Identification of Two Vulnerability Features: A New Framework for Electrical Networks Based on the Load Redistribution Mechanism of Complex Networks

Wei, Xiaoguang; Gao, Shibin; Huang, Tao; Wang, Tao; Wang, Fan

doi:10.1155/2019/3531209

Cited by 16 publications

(21 citation statements)

References 35 publications

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“…In addition, both the FRRN and the FRFN do not consider the preprocessing of fault alarm messages, that is, the fault alarm messages cannot be effectively utilized by deleting redundant information before modeling the SNPS-based diagnosis models. erefore, when the scale and complexity [53][54][55][56] of a power system increase and the redundancy of fault information is high, the fault tolerance of SNPS-based methods will reduce rapidly.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

et al. 2020

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Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

et al. 2020

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“…In the EN, cascading failures have been analyzed from the perspective of the overloaded mechanism [15,[27][28][29]. When a line fails, the power transmitted over the line will be redistributed in the network, and thus, a fault may cause increased flow in other branches and even overload them, leading to the fault propagation.…”

Section: Introductionmentioning

confidence: 99%

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

et al. 2019

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Communication networks as smart infrastructure systems play an important role in smart girds to monitor, control, and manage the operation of electrical networks. However, due to the interdependencies between communication networks and electrical networks, once communication networks fail (or are attacked), the faults can be easily propagated to electrical networks which even lead to cascading blackout; therefore it is crucial to investigate the impacts of failures of communication networks on the operation of electrical networks. This paper focuses on cascading failures in interdependent systems from the perspective of cyber-physical security. In the interdependent fault propagation model, the complex network-based virus propagation model is used to describe virus infection in the scale-free and small-world topologically structured communication networks. Meanwhile, in the electrical network, dynamic power flow is employed to reproduce the behaviors of the electrical networks after a fault. In addition, two time windows, i.e., the virus infection cycle and the tripping time of overloaded branches, are considered to analyze the fault characteristics of both electrical branches and communication nodes along time under virus propagation. The proposed model is applied to the IEEE 118-bus system and the French grid coupled with different communication network structures. The results show that the scale-free communication network is more vulnerable to virus propagation in smart cyber-physical grids.

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“…Based on CNT, various studies have proven that many electrical networks are small-world networks by investigating the average shortest path and average clustering coefficient of networks [5]- [7]. In addition, electrical networks have also been shown to exhibit scale-free features [8]- [10]. Such scale-free properties demonstrate that electrical networks are robust against random attacks; however, they are highly vulnerable if critical targets are attacked.…”

Section: Introductionmentioning

confidence: 99%

Electrical Network Operational Vulnerability Evaluation Based on Small-World and Scale-Free Properties

et al. 2019

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Assessment of electrical network vulnerability based on complex network theory (CNT) has attracted increasing attention. However, CNT focuses on analyzing the structural vulnerability and has significant limitations regarding operational vulnerability. To address the lack of a comprehensive CNT-based framework to assess operational vulnerability, a temporal-spatial correlation graph (TSCG) that considers the topological, physical, and operational characteristics of electrical networks is proposed. To better assess vulnerability, two metrics, i.e., impact ability and susceptibility of branches, based on symmetric entropy from the load redistribution mechanism of electrical networks and their corresponding TSCGs are proposed. Applications to IEEE 39-bus system, IEEE 118-bus system, and French grid demonstrate that the proposed TSCGs have distinctive features that can intuitively and simply reveal the features of impact ability and susceptibility in CNT.

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Identification of Two Vulnerability Features: A New Framework for Electrical Networks Based on the Load Redistribution Mechanism of Complex Networks

Cited by 16 publications

References 35 publications

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

Cascading Failures Analysis Considering Extreme Virus Propagation of Cyber‐Physical Systems in Smart Grids

Electrical Network Operational Vulnerability Evaluation Based on Small-World and Scale-Free Properties

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