Academic Journal
Stochastic load frequency control of power systems via Gaussian processes.
| Τίτλος: | Stochastic load frequency control of power systems via Gaussian processes. |
|---|---|
| Συγγραφείς: | Ma, Tong, Barajas-Solano, David Alonso, Tartakovsky, Alexandre M. |
| Πηγή: | International Journal of Control; May2026, Vol. 99 Issue 5, p1279-1294, 16p |
| Θεματικοί όροι: | Gaussian processes, Stochastic programming, Research methodology, Uncertainty (Information theory), Predictive control systems, Electric power systems, Wind power |
| Περίληψη: | To enhance the safety and efficiency of the power grid system, a finite-horizon chance constrained optimisation problem is formulated to suppress the load frequency deviation resulting from stochastic uncertainties (e.g. wind energies and load disturbances) and to reduce the mechanical power cost, meanwhile maintaining quality specifications. Especially, using a scenario-based approach, Gaussian process models are built to quantify stochastic uncertainties and to evaluate the model cost and constraint functions over the prediction horizon, which yields a tractable stochastic nonlinear model predictive control (SNMPC) framework for handling chance constrained load frequency control problems with Gaussian parametric uncertainties. Comparative study between the GP-SNMPC framework and scenario generation SMPC framework is carried out, which demonstrates that the GP-SNMPC framework is more computationally efficient and delivers a better performance in keeping load frequency balance while maintaining the system constraints. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Stochastic load frequency control of power systems via Gaussian processes. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Tong%22">Ma, Tong</searchLink><br /><searchLink fieldCode="AR" term="%22Barajas-Solano%2C+David+Alonso%22">Barajas-Solano, David Alonso</searchLink><br /><searchLink fieldCode="AR" term="%22Tartakovsky%2C+Alexandre+M%2E%22">Tartakovsky, Alexandre M.</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Control; May2026, Vol. 99 Issue 5, p1279-1294, 16p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+programming%22">Stochastic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Research+methodology%22">Research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Uncertainty+%28Information+theory%29%22">Uncertainty (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To enhance the safety and efficiency of the power grid system, a finite-horizon chance constrained optimisation problem is formulated to suppress the load frequency deviation resulting from stochastic uncertainties (e.g. wind energies and load disturbances) and to reduce the mechanical power cost, meanwhile maintaining quality specifications. Especially, using a scenario-based approach, Gaussian process models are built to quantify stochastic uncertainties and to evaluate the model cost and constraint functions over the prediction horizon, which yields a tractable stochastic nonlinear model predictive control (SNMPC) framework for handling chance constrained load frequency control problems with Gaussian parametric uncertainties. Comparative study between the GP-SNMPC framework and scenario generation SMPC framework is carried out, which demonstrates that the GP-SNMPC framework is more computationally efficient and delivers a better performance in keeping load frequency balance while maintaining the system constraints. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Control is the property of Taylor & Francis Ltd 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: 10.1080/00207179.2025.2568588 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1279 Subjects: – SubjectFull: Gaussian processes Type: general – SubjectFull: Stochastic programming Type: general – SubjectFull: Research methodology Type: general – SubjectFull: Uncertainty (Information theory) Type: general – SubjectFull: Predictive control systems Type: general – SubjectFull: Electric power systems Type: general – SubjectFull: Wind power Type: general Titles: – TitleFull: Stochastic load frequency control of power systems via Gaussian processes. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Tong – PersonEntity: Name: NameFull: Barajas-Solano, David Alonso – PersonEntity: Name: NameFull: Tartakovsky, Alexandre M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207179 Numbering: – Type: volume Value: 99 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Control Type: main |
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