Optimizing intelligent startup strategy of power system using PPO algorithm.
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| Titel: | Optimizing intelligent startup strategy of power system using PPO algorithm. |
|---|---|
| Autoren: | Sun, Yan, Wu, Yin, Wu, Yan, Liang, Kai, Dong, Cuihong, Liu, Shiwei |
| Quelle: | Intelligent Decision Technologies; 2024, Vol. 18 Issue 4, p3091-3104, 14p |
| Schlagwörter: | MACHINE learning, ELECTRIC lines, MATHEMATICAL optimization, RENEWABLE energy sources, DYNAMIC models |
| Abstract: | This article aimed to use the proximal policy optimization (PPO) algorithm to address the limitations of power system startup strategies, to enhance the adaptability, coping ability, and overall robustness of the system to variable grid demand and integrated renewable energy, the constraints in the power system start-up strategy are optimized. Firstly, this article constructed a dynamic model of the power system, including key components such as generators, transformers, and transmission lines; secondly, it integrated the PPO algorithm and designed interfaces that allow the algorithm to interact with the power system model; afterward, the state variables of the power system were determined, and a reward function was designed to evaluate the startup efficiency and stability of the system. Next, this article adjusted the reward function and trained and iterated multiple times in the simulation environment to guide the algorithm to learn the optimal startup strategy. Finally, an effective evaluation of the strategy can be conducted. The research results showed that after optimization by the PPO algorithm, the stable frequency startup of the power system only took about 23 seconds, and the system recovery time was reduced by 33.3% under a sudden load increase. The algorithm used can significantly optimize the intelligent startup strategy of the power system. [ABSTRACT FROM AUTHOR] |
| Copyright of Intelligent Decision Technologies is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Datenbank: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 181971821 RelevancyScore: 1007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1007.05755615234 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimizing intelligent startup strategy of power system using PPO algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Yan%22">Sun, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yin%22">Wu, Yin</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Yan%22">Wu, Yan</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+Kai%22">Liang, Kai</searchLink><br /><searchLink fieldCode="AR" term="%22Dong%2C+Cuihong%22">Dong, Cuihong</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Shiwei%22">Liu, Shiwei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Intelligent Decision Technologies; 2024, Vol. 18 Issue 4, p3091-3104, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTRIC+lines%22">ELECTRIC lines</searchLink><br /><searchLink fieldCode="DE" term="%22MATHEMATICAL+optimization%22">MATHEMATICAL optimization</searchLink><br /><searchLink fieldCode="DE" term="%22RENEWABLE+energy+sources%22">RENEWABLE energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22DYNAMIC+models%22">DYNAMIC models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article aimed to use the proximal policy optimization (PPO) algorithm to address the limitations of power system startup strategies, to enhance the adaptability, coping ability, and overall robustness of the system to variable grid demand and integrated renewable energy, the constraints in the power system start-up strategy are optimized. Firstly, this article constructed a dynamic model of the power system, including key components such as generators, transformers, and transmission lines; secondly, it integrated the PPO algorithm and designed interfaces that allow the algorithm to interact with the power system model; afterward, the state variables of the power system were determined, and a reward function was designed to evaluate the startup efficiency and stability of the system. Next, this article adjusted the reward function and trained and iterated multiple times in the simulation environment to guide the algorithm to learn the optimal startup strategy. Finally, an effective evaluation of the strategy can be conducted. The research results showed that after optimization by the PPO algorithm, the stable frequency startup of the power system only took about 23 seconds, and the system recovery time was reduced by 33.3% under a sudden load increase. The algorithm used can significantly optimize the intelligent startup strategy of the power system. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Intelligent Decision Technologies is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3233/IDT-240122 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 3091 Subjects: – SubjectFull: MACHINE learning Type: general – SubjectFull: ELECTRIC lines Type: general – SubjectFull: MATHEMATICAL optimization Type: general – SubjectFull: RENEWABLE energy sources Type: general – SubjectFull: DYNAMIC models Type: general Titles: – TitleFull: Optimizing intelligent startup strategy of power system using PPO algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Yan – PersonEntity: Name: NameFull: Wu, Yin – PersonEntity: Name: NameFull: Wu, Yan – PersonEntity: Name: NameFull: Liang, Kai – PersonEntity: Name: NameFull: Dong, Cuihong – PersonEntity: Name: NameFull: Liu, Shiwei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 18724981 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Intelligent Decision Technologies Type: main |
| ResultId | 1 |