differential evolution (JADE) is proposed to identify the sets of critical nodes that can maximize the impact on the system load of CPPS. Combined with the cascading failure characteristics of CPPS, in the population generation strategy, the test vectors that do not meet the conditions after the crossover operation are filtered and replaced to improve the search efficiency of the algorithm. Finally, in the case study, an interdependent CPPS is constructed with an IEEE 39-bus system and a scale-free network of the same nodes. The simulation results verify the effectiveness of the proposed node importance assessment strategy. In addition, it is also shown that evaluating the critical nodes in power systems is important for enhancing the robustness and ensuring the security of the power grid operation.

Cascade failure analysis of power-information interdependent network

Modern information technology has been constantly evolving, transforming the traditional power grid into a network that couples both power and information layers. Understanding the cascade failure behavior of such power communication interdependent networks is essential for effectively controlling catastrophic network failures, preventing system collapse, and ensuring normal network operation. This research can contribute to the development of tools to predict and prevent such failures, and restore normal network functions in a timely manner. This paper focuses on the modeling method and cascading fault analysis of the power-information double-layer coupling network. We construct power information interdependent networks based on IEEE30 system and England39 system, and evaluate the cascade failure results using load distribution cascade failure model and HITS algorithm. The evaluation criteria include network efficiency, residual network size, and residual network load. By analyzing these parameters, we can gain insights into the performance of the power-information interdependent networks during cascade failures. Through simulation results, we demonstrate that the type i attack proposed in this paper renders the network structure unstable and less robust compared to the degree attack, intermediate attack, and random attack. These findings provide valuable references for developing strategies to mitigate the cascading failure of power-information interdependent networks.

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

A Cascading Fault Path Prediction Method for Integrated Energy Distribution Networks Based on the Improved OPA Model under Typhoon Disasters

GRNN-Based Real-Time Fault Chain Prediction

This paper proposes a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs). While identifying risky FCs is pivotal to alleviating cascading failures, the complex spatio-temporal dependencies among the components of the power system render challenges to modeling and analyzing FCs. Furthermore, the real-time search of risky FCs faces an inherent combinatorial complexity that grows exponentially with the size of the system. The proposed framework leverages the recent advances in graph recurrent neural networks to circumvent the computational complexities of the real-time search of FCs. The search process is formalized as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN) that judiciously accounts for the inherent temporal and spatial structures of the data generated by the system. The key features of this structure include (i) leveraging the spatial structure of the data induced by the system topology, (ii) leveraging the temporal structure of data induced by system dynamics, and (iii) efficiently summarizing the system's history in the latent space of the GRNN. The proposed framework's efficiency is compared to the relevant literature on the IEEE 39-bus New England system and the IEEE 118-bus system.

Search strategy and line association analysis of cascading failure accident chain in new energy power systems

As the penetration rate of new energy in the power system gradually increases and the complexity of cascading faults increases, it is of great significance for the power system to comprehensively explore the chain of cascading faults in the new energy power system and quickly determine the closely related lines in the cascading faults. In response to the lack of consideration in existing research of the changes in the importance of transmission lines after the introduction of new energy, this paper proposes a cascading failure prediction index that integrates the importance and operational status of transmission lines in new energy power systems and applies it to the search for cascading failures in new energy power systems. First, the development characteristics of cascading faults were analyzed, and the main factors influencing cascading faults were identified: the importance of the transmission line and operating status of the new energy power system. Based on these factors, a prediction index for cascading faults was established, and the accident chain was searched using this index. Then, the FP-growth algorithm was used to analyze the lines in the fault chain concentration, and based on the analysis results, the correlation relationship suitable for the cascading failure lines in the new energy power system was determined. Finally, a simulation was conducted on an IEEE 10 machine 39 node system containing new energy wind turbines, and the results verified the effectiveness of the proposed indicators and strategies.

A coordinated power flow control strategy to enhance the reliability of hybrid ac/dc power grids during cascading faults

Cascading fault is a newly emerging challenge in hybrid ac/dc grids including current source converters (CSCs) which commences from a dc or a long-lasting severe inverter ac fault and ultimately provokes a total blackout at the inverter ac side. Nevertheless, since the cascading faults emanating from dc faults are not accompanied by commutation failure, the prevailing commutation failure elimination strategies are unable to inhibit these contingencies. To obviate such a challenge, this paper proposes a coordinated power flow control strategy which can preclude the blackouts caused by cascading faults initiated from either dc or inverter ac faults, thereby improving the reliability of hybrid ac/dc power grids. To achieve this, a back-to-back voltage source converter (B2B-VSC) is employed which is responsible for controlling both the inverter ac bus voltage and the active power flow of its connected ac line. This would not only mitigate the probability of commutation failure due to the reinforced inverter ac bus voltage, but also enhance the power flow flexibility of the power grid during both normal and fault conditions. In addition, the cost associated with implementation of the proposed strategy is much less than installing several backup parallel lines which are useless under no-fault circumstances. Finally, the practical viability of the proposed strategy is scrutinized through laboratory testing using co-simulation of RTDS and Opal-RT simulators. The obtained results show that the developed strategy can effectively manage the power flow of B2B-VSC and CSC-HVDC systems during different possible fault cases so that the power outage at the inverter ac side is minimized.

Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP.

Container cascade fault detection based on spatial–temporal correlation in cloud environment

Containers are light, numerous, and interdependent, which are prone to cascading fault, increasing the probability of fault and the difficulty of detection. Existing detection methods are usually based on a cascade fault model with traditional association analysis. The tradition model lacks consideration of the fault cascade history dimension and container space correlation dimension which results in a lower detection effect. And the imbalance of fault data in the cloud environment to the detection method to bring interference. Instead, this paper proposes a cascade fault detection method based on spatial–temporal correlation in cloud environment. First, the container cascade fault relationship model is constructed by extracting the spatial–temporal correlation from historical container faults. Second, based on dynamic feedback data sampling combined with ensemble learning, a container fault model learning method is designed to solve the imbalance of fault data. Then, a real-time container cascade fault detection mechanism for container cascade failure is proposed. The experimental results show that compared with the existing fault detection methods, the proposed method can effectively improve the detection accuracy, recall rate, and F1 value.

Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State

With the promotion of energy security strategy and the access of the high penetration of renewable energy, the related methods of situational awareness in the traditional transmission network may be inapplicable for modern power grids. For the early warning and location of the cascading faults caused by power flow shift, a novel method for security situation prediction in the transmission network is put forward based on Long Short Term Memory. Firstly, the power flow model is used to construct the early warning framework of transmission network security situation based on LSTM, and ADASYN oversampling is used for sample equilibrium. Secondly, the validity of the proposed method is verified by the open-source platform “Grid2Op” in the modified IEEE 36-bus system. Results indicate that the early warning accuracy for cascading failure can achieve around 90% with prediction time less than 1 second. Various simulation results demonstrate that the proposed method can improve the accuracy of security situation warning, shorten the time for identifying abnormality of the transmission network and provide reliable support for the operation and maintenance of the transmission network.

Identification of Vulnerable Lines in Smart Grid Systems Based on Improved Agglomerative Hierarchical Clustering

The identification of vulnerable lines in smart grid systems is of great significance to increase the stability of the smart grid systems and reduce the occurrence of cascading fault blackouts. Inspired by the machine learning method, this study proposes a vulnerable line identification approach based on the improved agglomerative hierarchical clustering algorithm. By jointly considering the topological parameters and the electrical properties, we discuss the vulnerability of the transmission lines and establish the influencing factors. Then, we adopt principal component analysis (PCA) to select the influencing factors and reduce their dimensionality. Finally, an improved agglomerative hierarchical clustering algorithm is proposed and employed to divide the lines to identify the vulnerable lines in the smart grid systems. Experiments over the IEEE 39-bus system demonstrate that our proposed method can efficiently and accurately identify different types of potential vulnerable lines in smart grid systems.

A novel preventive islanding scheme of power system under extreme typhoon events

Extreme typhoon events often damage transmission lines and towers, causing large-scale cascading faults, and even collapsing the power system. In this paper, a preventive islanding scheme under extreme typhoon events is proposed to prevent the occurrence and propagation of cascading faults. Firstly, the outage probability of transmission branches under typhoon events is modeled and evaluated by employing the Batts typhoon model and fragility curve. Then, based on power flow calculation, a preventive islanding criterion is proposed to describe the damage degree of typhoon events to the power system and provide a decision-making basis for preventive islanding. Besides, a preventive islanding surface searching algorithm based on the combination of nodes with active power balance is proposed to minimize the load shedding of the system, and the vulnerable branches are selected as the obligatory splitting branches. Then, an optimal load shedding model is proposed to minimize the voltage deviation of each node in the islands by optimizing the reactive power generation and load shedding distribution in each island. Finally, a staged preventive islanding process under extreme typhoon events is proposed based on the time sequence of typhoon development. The simulation tests of the IEEE 39-bus and 118-bus systems under real typhoons indicate that the preventive islanding scheme proposed in this paper can effectively prevent the occurrence of cascading faults and reduce the load loss of the power system under an extreme typhoon event.

Improved transient stability of virtual synchronous generators for grid voltage cascading faults

• The basis of the investigation is a cascading fault of a grid-connected inverter in both low and high voltage power grids. • Pay attention to the fault with equilibrium point and the fault without equilibrium point while dealing with low-voltage faults, and utilize the phase portrait to assess stability. Finally, a control technique is given to ensure that the two defects are well controlled. • The equal area method is utilized to assess the fault in high voltage faults, and it is inferred that there must be an equilibrium point in high voltage faults. Finally, a control technique for active power transient overshoot mitigation and PCC voltage support during faults is provided. The topic of VSG grid voltage fault ride through has drawn increasing attention as virtual synchronous generator (VSG) technology has been widely applied in the field of renewable energy power generation. Due to the delay of the reactive power compensation device after the low-voltage fault recovery, the high-voltage fault will happen immediately after the grid's low-voltage fault is resolved. This phenomenon is known as low and high voltage cascading faults. There is not enough research on this specific voltage fault right now. For specific investigation, the low and high voltage cascading faults in this paper are separated into low-voltage fault stages and high-voltage fault stages. The equal area criterion (EAC) is used to analyze the specific process at the low-voltage fault stage. In addition, the dynamic damping control (DDC) is utilized to resolve the problem of low-voltage fault stability. Under low voltage fault, the phase portrait studies the single-period stability with an equilibrium point in a single-period and the multi-period stability without an equilibrium point in a single-period, respectively. Using a phase portrait to analyze system performance during a high voltage fault, and a virtual capacitor feedback control (VCFC) is offered to effectively reduce active power overshoot due to high voltage fault. The EAC is used to analyze the specific process of the high voltage stage, it is concluded that there is always an equilibrium point in the high-voltage fault where IGBT limitations are ignored. Finally, the correctness and feasibility of the theory are verified by simulation, and the specific analysis results are given.

Routing optimization method of cyber-energy system considering cross-space cascading fault handling process

The degree of coupling between information and energy is increased, leading to a cyber-energy system. The amount of information carried by communication networks is expanding, and if a cyber system fails or operates abnormally, the defect may propagate across space to the entire system. In this paper, a routing optimization method of cyber-energy systems is proposed to optimize information operations and respond to cyber attacks. To account for the difference in information demand between electricity and gas, we describe the communication business and determine the business’s importance using the fuzzy analytic hierarchy approach. The risk assessment model is improved by analyzing the structural vulnerability of the energy system based on complex network theory. To coordinate various conditions, the approach is split into preventative and emergency control modes. The Yen algorithm and the fuzzy membership function are coupled in the preventative control mode to simultaneously optimize load balancing and communication risk. In the event of cyber attacks, the emergency control mode is triggered, which employs emergency isolation to minimize fault propagation and to preserve system stability in a partially disconnected operating state. Finally, the method’s effectiveness is illustrated through using IEEE 30 bus and gas 13 node coupling system.

Robustness Analysis of CPPS considering Power Flow Constraints

Comprehensively considering the power flow constraints of the power network, the cyber-physical power system (CPPS) network integration model is established by combining with the topological structure information of the power network and information network. The Newton–Raphson method is used to solve the AC power flow distribution in the power network. Based on the interdependent network theory, a CPPS cascading fault analysis procedure considering the power flow constraints is given. Finally, the importance of CPPS network nodes is evaluated and analyzed. Simulation experiments are carried out with the IEEE39 node system as an example. The results show that some electrical components failures in the power network may lead to large-scale transfer of power flow, resulting in overloading of other electrical components or transmission lines. It promotes further cascade propagation of failures in the CPPS network, which eventually leads to a sharp decrease in the robustness of CPPS.