Integrated Transfer Learning and Reinforcement Learning for Reactive Current Injection During Voltage Sags.
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| Title: | Integrated Transfer Learning and Reinforcement Learning for Reactive Current Injection During Voltage Sags. |
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| Authors: | Fathollahi, Mohana1 (AUTHOR) mohana.fathollahi@upc.edu, Santiago, Antonio Camacho2 (AUTHOR), Angulo, Cecilio1,2 (AUTHOR) |
| Source: | Energies (19961073). Jun2026, Vol. 19 Issue 12, p2908. 16p. |
| Subject Terms: | *Reinforcement learning, *Reactive power, *Knowledge transfer, *Electric power system stability, *Real-time control |
| Abstract: | Modern power grids with high renewable energy penetration are vulnerable to fast voltage disturbances caused by grid faults. Among these, voltage sags are critical because they develop within milliseconds and require rapid reactive current support to maintain grid stability and power reliability. Reinforcement learning has previously shown potential for reactive current injection control during voltage sag events due to its fast response and adaptability to changing system conditions. However, existing approaches rely on separate policies for specific subsets of the operating space, which limits their ability to provide optimal actions when the system operates across broader or combined state regions. To address this limitation, this paper proposes a unified Soft Actor–Critic (SAC) target policy trained over the full state and action space by integrating multi-source transfer learning with potential-based reward shaping approach. Results show that the proposed multi-source transfer approach enables the target agent to converge faster and reach a higher reward solution than the baseline SAC and single-source transfer approach. The trained policy also improved prediction accuracy, achieving reactive-current errors below 0.2 A with respect to the ground-truth reference generated through extensive simulations over the full observation and action space. The reference follows the grid-code requirement for minimum reactive current injection during faults and provides a benchmark for evaluating prediction accuracy. This can help distributed generation sources respond more effectively during severe perturbations such as voltage sags, support voltage recovery, and reduce the risk of cascaded disconnections that could lead to unwanted blackouts. Additionally, the inference execution time is also sufficiently fast to satisfy the response-time requirement of voltage sag events, confirming the real-time feasibility of the proposed controller. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194909357 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Integrated Transfer Learning and Reinforcement Learning for Reactive Current Injection During Voltage Sags. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fathollahi%2C+Mohana%22">Fathollahi, Mohana</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mohana.fathollahi@upc.edu</i><br /><searchLink fieldCode="AR" term="%22Santiago%2C+Antonio+Camacho%22">Santiago, Antonio Camacho</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Angulo%2C+Cecilio%22">Angulo, Cecilio</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2908. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Reactive+power%22">Reactive power</searchLink><br />*<searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+stability%22">Electric power system stability</searchLink><br />*<searchLink fieldCode="DE" term="%22Real-time+control%22">Real-time control</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Modern power grids with high renewable energy penetration are vulnerable to fast voltage disturbances caused by grid faults. Among these, voltage sags are critical because they develop within milliseconds and require rapid reactive current support to maintain grid stability and power reliability. Reinforcement learning has previously shown potential for reactive current injection control during voltage sag events due to its fast response and adaptability to changing system conditions. However, existing approaches rely on separate policies for specific subsets of the operating space, which limits their ability to provide optimal actions when the system operates across broader or combined state regions. To address this limitation, this paper proposes a unified Soft Actor–Critic (SAC) target policy trained over the full state and action space by integrating multi-source transfer learning with potential-based reward shaping approach. Results show that the proposed multi-source transfer approach enables the target agent to converge faster and reach a higher reward solution than the baseline SAC and single-source transfer approach. The trained policy also improved prediction accuracy, achieving reactive-current errors below 0.2 A with respect to the ground-truth reference generated through extensive simulations over the full observation and action space. The reference follows the grid-code requirement for minimum reactive current injection during faults and provides a benchmark for evaluating prediction accuracy. This can help distributed generation sources respond more effectively during severe perturbations such as voltage sags, support voltage recovery, and reduce the risk of cascaded disconnections that could lead to unwanted blackouts. Additionally, the inference execution time is also sufficiently fast to satisfy the response-time requirement of voltage sag events, confirming the real-time feasibility of the proposed controller. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194909357 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19122908 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2908 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Reactive power Type: general – SubjectFull: Knowledge transfer Type: general – SubjectFull: Electric power system stability Type: general – SubjectFull: Real-time control Type: general Titles: – TitleFull: Integrated Transfer Learning and Reinforcement Learning for Reactive Current Injection During Voltage Sags. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fathollahi, Mohana – PersonEntity: Name: NameFull: Santiago, Antonio Camacho – PersonEntity: Name: NameFull: Angulo, Cecilio IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 12 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
