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Comparative Review of Reactive Power Estimation Techniques for Voltage Restoration.

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Title: Comparative Review of Reactive Power Estimation Techniques for Voltage Restoration.
Authors: Faleiro, Natanael1 (AUTHOR) natanaelfaleiro@gmail.com, Monteiro, Raul1,2 (AUTHOR), Fonseca, André2,3 (AUTHOR), Negrete, Lina1 (AUTHOR), Lima, Rogério3 (AUTHOR), Bonaldo, Jakson3 (AUTHOR)
Source: Energies (19961073). Feb2026, Vol. 19 Issue 3, p826. 36p.
Subject Terms: *Reactive power control, *Machine learning, *Mathematical optimization, *Voltage regulators, *Distributed resources (Electric utilities), *Numerical analysis
Abstract: With the focus on the growing concern of voltage instability and its inherent risks connected to blackouts, this study addresses the importance of Volt/VAR control (VVC) in maintaining voltage stability, optimizing power factor, and reducing losses. As such, this scientific article presents a review of the methodologies used to estimate the quantity of reactive power required to restore voltage in power grids. Although reviews exist on classical methods, optimization, and machine learning, a study unifying these approaches is lacking. This gap hinders an integrated comparison of methodologies and constitutes the main motivation for this study in 2025. This absence of a consolidated and up-to-date review limits both academic progress and practical decision-making in modern power systems, especially as DER penetration accelerates. This research was conducted using the Scopus database through the selection of articles that address reactive power estimation methods. The results indicate that traditional numerical and optimization methods, although accurate, demonstrate high computational costs for real-time application. In contrast, techniques such as Deep Reinforcement Learning (DRL) and hybrid models show greater potential for dealing with uncertainties and dynamic topologies. The conclusion reached is that the solution for reactive power management lies in hybrid approaches, which combine machine learning with numerical methods, supported by an intelligent and robust data infrastructure. The comparative analysis shows that numerical methods offer high precision but are computationally expensive for real-time use; optimization techniques provide good robustness but depend on detailed models that are sensitive to system conditions; and machine learning-based approaches offer greater adaptability under uncertainty, although they require large datasets and careful training. Given these complementary limitations, hybrid approaches emerge as the most promising alternative, combining the reliability of classical methods with the flexibility of intelligent models, especially in smart grids with dynamic topologies and high penetration of Distributed Energy Resources (DERs). [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 191587305
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Comparative Review of Reactive Power Estimation Techniques for Voltage Restoration.
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  Data: <searchLink fieldCode="AR" term="%22Faleiro%2C+Natanael%22">Faleiro, Natanael</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> natanaelfaleiro@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Monteiro%2C+Raul%22">Monteiro, Raul</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fonseca%2C+André%22">Fonseca, André</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Negrete%2C+Lina%22">Negrete, Lina</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lima%2C+Rogério%22">Lima, Rogério</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bonaldo%2C+Jakson%22">Bonaldo, Jakson</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Feb2026, Vol. 19 Issue 3, p826. 36p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Reactive+power+control%22">Reactive power control</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Voltage+regulators%22">Voltage regulators</searchLink><br />*<searchLink fieldCode="DE" term="%22Distributed+resources+%28Electric+utilities%29%22">Distributed resources (Electric utilities)</searchLink><br />*<searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the focus on the growing concern of voltage instability and its inherent risks connected to blackouts, this study addresses the importance of Volt/VAR control (VVC) in maintaining voltage stability, optimizing power factor, and reducing losses. As such, this scientific article presents a review of the methodologies used to estimate the quantity of reactive power required to restore voltage in power grids. Although reviews exist on classical methods, optimization, and machine learning, a study unifying these approaches is lacking. This gap hinders an integrated comparison of methodologies and constitutes the main motivation for this study in 2025. This absence of a consolidated and up-to-date review limits both academic progress and practical decision-making in modern power systems, especially as DER penetration accelerates. This research was conducted using the Scopus database through the selection of articles that address reactive power estimation methods. The results indicate that traditional numerical and optimization methods, although accurate, demonstrate high computational costs for real-time application. In contrast, techniques such as Deep Reinforcement Learning (DRL) and hybrid models show greater potential for dealing with uncertainties and dynamic topologies. The conclusion reached is that the solution for reactive power management lies in hybrid approaches, which combine machine learning with numerical methods, supported by an intelligent and robust data infrastructure. The comparative analysis shows that numerical methods offer high precision but are computationally expensive for real-time use; optimization techniques provide good robustness but depend on detailed models that are sensitive to system conditions; and machine learning-based approaches offer greater adaptability under uncertainty, although they require large datasets and careful training. Given these complementary limitations, hybrid approaches emerge as the most promising alternative, combining the reliability of classical methods with the flexibility of intelligent models, especially in smart grids with dynamic topologies and high penetration of Distributed Energy Resources (DERs). [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19030826
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      – Code: eng
        Text: English
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        PageCount: 36
        StartPage: 826
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      – SubjectFull: Reactive power control
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Voltage regulators
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      – SubjectFull: Distributed resources (Electric utilities)
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      – SubjectFull: Numerical analysis
        Type: general
    Titles:
      – TitleFull: Comparative Review of Reactive Power Estimation Techniques for Voltage Restoration.
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            NameFull: Monteiro, Raul
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 19
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