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A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems.

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Title: A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems.
Authors: Li, Tairan1 (AUTHOR), Wang, Qingpeng2 (AUTHOR) 13095011683@163.com, Yang, Gao2 (AUTHOR), Huang, Qiang2 (AUTHOR), Ren, Qingqing2 (AUTHOR), Yang, Hao2 (AUTHOR), Jie, Huamin (AUTHOR) jieh0002@e.ntu.edu.sg
Source: International Transactions on Electrical Energy Systems. 7/11/2026, Vol. 2026, p1-16. 16p.
Subject Terms: *Alarms, *System failures, *Smart power grids, *Long short-term memory, *Machine learning, *Stability criterion, *Detection alarms
Abstract: Recent developments and the rapid expansion of modern power grids have led to alarm flooding. This means that operators receive such a large volume of alarms that they become overwhelmed and stall in inquiry, resulting in mistakes. To better address this issue, an innovative, multifaceted, and hierarchical framework is proposed that combines stability signals and failure event information for intelligent alarm classification and screening. The UCI electrical grid stability and the power system faults datasets, both open source datasets, are used in conjunction with the synthetic multimodal fusion dataset for comprehensive performance evaluation. We benchmark baseline models, including random forest, extreme gradient boosting, and CNN–LSTM, on unimodal datasets. The CNN–LSTM gives us the best temporal stability prediction. At the same time, the XGB has the best heterogeneous fault classification. The proposed hierarchical multimodal model achieves an accuracy of 0.97, an F1 score of 0.96, and an ROC‐AUC of 0.98 for the combination of anomaly detection and hybrid CNN–LSTM–XGB fusion. These results demonstrate that hierarchical multimodal learning provides a scalable, intelligent solution for alarm classification and screening in next‐generation cyberphysical power grid systems. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 195289950
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Tairan%22">Li, Tairan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Qingpeng%22">Wang, Qingpeng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 13095011683@163.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Gao%22">Yang, Gao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Qiang%22">Huang, Qiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ren%2C+Qingqing%22">Ren, Qingqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Hao%22">Yang, Hao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jie%2C+Huamin%22">Jie, Huamin</searchLink> (AUTHOR)<i> jieh0002@e.ntu.edu.sg</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Transactions+on+Electrical+Energy+Systems%22">International Transactions on Electrical Energy Systems</searchLink>. 7/11/2026, Vol. 2026, p1-16. 16p.
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  Data: *<searchLink fieldCode="DE" term="%22Alarms%22">Alarms</searchLink><br />*<searchLink fieldCode="DE" term="%22System+failures%22">System failures</searchLink><br />*<searchLink fieldCode="DE" term="%22Smart+power+grids%22">Smart power grids</searchLink><br />*<searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Stability+criterion%22">Stability criterion</searchLink><br />*<searchLink fieldCode="DE" term="%22Detection+alarms%22">Detection alarms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Recent developments and the rapid expansion of modern power grids have led to alarm flooding. This means that operators receive such a large volume of alarms that they become overwhelmed and stall in inquiry, resulting in mistakes. To better address this issue, an innovative, multifaceted, and hierarchical framework is proposed that combines stability signals and failure event information for intelligent alarm classification and screening. The UCI electrical grid stability and the power system faults datasets, both open source datasets, are used in conjunction with the synthetic multimodal fusion dataset for comprehensive performance evaluation. We benchmark baseline models, including random forest, extreme gradient boosting, and CNN–LSTM, on unimodal datasets. The CNN–LSTM gives us the best temporal stability prediction. At the same time, the XGB has the best heterogeneous fault classification. The proposed hierarchical multimodal model achieves an accuracy of 0.97, an F1 score of 0.96, and an ROC‐AUC of 0.98 for the combination of anomaly detection and hybrid CNN–LSTM–XGB fusion. These results demonstrate that hierarchical multimodal learning provides a scalable, intelligent solution for alarm classification and screening in next‐generation cyberphysical power grid systems. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1155/etep/5528391
    Languages:
      – Code: eng
        Text: English
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        PageCount: 16
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      – SubjectFull: Alarms
        Type: general
      – SubjectFull: System failures
        Type: general
      – SubjectFull: Smart power grids
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Stability criterion
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      – SubjectFull: Detection alarms
        Type: general
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      – TitleFull: A Hierarchical Multimodal AI Framework for Intelligent Alarm Handling in Smart Power Grid Monitoring Systems.
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            NameFull: Li, Tairan
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            NameFull: Wang, Qingpeng
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            NameFull: Yang, Gao
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            NameFull: Ren, Qingqing
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            – D: 11
              M: 07
              Text: 7/11/2026
              Type: published
              Y: 2026
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              Value: 20507038
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              Value: 2026
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            – TitleFull: International Transactions on Electrical Energy Systems
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