A power flow based model for the analysis of vulnerability in power networks

Wang, Zhuoyang; Chen, Guo; Hill, David J.; Zhang, Dong

doi:10.1016/j.physa.2016.05.001

Cited by 26 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing studies on power network invulnerability are either based on analytic method [1,2] or inspired by complex network theory [5,6]. The former can fully reflect the functional features of the increasingly complex power networks, but is too complex to be applied widely or computed easily [3,4]. The latter approach supports rapid analysis of large networks, but has difficulty in analysing functional features.…”

Section: Introductionmentioning

confidence: 99%

A Novel Invulnerability Index for Invulnerability Assessment of Complex Power Network

Huang¹,

Hua²,

Li³

et al. 2019

EJEE

View full text Add to dashboard Cite

Based on complex network theory and electrical analysis methods, this paper puts forward a novel Invulnerability index to improve invulnerability assessment of complex power network. The index was constructed based on concepts like line invulnerability value, invulnerability value of source-source-load pair, influence value and influence penalty. The invulnerability assessment effect of the index was compared with that of other indices under random, comprehensive static and comprehensive dynamic attacks. It was verified that the proposed invulnerability index outperformed other indices in the assessment of power network invulnerability. The research findings shed new light on the analysis of power network invulnerability.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Invulnerability Index for Invulnerability Assessment of Complex Power Network

Huang¹,

Hua²,

Li³

et al. 2019

EJEE

View full text Add to dashboard Cite

show abstract

“…Many researches have been carried out attempting to solve various challenges which energy systems are currently facing, e.g., system reliability and stability (Zio and Di Maio, 2010;Fang and Zio, 2013;Zhang et al, 2016a), operation efficiency and cost control (Hegde and Gray, 2017;Azadeh et al, 2016), renewable energy management (Lou et al, 2016) and environment issues (Tan et al, 2016). The application of techniques of forecasting (Wang et al, 2016;Kalantari-Dahaghi et al, 2015), classification (Hu et al, 2010;Pooyan et al, 2015) and optimization (Azadeh et al, 2016;Xiong et al, 2018) has been successfully explored in different energy systems, to the benefit of regulators, customers and operators. Besides, recurrent and cascade neural networks are among the best choices for dynamic system predictive modeling (Vaferi et al, 2015;Lashkarbolooki et al, 2013;Güler and Übeyli, 2006;Güler et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Zio

Zhang

et al. 2018

Journal of Natural Gas Science and Engineering

View full text Add to dashboard Cite

et al.. A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks. A B S T R A C TThe current framework of management of natural gas pipeline systems, based on off-line simulation, is facing challenges because of the increasing complexity, uncertainty and a number of time-dependent factors. To be effective, it requires comprehensive knowledge of system characteristics, accurate initial and boundary conditions. In an attempt to circumvent these problems, in this work we propose to use the deep learning method in the natural gas transmission system operation and management context. A data-driven prediction method is developed from real-time data of operation pressure and gas consumption. Specifically, the deep learning method is combined with the data window method and structural controllability theory to predict the conditions of gas pipeline network components. The data window method is applied to reconstruct the data structure and build a "memory" for the deep learning method. Structural controllability theory is applied to extract critical parameters, for reducing the problem size. The developed method allows accurate and efficient predictions, especially in abnormal conditions. For demonstration, the method is applied to a complex gas pipeline network. The results show that the developed method can provide accurate real-time predictions useful for reducing potential losses in operation, and perform efficient and effective management of the gas pipeline system. In the case study, the average prediction accuracy is higher than 0.99.

show abstract

“…The evolution of both logical and real values of system parameters can be analyzed by a hybrid attack graph under attack and recovery actions scenarios [14]. As simultaneous attacks and sequential attacks have diverse impacts on power systems, it is necessary to investigate the cascading failure propagation of multiple attack scenarios by using proper evaluation indexes.However, vulnerability of topology is affected by the transmission efficiency, connectivity, and connected components [15], particularly the power flow distribution of power systems [16]. The topology of the power system is relatively inflexible and vulnerable to intentional attacks [17].…”

mentioning

confidence: 99%

“…However, vulnerability of topology is affected by the transmission efficiency, connectivity, and connected components [15], particularly the power flow distribution of power systems [16]. The topology of the power system is relatively inflexible and vulnerable to intentional attacks [17].…”

mentioning

confidence: 99%

Community-Based Link-Addition Strategies for Mitigating Cascading Failures in Modern Power Systems

Lee

2020

Processes

View full text Add to dashboard Cite

The propagation of cascading failures of modern power systems is mainly constrained by the network topology and system parameter. In order to alleviate the cascading failure impacts, it is necessary to adjust the original network topology considering the geographical factors, construction costs and requirements of engineering practice. Based on the complex network theory, the power system is modeled as a directed graph. The graph is divided into communities based on the Fast–Newman algorithm, where each community contains at least one generator node. Combined with the islanding characteristics and the node vulnerability, three low-degree-node-based link-addition strategies are proposed to optimize the original topology. A new evaluation index combining with the attack difficulty and the island ratio is proposed to measure the impacts on the network under sequential attacks. From the analysis of the experimental results of three attack scenarios, this study adopts the proposed strategies to enhance the network connectivity and improve the robustness to some extent. It is therefore helpful to guide the power system cascading failure mitigation strategies and network optimization planning.

show abstract

A power flow based model for the analysis of vulnerability in power networks

Cited by 26 publications

References 22 publications

A Novel Invulnerability Index for Invulnerability Assessment of Complex Power Network

A Novel Invulnerability Index for Invulnerability Assessment of Complex Power Network

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Community-Based Link-Addition Strategies for Mitigating Cascading Failures in Modern Power Systems

Contact Info

Product

Resources

About