Hierarchical Spectral Clustering of Power Grids
Open accessAbstract
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 k-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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The electrical distance matrix is used for partitioning LV DNs in prior works.
“The electrical distance matrix is used for partitioning LV DNs in [25], [26].”
From Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network (2022) · cites this paper as [26]
The Introduction says electrical distance between nodes is used to define zones when partitioning power networks. But the paper studies transmission-grid test systems, not low-voltage distribution networks, and its own partitioning method uses spectral distance rather than an electrical distance matrix. (LLM, conf=0.85)
Experimental — accuracy not yet measuredA power network can be clustered based on static network parameters such as line impedance/admittance or dynamic parameters such as power flows, line losses, voltage variations.
“A power network can be clustered based on static network parameters such as line impedance/admittance or dynamic parameters such as power flows, line losses, voltage variations [12], [26], [39].”
From Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network (2022) · cites this paper as [26]
The “Weights in Power Grids” section distinguishes static line-admittance weights from dynamic power-flow weights and describes clustering using both. It does not identify line losses or voltage variations as clustering parameters, so those examples are not supported by this source. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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Research works printed but not linked: 27
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Cited by
Sorted by citations
Hashmi, M. U., Koirala, A., Ergun, H., & Van Hertem, D. (2022) · arXiv · doi:10.48550/arxiv.2207.10234 · Cited by 1 · see citing works
- Authors
- Sanchez-Garcia, R. J., Fennelly, M., Norris, S., Wright, N., Niblo, G., Brodzki, J., & Bialek, J. W.
- Affiliations
- University of Southampton (GB)
- Durham University (GB)
- Venue
- IEEE Transactions on Power Systems
- Published
- 2014-09
- Volume
- 29
- Issue
- 5
- Pages
- 2229-2237
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W1992454573
- Cited by
- 262
- References
- 0
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 29 · 2023: 16 · 2022: 28 · 2021: 26 · 2020: 30 · 2019: 23 · 2018: 26 · 2017: 19 · 2016: 12 · 2015: 10 · 2014: 4
- FWCI
- 7.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- Durham Research Online (Durham University) (publishedVersion, other-oa)
- Durham Research Online (Durham University) (acceptedVersion, cc-by)
- ePrints Soton (University of Southampton) (acceptedVersion)
- Durham Research Online (Durham University) (submittedVersion, cc-by)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- Engineering and Physical Sciences Research Council (EP/G060169/1, EP/G059101/1)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- power transmission networks, controlled islanding, power flow, dendrogram, power system reliability
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