View in EDS HTML Full Text PDF Full Text

Alarm Event Prediction Based on Structural Causal Model in Smart Substation.

Saved in:
Bibliographic Details
Title: Alarm Event Prediction Based on Structural Causal Model in Smart Substation.
Authors: Lu, Xiang1 (AUTHOR), Chen, Youwei2 (AUTHOR), Fu, Yijia1,2 (AUTHOR), Ren, Fang2 (AUTHOR), Ma, Zhonggui2 (AUTHOR) zhongguima@ustb.edu.cn
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2296. 21p.
Subject Terms: *Causal inference, *Situational awareness, *Causal models, *Condition-based maintenance, *Deep learning, *Electric substations, *Fault diagnosis
Abstract: In smart substations, long-term operation and environmental disturbances accelerate equipment aging, often leading to abnormal operating states and frequent alarm events. These alarms provide important early indications of potential equipment faults. Situational awareness technologies offer effective means for real-time monitoring and early warning in substations. Meanwhile, Structural Causal Models (SCMs) can uncover underlying causal relationships in operational data, improving prediction stability and interpretability compared with conventional correlation-based methods. This study proposes a novel situational awareness framework for smart substations that integrates deep learning-based causal inference with expert domain knowledge. By guiding the model with the causal diagram derived from substation alarm data as a strong prior, our method learns causal relationships that are statistically significant. Compared with traditional correlation-based statistical approaches, causal inference enables the explicit modeling and adjustment of potential confounding effects under given assumptions, leading to more reliable relationship estimation and a more interpretable model structure. Finally, a case study using real substation data shows improved predictive performance of the proposed method relative to conventional correlation analysis. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194141411
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Alarm Event Prediction Based on Structural Causal Model in Smart Substation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lu%2C+Xiang%22">Lu, Xiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Youwei%22">Chen, Youwei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Yijia%22">Fu, Yijia</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Fang%22">Ren, Fang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhonggui%22">Ma, Zhonggui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> zhongguima@ustb.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2296. 21p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Causal+inference%22">Causal inference</searchLink><br />*<searchLink fieldCode="DE" term="%22Situational+awareness%22">Situational awareness</searchLink><br />*<searchLink fieldCode="DE" term="%22Causal+models%22">Causal models</searchLink><br />*<searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+substations%22">Electric substations</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In smart substations, long-term operation and environmental disturbances accelerate equipment aging, often leading to abnormal operating states and frequent alarm events. These alarms provide important early indications of potential equipment faults. Situational awareness technologies offer effective means for real-time monitoring and early warning in substations. Meanwhile, Structural Causal Models (SCMs) can uncover underlying causal relationships in operational data, improving prediction stability and interpretability compared with conventional correlation-based methods. This study proposes a novel situational awareness framework for smart substations that integrates deep learning-based causal inference with expert domain knowledge. By guiding the model with the causal diagram derived from substation alarm data as a strong prior, our method learns causal relationships that are statistically significant. Compared with traditional correlation-based statistical approaches, causal inference enables the explicit modeling and adjustment of potential confounding effects under given assumptions, leading to more reliable relationship estimation and a more interpretable model structure. Finally, a case study using real substation data shows improved predictive performance of the proposed method relative to conventional correlation analysis. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141411
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19102296
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 2296
    Subjects:
      – SubjectFull: Causal inference
        Type: general
      – SubjectFull: Situational awareness
        Type: general
      – SubjectFull: Causal models
        Type: general
      – SubjectFull: Condition-based maintenance
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Electric substations
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
    Titles:
      – TitleFull: Alarm Event Prediction Based on Structural Causal Model in Smart Substation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lu, Xiang
      – PersonEntity:
          Name:
            NameFull: Chen, Youwei
      – PersonEntity:
          Name:
            NameFull: Fu, Yijia
      – PersonEntity:
          Name:
            NameFull: Ren, Fang
      – PersonEntity:
          Name:
            NameFull: Ma, Zhonggui
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
ResultId 1