Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning
<p dir="ltr">With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures...
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2025
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| _version_ | 1864513538451570688 |
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| author | Mohamed Massaoudi (16888710) |
| author2 | Maymouna Ez Eddin (21633650) Ali Ghrayeb (16864266) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) |
| author2_role | author author author author |
| author_facet | Mohamed Massaoudi (16888710) Maymouna Ez Eddin (21633650) Ali Ghrayeb (16864266) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Massaoudi (16888710) Maymouna Ez Eddin (21633650) Ali Ghrayeb (16864266) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) |
| dc.date.none.fl_str_mv | 2025-01-06T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/oajpe.2025.3535709 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Advancing_Coherent_Power_Grid_Partitioning_A_Review_Embracing_Machine_and_Deep_Learning/30233695 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Decentralized consensus power network partitioning power systems coherency renewable energy integration smart grid |
| dc.title.none.fl_str_mv | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Access Journal of Power and Energy<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/oajpe.2025.3535709" target="_blank">https://dx.doi.org/10.1109/oajpe.2025.3535709</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1ba5ab39bf22863b04eb96f559198e47 |
| identifier_str_mv | 10.1109/oajpe.2025.3535709 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30233695 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep LearningMohamed Massaoudi (16888710)Maymouna Ez Eddin (21633650)Ali Ghrayeb (16864266)Haitham Abu-Rub (16855500)Shady S. Refaat (16864269)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningDecentralized consensuspower network partitioningpower systems coherencyrenewable energy integrationsmart grid<p dir="ltr">With the escalating intricacy and expansion of the interconnected electrical grid, the likelihood of power system (PS) collapse has escalated dramatically. There is an increased emphasis on immunizing renewable-dominated power systems from large-scale cascading failures and cyberattacks through optimal power grid partitioning (PGP). By altering the network’s topology, partitioning aims to create areas within the PS that are not only robust but also have increased flexibility in generation and improved controllability over variable demand. This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. To this end, an in-depth exploration of the basic principles of PGP and performance quantification is provided. The coherency adequacy and controlled islanding within the power network are comprehensively discussed. Subsequently, state-of-the-art research that envisions the use of clustering-based machine learning and deep learning-based solutions for PGP is presented. Finally, key research gaps and future directions for effective PGP are outlined. This paper provides PS researchers with a bird’s eye view of the current state of mainstream PGP implementations. Additionally, it assists stakeholders in selecting the most appropriate clustering algorithms for PGP applications.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Access Journal of Power and Energy<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/oajpe.2025.3535709" target="_blank">https://dx.doi.org/10.1109/oajpe.2025.3535709</a></p>2025-01-06T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/oajpe.2025.3535709https://figshare.com/articles/journal_contribution/Advancing_Coherent_Power_Grid_Partitioning_A_Review_Embracing_Machine_and_Deep_Learning/30233695CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/302336952025-01-06T03:00:00Z |
| spellingShingle | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning Mohamed Massaoudi (16888710) Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Decentralized consensus power network partitioning power systems coherency renewable energy integration smart grid |
| status_str | publishedVersion |
| title | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| title_full | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| title_fullStr | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| title_full_unstemmed | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| title_short | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| title_sort | Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Decentralized consensus power network partitioning power systems coherency renewable energy integration smart grid |