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Research on Power Grid Accident Analysis and Early Warning Model Based on Meteorological Factors.

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Title: Research on Power Grid Accident Analysis and Early Warning Model Based on Meteorological Factors.
Authors: Li, Haoyu1 (AUTHOR) lihaoyu@mail.shiep.edu.cn, Yang, Xiu1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2288. 28p.
Subject Terms: *Electric power system faults, *Prediction models, *Autoencoders, *Model validation, *Fault diagnosis, *Weather, *Ensemble learning
Geographic Terms: East Asia
Abstract: Natural disasters and extreme meteorological events are primary causes of unplanned outages in modern power systems. Existing early warning methods suffer from insufficient non-linear feature extraction, severe class imbalance, and limited minority-class recall under single-classifier architectures. This paper proposes a seven-class meteorological fault early warning framework that integrates a sparse autoencoder (SAE), a G1–entropy composite weighting scheme, SMOTE oversampling, and a soft-voting BP–XGBoost ensemble. A leakage-free experimental protocol confines SMOTE exclusively to the training partition, eliminating data contamination from evaluation. Validated on 1955 fault records from a regional grid in East China covering 110 kV, 220 kV, and 500 kV voltage levels (2013–2022), the proposed framework achieved 96.42% accuracy and a 97.46% macro F1-score on the held-out test set, outperforming SVM (72.68%), Random Forest (89.31%), LSTM (81.47%), 1D-CNN (85.38%), and LightGBM (92.15%). Ablation experiments confirmed that SMOTE and G1–entropy weighting contributed macro F1 gains of 8.34 and 6.91 percentage points, respectively, while removing the XGBoost branch degraded accuracy by 28.25%. Temporal validation on 2019–2022 records yielded 91.57% accuracy, confirming temporal generalization. Error analysis further revealed that bidirectional misclassification between lightning damage and wind damage, rooted in shared atmospheric instability signatures, constitutes the dominant residual error source, providing theoretical guidance for future threshold optimization strategies. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141403
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on Power Grid Accident Analysis and Early Warning Model Based on Meteorological Factors.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Haoyu%22">Li, Haoyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lihaoyu@mail.shiep.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Xiu%22">Yang, Xiu</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2288. 28p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Electric+power+system+faults%22">Electric power system faults</searchLink><br />*<searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br />*<searchLink fieldCode="DE" term="%22Autoencoders%22">Autoencoders</searchLink><br />*<searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br />*<searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22Weather%22">Weather</searchLink><br />*<searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22East+Asia%22">East Asia</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Natural disasters and extreme meteorological events are primary causes of unplanned outages in modern power systems. Existing early warning methods suffer from insufficient non-linear feature extraction, severe class imbalance, and limited minority-class recall under single-classifier architectures. This paper proposes a seven-class meteorological fault early warning framework that integrates a sparse autoencoder (SAE), a G1–entropy composite weighting scheme, SMOTE oversampling, and a soft-voting BP–XGBoost ensemble. A leakage-free experimental protocol confines SMOTE exclusively to the training partition, eliminating data contamination from evaluation. Validated on 1955 fault records from a regional grid in East China covering 110 kV, 220 kV, and 500 kV voltage levels (2013–2022), the proposed framework achieved 96.42% accuracy and a 97.46% macro F1-score on the held-out test set, outperforming SVM (72.68%), Random Forest (89.31%), LSTM (81.47%), 1D-CNN (85.38%), and LightGBM (92.15%). Ablation experiments confirmed that SMOTE and G1–entropy weighting contributed macro F1 gains of 8.34 and 6.91 percentage points, respectively, while removing the XGBoost branch degraded accuracy by 28.25%. Temporal validation on 2019–2022 records yielded 91.57% accuracy, confirming temporal generalization. Error analysis further revealed that bidirectional misclassification between lightning damage and wind damage, rooted in shared atmospheric instability signatures, constitutes the dominant residual error source, providing theoretical guidance for future threshold optimization strategies. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141403
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19102288
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 2288
    Subjects:
      – SubjectFull: Electric power system faults
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Autoencoders
        Type: general
      – SubjectFull: Model validation
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Weather
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: East Asia
        Type: general
    Titles:
      – TitleFull: Research on Power Grid Accident Analysis and Early Warning Model Based on Meteorological Factors.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Li, Haoyu
      – PersonEntity:
          Name:
            NameFull: Yang, Xiu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – Type: volume
              Value: 19
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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