Strategic Power Infrastructure Defense

Li, Hao; Rosenwald, G.W.; Jung, Jonghoon; Liu, Chen‐Ching

doi:10.1109/jproc.2005.847260

Cited by 123 publications

(8 citation statements)

References 50 publications

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“…A common criterion is to use the eigengaps, that is, the difference between two consecutive eigenvalues (recall that a good -partition exists if is small). Since we are interested in the smallest eigenvalues, we use an eigenvalue difference relative to their size: (10) A high value of (see Fig. 2 for an example) indicates that the network admits a good decomposition into at least islands, and that this will be revealed by the spectral embedding in dimension .…”

Section: A Rationalementioning

confidence: 99%

“…We first concentrate on dimensions 2 and 3, where we can visualise the embeddings. More generally, we would use the relative eigengaps (10) to determine the optimal spectral dimension , and, if , as visualization is harder, a dendrogram to represent the hierarchical internal structure.…”

Section: A Power Flow Laplacianmentioning

confidence: 99%

“…The most popular methodologies proposed include keeping together slow-coherent generators to avoid the loss of transient stability [7] and graph-search based approaches, e.g., [8], [9]. Recently a novel methodology of spectral clustering has been proposed for controlled islanding that is based on recent advances in graph theory [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Spectral Clustering of Power Grids

Sánchez-García

Fennelly

Norris

et al. 2014

IEEE Trans. Power Syst.

201

171

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A power transmission system can be represented by a network with nodes and links representing buses and electrical transmission lines, respectively. Each line can be given a weight, representing some electrical property of the line, such as line admittance or average power flow at a given time. We use a hierarchical spectral clustering methodology to reveal the internal connectivity structure of such a network. Spectral clustering uses the eigenvalues and eigenvectors of a matrix associated to the network, it is computationally very efficient, and it works for any choice of weights. When using line admittances, it reveals the static internal connectivity structure of the underlying network, while using power flows highlights islands with minimal power flow disruption, and thus it naturally relates to controlled islanding. Our methodology goes beyond the standard -means algorithm by instead representing the complete network substructure as a dendrogram. We provide a thorough theoretical justification of the use of spectral clustering in power systems, and we include the results of our methodology for several test systems of small, medium and large size, including a model of the Great Britain transmission network.

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Section: A Rationalementioning

confidence: 99%

Section: A Power Flow Laplacianmentioning

confidence: 99%

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Hierarchical Spectral Clustering of Power Grids

Sánchez-García

Fennelly

Norris

et al. 2014

IEEE Trans. Power Syst.

201

171

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“…Artificial Intelligence (Al) technology, especially Distributed Artificial Intelligence (DAI) [17,18] technology has been recommended strongly in several papers. MAS (Multi-agent System) technology is a major research area in DAI.…”

Section: Technology Back Onmentioning

confidence: 99%

Wide-area Protection and Control System With WAMS Based

Yan

2006

2006 International Conference on Power System Technology

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the paper introduces wide-area protection and emergency control system with the wide area measurement system. The methods for acquisition, transmission, and management of PMU data (Phasor Measurement Units), which have been applied in the engineering, are discussed at first. Then the feasibility application of WAMS data in power system is introduced on voltage stability, transient stability, local protection and SPSS (system PSS), Oscillatory and Angler stability. Some supported technologies are analyzed also.

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“…Although spectral clustering has been widely used in power systems to find intentional controlled islanding solutions with minimal power flow disruption [7]- [9], there is no agreement nor clear reasoning among researchers on which graph Laplacian matrix should be used and why [10]. In this work, we attempt to find an answer to this question.…”

Section: Introductionmentioning

confidence: 99%

Reduced-Order Models of Static Power Grids Based on Spectral Clustering

Baquedano-Aguilar,

Meyn,

Bretas

2023

2023 North American Power Symposium (NAPS)

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For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning. The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piece-wise quadratic approach, then the error distribution is in the order of 10 −3 .

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Strategic Power Infrastructure Defense

Cited by 123 publications

References 50 publications

Hierarchical Spectral Clustering of Power Grids

Hierarchical Spectral Clustering of Power Grids

Wide-area Protection and Control System With WAMS Based

Reduced-Order Models of Static Power Grids Based on Spectral Clustering

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