Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning
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 opti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
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| Online Access: | https://ieeexplore.ieee.org/document/10855832/ |
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| author | Mohamed Massaoudi Maymouna Ez Eddin Ali Ghrayeb Haitham Abu-Rub Shady S. Refaat |
| author_facet | Mohamed Massaoudi Maymouna Ez Eddin Ali Ghrayeb Haitham Abu-Rub Shady S. Refaat |
| author_sort | Mohamed Massaoudi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d759de6584a14ccb97e17e5413ee8440 |
| institution | Kabale University |
| issn | 2687-7910 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj-art-d759de6584a14ccb97e17e5413ee84402025-02-07T00:02:00ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102025-01-0112597510.1109/OAJPE.2025.353570910855832Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep LearningMohamed Massaoudi0https://orcid.org/0000-0002-9388-2115Maymouna Ez Eddin1Ali Ghrayeb2https://orcid.org/0000-0002-6808-5886Haitham Abu-Rub3https://orcid.org/0000-0001-8687-3942Shady S. Refaat4https://orcid.org/0000-0001-9392-6141Department of Electrical Engineering, Texas A&M University, College Station, TX, USADepartment of Electrical Engineering, Texas A&M University, College Station, TX, USADepartment of Electrical Engineering, Texas A&M University at Qatar, Doha, QatarDepartment of Electrical Engineering, Texas A&M University at Qatar, Doha, QatarSchool of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, U.K.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.https://ieeexplore.ieee.org/document/10855832/Decentralized consensuspower network partitioningpower systems coherencyrenewable energy integrationsmart grid |
| spellingShingle | Mohamed Massaoudi Maymouna Ez Eddin Ali Ghrayeb Haitham Abu-Rub Shady S. Refaat Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning IEEE Open Access Journal of Power and Energy Decentralized consensus power network partitioning power systems coherency renewable energy integration smart grid |
| 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 | Decentralized consensus power network partitioning power systems coherency renewable energy integration smart grid |
| url | https://ieeexplore.ieee.org/document/10855832/ |
| work_keys_str_mv | AT mohamedmassaoudi advancingcoherentpowergridpartitioningareviewembracingmachineanddeeplearning AT maymounaezeddin advancingcoherentpowergridpartitioningareviewembracingmachineanddeeplearning AT alighrayeb advancingcoherentpowergridpartitioningareviewembracingmachineanddeeplearning AT haithamaburub advancingcoherentpowergridpartitioningareviewembracingmachineanddeeplearning AT shadysrefaat advancingcoherentpowergridpartitioningareviewembracingmachineanddeeplearning |