Complex Network-Based Cascading Faults Graph for the Analysis of Transmission Network Vulnerability

Wei, Xiaoguang; Gao, Shibin; Huang, Tao; Bompard, Ettore; Pi, Renjian; Wang, Tao

doi:10.1109/tii.2018.2840429

Cited by 63 publications

(48 citation statements)

References 35 publications

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“…On the other hand, the vulnerability of network has been addressed and assessed in [2,[18][19][20][21][22][23][24]. The vulnerability studies aim to identify critical components and recognize severe scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…The vulnerability studies aim to identify critical components and recognize severe scenarios. Therefore, many indices have been proposed in [2,[18][19][20][21][22][23][24] to conduct the vulnerability of the network. A comparison of four methods for power system contingency screening and ranking has been presented in [25].…”

Section: Related Workmentioning

confidence: 99%

“…8 (Cluster No. (1)) shows all contingencies that their outages may result in cascading blackouts, where all insecurity indices reached the maximum value, which is an equal four, refers to (2). Reached the maximum value means that the power follow does not converge at the end.…”

Section: Comparison Of Km and Fcm Algorithmsmentioning

confidence: 99%

“…On the other hand, cluster No. (2), as shown in Fig. 9, contains all contingencies that their outages result in experiencing the shortage of reactive power support.…”

Section: Comparison Of Km and Fcm Algorithmsmentioning

confidence: 99%

“…Consequently, single tripping of a transmission line or generation unit may trigger a sequence of unplanned outages that eventually result in a total blackout [1]. Therefore, the vulnerability of transmission circuits, power transformers, and generation units should be investigated all the time to ensure that their outage will not leave the network in danger [2]. The study of system security under steady-state conditions is known as contingency analysis [3].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Conceptual Analysis of Different Clustering Techniques for Static Security Investigation

Hemade¹,

Ibrahim²,

Talaat³

2019

IJISA

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Power system contingency studies play a pivotal role in maintaining the security and integrity of modern power system operation. However, the number of possible contingencies is enormous and mostly vague. Therefore, in this paper, two well-known clustering techniques namely K-Means (KM) and Fuzzy C-Means (FCM) are used for contingency screening and ranking. The performance of both algorithms is comparatively investigated using IEEE 118-bus test system. Considering various loading conditions and multiple outages, the IEEE 118-bus contingencies have been generated using fast-decoupled power flow (FDPF). Silhouette analysis and fuzzy partition coefficient techniques have been profitably exploited to offer an insight view of the number of centroids. Moreover, the principal component analysis (PCA) has been used to extract the dominant features and ensure the consistency of passed data with artificial intelligence algorithms' requirements. Although analysis of comparison results showed excellent compatibility between the two clustering algorithms, the FCM model was found more suitable for power system static security investigation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison Of Km and Fcm Algorithmsmentioning

confidence: 99%

“…On the other hand, cluster No. (2), as shown in Fig. 9, contains all contingencies that their outages result in experiencing the shortage of reactive power support.…”

Section: Comparison Of Km and Fcm Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Conceptual Analysis of Different Clustering Techniques for Static Security Investigation

Hemade¹,

Ibrahim²,

Talaat³

2019

IJISA

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Reliability assessment of integrated energy systems based on complex network theory

Pan

Yan

Gao

et al. 2022

Engineering Reports

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As integrated energy systems (IES) continue to undergo development and advancement, the degree of coupling between subsystems and their complexity are also on the rise, posing a challenge for the reliability assessment of such systems. The conventional method is unable to explain the interaction between subsystems as well as system-level phenomena. The complex network theory is needed to provide a deeper analysis. Firstly, this paper introduces the basic concept and analysis tools of complex network theory. Secondly, the topological network model and the identification indexes of weak links for the IES are summarized as the basis for reliability assessment. Then, the reliability assessment indexes and assessment methods are sorted out for the IES based on the complex network theory; and further, the measures to optimize the reliability of the IES are put forward. Finally, from the perspective of complex network theory, directions for future research on the reliability assessment of IES are suggested.

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Vulnerability assessment of a power transmission network employing complex network theory in a resilience framework

2020

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Complex Network-Based Cascading Faults Graph for the Analysis of Transmission Network Vulnerability

Cited by 63 publications

References 35 publications

Conceptual Analysis of Different Clustering Techniques for Static Security Investigation

Conceptual Analysis of Different Clustering Techniques for Static Security Investigation

Reliability assessment of integrated energy systems based on complex network theory

Vulnerability assessment of a power transmission network employing complex network theory in a resilience framework

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