Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation
<p dir="ltr">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 frame...
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2024
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| _version_ | 1864513545054453760 |
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| author | Maymouna Ez Eddin (21633650) |
| author2 | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Mohammad Shadmand (19672585) Mohamed Abdallah (3073191) |
| author2_role | author author author author |
| author_facet | Maymouna Ez Eddin (21633650) Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Mohammad Shadmand (19672585) Mohamed Abdallah (3073191) |
| author_role | author |
| dc.creator.none.fl_str_mv | Maymouna Ez Eddin (21633650) Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Mohammad Shadmand (19672585) Mohamed Abdallah (3073191) |
| dc.date.none.fl_str_mv | 2024-09-05T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/tpwrs.2024.3361313 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimum_Partition_of_Power_Networks_Using_Singular_Value_Decomposition_and_Affinity_Propagation/29446151 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Machine learning Clustering algorithms complex networks grid partitioning machine learning power system analysis Partitioning algorithms Power grids Laplace equations Complex networks Machine learning Power system analysis computing |
| dc.title.none.fl_str_mv | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Power Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tpwrs.2024.3361313" target="_blank">https://dx.doi.org/10.1109/tpwrs.2024.3361313</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_acf6b69a00bd5e1db4ff2be20abc7422 |
| identifier_str_mv | 10.1109/tpwrs.2024.3361313 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29446151 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity PropagationMaymouna Ez Eddin (21633650)Mohamed Massaoudi (16888710)Haitham Abu-Rub (16855500)Mohammad Shadmand (19672585)Mohamed Abdallah (3073191)EngineeringElectrical engineeringInformation and computing sciencesMachine learningClustering algorithmscomplex networksgrid partitioningmachine learningpower system analysisPartitioning algorithmsPower gridsLaplace equationsComplex networksMachine learningPower system analysis computing<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Transactions on Power Systems<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/tpwrs.2024.3361313" target="_blank">https://dx.doi.org/10.1109/tpwrs.2024.3361313</a></p>2024-09-05T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/tpwrs.2024.3361313https://figshare.com/articles/journal_contribution/Optimum_Partition_of_Power_Networks_Using_Singular_Value_Decomposition_and_Affinity_Propagation/29446151CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294461512024-09-05T03:00:00Z |
| spellingShingle | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation Maymouna Ez Eddin (21633650) Engineering Electrical engineering Information and computing sciences Machine learning Clustering algorithms complex networks grid partitioning machine learning power system analysis Partitioning algorithms Power grids Laplace equations Complex networks Machine learning Power system analysis computing |
| status_str | publishedVersion |
| title | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| title_full | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| title_fullStr | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| title_full_unstemmed | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| title_short | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| title_sort | Optimum Partition of Power Networks Using Singular Value Decomposition and Affinity Propagation |
| topic | Engineering Electrical engineering Information and computing sciences Machine learning Clustering algorithms complex networks grid partitioning machine learning power system analysis Partitioning algorithms Power grids Laplace equations Complex networks Machine learning Power system analysis computing |