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• W3135608049 abstract "Accurate and fast event identification in power systems is critical for taking timely controls to avoid instability. In this paper, a synchrophasor measurement-based fast and robust event identification method is proposed considering different penetration levels of renewable energy. A difference Teager-Kaiser energy operator (dTKEO)-based algorithm is first proposed to improve multiple-events detection accuracy. Then, feature extractions via the integrated additive angular margin (AAM) loss and the long short-term memory (LSTM) network are developed. This allows us to deal with intra-class similarity and inter-class variance of events when high penetration renewable energy occurs. With the extracted features, a multi-stage weighted summing (MSWS) loss-based criterion is developed for adaptive data window determination and fast event pre-classification. Finally, the re-identification model based on feature similarity is established to identify unknown events, a challenge for existing machine learning algorithms. Simulation results on the IEEE 39-bus, Kundur 2-area, and an actual large-scale power grid system are used to demonstrate the advantages of the proposed method over others." @default.
• W3135608049 created "2021-03-15" @default.
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• W3135608049 date "2021-09-01" @default.
• W3135608049 modified "2023-10-08" @default.
• W3135608049 title "Fast Power System Event Identification Using Enhanced LSTM Network With Renewable Energy Integration" @default.
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• W3135608049 doi "https://doi.org/10.1109/tpwrs.2021.3064250" @default.
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