Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation Article Swipe
Maymouna Ez Eddin
,
Mohamed Massaoudi
,
Haitham Abu‐Rub
,
Mohammad B. Shadmand
,
Mohamed Abdallah
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tpwrs.2024.3361313
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tpwrs.2024.3361313
Due to coupling and correlation between nodes and buses in the power system, Power Grid Partitioning (PGP) is a promising approach to analyze large power systems and provide timely actions during disturbances. From this perspective, this paper proposes an efficient framework for fast and optimal PGP, based on singular value decomposition analysis of the graph's Laplacian. An Affinity Propagation clustering algorithm-based PGP is tailored for automatically forming highly interconnected clusters based on pairwise similarities without requiring a predefined number of partitions. The core objective is to quantify the clustering performance based on internal clustering validity indices, such as the Silhouette Index, Calinski-Harabasz Index, and Davies-Bouldin Index. The adopted methodology aims to enhance partitioning efficiency substantially while preserving a high level of partitioning quality. The proposed framework is verified on IEEE 14, 39, 118, and 2000-bus systems and compared to nine other well-known and widely used clustering techniques, including K-Means and Gaussian Mixture models. The simulation results demonstrate the scalability of the proposed approach and its high-quality partitioning output with a Silhouette index of 0.6162, 0.6597, 0.6664, and 0.6555 for the IEEE 14, 39, 118, and 2000-bus systems, respectively. Other InformationPublished in: IEEE Transactions on Power SystemsLicense: https://creativecommons.org/licenses/by/4.0/See article on publisher's website: https://dx.doi.org/10.1109/tpwrs.2024.3361313
Related Topics
Concepts
Cluster analysis
Partition (number theory)
Graph partition
Affinity propagation
Computer science
Electric power system
Scalability
Singular value decomposition
Silhouette
Data mining
Algorithm
Mathematical optimization
Graph
Power (physics)
Correlation clustering
Mathematics
Canopy clustering algorithm
Theoretical computer science
Artificial intelligence
Physics
Quantum mechanics
Database
Combinatorics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tpwrs.2024.3361313
- https://ieeexplore.ieee.org/ielx7/59/4374138/10418487.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391454498
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391454498Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tpwrs.2024.3361313Digital Object Identifier
- Title
-
Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity PropagationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-01Full publication date if available
- Authors
-
Maymouna Ez Eddin, Mohamed Massaoudi, Haitham Abu‐Rub, Mohammad B. Shadmand, Mohamed AbdallahList of authors in order
- Landing page
-
https://doi.org/10.1109/tpwrs.2024.3361313Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/59/4374138/10418487.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/59/4374138/10418487.pdfDirect OA link when available
- Concepts
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Cluster analysis, Partition (number theory), Graph partition, Affinity propagation, Computer science, Electric power system, Scalability, Singular value decomposition, Silhouette, Data mining, Algorithm, Mathematical optimization, Graph, Power (physics), Correlation clustering, Mathematics, Canopy clustering algorithm, Theoretical computer science, Artificial intelligence, Physics, Quantum mechanics, Database, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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