Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review
<p dir="ltr">The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-ed...
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| _version_ | 1864513527067181056 |
|---|---|
| author | Mohamed Sadok Massaoudi (17984071) |
| author2 | Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| author2_role | author author |
| author_facet | Mohamed Sadok Massaoudi (17984071) Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Sadok Massaoudi (17984071) Haitham Abu-Rub (16855500) Ali Ghrayeb (16864266) |
| dc.date.none.fl_str_mv | 2023-11-27T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3337118 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Navigating_the_Landscape_of_Deep_Reinforcement_Learning_for_Power_System_Stability_Control_A_Review/25239751 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Power system stability Stability analysis Uncertainty Optimization Smart grids Heuristic algorithms Reinforcement learning Deep learning Security management Deep reinforcement learning dynamic security control electric power systems |
| dc.title.none.fl_str_mv | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) breakthroughs have been in the spotlight over the last few years with potential contributions to PS stability (PSS). The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. Furthermore, this paper highlights the theoretical advantages and the key tradeoffs of the emerging DRL techniques as powerful tools for optimal power flow. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in large-scale PSS is also presented. This review aims to support research in this area of DRL algorithms to embrace PSS against unseen faults and different PS topologies.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" 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/access.2023.3337118" target="_blank">https://dx.doi.org/10.1109/access.2023.3337118</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2d6fe7faf167251d4b2a7b5ac0db6a4f |
| identifier_str_mv | 10.1109/access.2023.3337118 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25239751 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A ReviewMohamed Sadok Massaoudi (17984071)Haitham Abu-Rub (16855500)Ali Ghrayeb (16864266)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringPower system stabilityStability analysisUncertaintyOptimizationSmart gridsHeuristic algorithmsReinforcement learningDeep learningSecurity managementDeep reinforcement learningdynamic security controlelectric power systems<p dir="ltr">The widespread penetration of inverter-based resources has profoundly impacted the electrical stability of power systems (PSs). Deepening grid integration of photovoltaic and wind systems is introducing unforeseen uncertainties for the electricity sector. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) breakthroughs have been in the spotlight over the last few years with potential contributions to PS stability (PSS). The ubiquitous DRL architecture, by learning from the dynamism inherent in PSs, produces near-optimal actions for PSS. This article provides a rigorous review of the latest research efforts focused on DRL to derive PSS policies while accounting for the unique properties of power grids. Furthermore, this paper highlights the theoretical advantages and the key tradeoffs of the emerging DRL techniques as powerful tools for optimal power flow. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in large-scale PSS is also presented. This review aims to support research in this area of DRL algorithms to embrace PSS against unseen faults and different PS topologies.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" 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/access.2023.3337118" target="_blank">https://dx.doi.org/10.1109/access.2023.3337118</a></p>2023-11-27T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3337118https://figshare.com/articles/journal_contribution/Navigating_the_Landscape_of_Deep_Reinforcement_Learning_for_Power_System_Stability_Control_A_Review/25239751CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252397512023-11-27T09:00:00Z |
| spellingShingle | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review Mohamed Sadok Massaoudi (17984071) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Power system stability Stability analysis Uncertainty Optimization Smart grids Heuristic algorithms Reinforcement learning Deep learning Security management Deep reinforcement learning dynamic security control electric power systems |
| status_str | publishedVersion |
| title | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| title_full | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| title_fullStr | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| title_full_unstemmed | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| title_short | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| title_sort | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Power system stability Stability analysis Uncertainty Optimization Smart grids Heuristic algorithms Reinforcement learning Deep learning Security management Deep reinforcement learning dynamic security control electric power systems |