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Transient Stability Preventive Control Based on SCINet and IDBO.

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Title: Transient Stability Preventive Control Based on SCINet and IDBO.
Authors: Liu, Songkai1,2 (AUTHOR), Liu, Lei1,2 (AUTHOR) 202408580121885@ctgu.edu.cn, Zhang, Lei1,2 (AUTHOR), Xiong, Xiang1,2 (AUTHOR), Liang, Jinbo1,2 (AUTHOR)
Source: Energies (19961073). Jun2026, Vol. 19 Issue 12, p2824. 32p.
Subject Terms: *Electric power systems, *Dynamic stability, *Feature selection, *Artificial neural networks, *Optimization algorithms, *Machine learning
Abstract: In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194909273
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Transient Stability Preventive Control Based on SCINet and IDBO.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Songkai%22">Liu, Songkai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Lei%22">Liu, Lei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 202408580121885@ctgu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lei%22">Zhang, Lei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiong%2C+Xiang%22">Xiong, Xiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Jinbo%22">Liang, Jinbo</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 12, p2824. 32p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Dynamic+stability%22">Dynamic stability</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
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      – Type: doi
        Value: 10.3390/en19122824
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 32
        StartPage: 2824
    Subjects:
      – SubjectFull: Electric power systems
        Type: general
      – SubjectFull: Dynamic stability
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Transient Stability Preventive Control Based on SCINet and IDBO.
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            NameFull: Liu, Songkai
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            NameFull: Liu, Lei
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            NameFull: Xiong, Xiang
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            NameFull: Liang, Jinbo
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            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19
            – Type: issue
              Value: 12
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            – TitleFull: Energies (19961073)
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