Postfault optimal islanding of smart grids using a reinforcement learning approach
Abstract This paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-06-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.12815 |
| _version_ | 1797804226743631872 |
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| author | Hamed Rezapour Sadegh Jamali |
| author_facet | Hamed Rezapour Sadegh Jamali |
| author_sort | Hamed Rezapour |
| collection | DOAJ |
| description | Abstract This paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed Energy Resources (DERs). The islanding plan aims to prevent island instability and to help DERs continue supplying the maximum number of loads by the optimal network reconfiguration. Fault isolation is carried out by the protection system and the proposed procedure is commenced right after the fault isolation by controlling the condition of the network remote‐controlled switches. The proposed islanding plan is a novel method by this paper in the management of the postfault conditions of smart grids. Furthermore, a Q‐learning reinforcement approach is presented as the optimization tool because of its great capability and fast response for the determination of optimal reconfiguration. Numerous simulation tests for various fault locations on a 6‐bus and a 33‐bus test networks show the effectiveness of the proposed method in the improvement of postfault network reliability and sustainability. |
| first_indexed | 2024-03-13T05:33:58Z |
| format | Article |
| id | doaj.art-e12a9b3b11c04f51a362208d109d5e62 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-13T05:33:58Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-e12a9b3b11c04f51a362208d109d5e622023-06-14T14:45:14ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-06-0117112471248210.1049/gtd2.12815Postfault optimal islanding of smart grids using a reinforcement learning approachHamed Rezapour0Sadegh Jamali1Centre of Excellence for Power System Automation and Operation, Electrical Engineering Department Iran University of Science and Technology Tehran IranCentre of Excellence for Power System Automation and Operation, Electrical Engineering Department Iran University of Science and Technology Tehran IranAbstract This paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed Energy Resources (DERs). The islanding plan aims to prevent island instability and to help DERs continue supplying the maximum number of loads by the optimal network reconfiguration. Fault isolation is carried out by the protection system and the proposed procedure is commenced right after the fault isolation by controlling the condition of the network remote‐controlled switches. The proposed islanding plan is a novel method by this paper in the management of the postfault conditions of smart grids. Furthermore, a Q‐learning reinforcement approach is presented as the optimization tool because of its great capability and fast response for the determination of optimal reconfiguration. Numerous simulation tests for various fault locations on a 6‐bus and a 33‐bus test networks show the effectiveness of the proposed method in the improvement of postfault network reliability and sustainability.https://doi.org/10.1049/gtd2.12815distributed energy resources (DERs)islandingreinforcement learningreliabilitysmart grid |
| spellingShingle | Hamed Rezapour Sadegh Jamali Postfault optimal islanding of smart grids using a reinforcement learning approach IET Generation, Transmission & Distribution distributed energy resources (DERs) islanding reinforcement learning reliability smart grid |
| title | Postfault optimal islanding of smart grids using a reinforcement learning approach |
| title_full | Postfault optimal islanding of smart grids using a reinforcement learning approach |
| title_fullStr | Postfault optimal islanding of smart grids using a reinforcement learning approach |
| title_full_unstemmed | Postfault optimal islanding of smart grids using a reinforcement learning approach |
| title_short | Postfault optimal islanding of smart grids using a reinforcement learning approach |
| title_sort | postfault optimal islanding of smart grids using a reinforcement learning approach |
| topic | distributed energy resources (DERs) islanding reinforcement learning reliability smart grid |
| url | https://doi.org/10.1049/gtd2.12815 |
| work_keys_str_mv | AT hamedrezapour postfaultoptimalislandingofsmartgridsusingareinforcementlearningapproach AT sadeghjamali postfaultoptimalislandingofsmartgridsusingareinforcementlearningapproach |