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<p>To enhance the safety of microgrid switching and the identification of misoperations, we propose Time-Synchronized Misoperation Recognition (TS-MR), a method tailored to switching operations. The approach performs rule-based pre-screening grounded in operating procedures and anti-misoperation interlocking, achieves millisecond-level time synchronization of multi-source heterogeneous data via a two-stage scheme that combines variational Bayesian inference with a UKF, and employs a fusion of a Transformer, a TCN, and a GNN for cross-modal representation with interpretable discrimination. Laboratory records constitute the training, validation, and test sets; HIL data are used solely for independent cross-validation; and public datasets are used only for cross-domain robustness calibration, and none contributes to training, validation, or threshold tuning. Under a unified evaluation protocol, TS-MR attains 94.69% accuracy and an AUC of 0.977 in typical switching scenarios; end-to-end latency is about 80 ms; the core forward-pass latency is about 42 ms; the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({P_{95}}\)</EquationSource> </InlineEquation> per-inference latency is 55 ms; and computational complexity is about 3.4 GFLOPs. Compared with CNN-BiLSTM, ConvLSTM, and GAT under identical preprocessing, time synchronization, and fixed random seeds, TS-MR improves accuracy by 0.9 to 3.7% points and AUC by 0.024 to 0.057. These results indicate that TS-MR provides high-confidence misoperation recognition and interpretable assessment for microgrid switching while satisfying engineering-grade real-time constraints.</p>

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Recognition method for microgrid switching misoperations based on multi-source heterogeneous data fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Hongzhao Yang,
  • Zhan Zhang,
  • Shijie Zhang,
  • Rui Liang

摘要

To enhance the safety of microgrid switching and the identification of misoperations, we propose Time-Synchronized Misoperation Recognition (TS-MR), a method tailored to switching operations. The approach performs rule-based pre-screening grounded in operating procedures and anti-misoperation interlocking, achieves millisecond-level time synchronization of multi-source heterogeneous data via a two-stage scheme that combines variational Bayesian inference with a UKF, and employs a fusion of a Transformer, a TCN, and a GNN for cross-modal representation with interpretable discrimination. Laboratory records constitute the training, validation, and test sets; HIL data are used solely for independent cross-validation; and public datasets are used only for cross-domain robustness calibration, and none contributes to training, validation, or threshold tuning. Under a unified evaluation protocol, TS-MR attains 94.69% accuracy and an AUC of 0.977 in typical switching scenarios; end-to-end latency is about 80 ms; the core forward-pass latency is about 42 ms; the \({P_{95}}\) per-inference latency is 55 ms; and computational complexity is about 3.4 GFLOPs. Compared with CNN-BiLSTM, ConvLSTM, and GAT under identical preprocessing, time synchronization, and fixed random seeds, TS-MR improves accuracy by 0.9 to 3.7% points and AUC by 0.024 to 0.057. These results indicate that TS-MR provides high-confidence misoperation recognition and interpretable assessment for microgrid switching while satisfying engineering-grade real-time constraints.