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- W2968165738 abstract "The disruptions, natural hazard and cyber-attacks are growing in frequency and severity in power systems. A machine learning technique based on deep neural networks is described that leverage the realtime operation of control center from historical data sources of synchrophasor measurements to predict abnormal conditions. Performance of our framework across synthetic data over control center real-time forecasting tasks shows that our methods yield a significant lift or increase of proposed evaluation metrics for the power system sustained oscillations on predicted disruptions. Our results suggest that, when deployed, our system will be able to provide an adequate prediction tool for various types of disturbances." @default.
- W2968165738 created "2019-08-22" @default.
- W2968165738 creator A5014048909 @default.
- W2968165738 date "2019-02-01" @default.
- W2968165738 modified "2023-09-25" @default.
- W2968165738 title "Faster Than Real-time Prediction of Disruptions in Power Grids using PMU: Gated Recurrent Unit Approach" @default.
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- W2968165738 doi "https://doi.org/10.1109/isgt.2019.8791625" @default.
- W2968165738 hasPublicationYear "2019" @default.
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