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• W4205628987 abstract "Faults are extreme events that canadversely affect the voltages in islanded microgrids. This paper provides a new data-driven methodology for timely prediction of the post-fault voltage stability in hybrid AC/DC microgrids. The proposed method performs a binary classification with delay constraints by processing sequences of the short-time mean squared deviations using a deep learning system. The deep learning system consists of a bidirectional long short-term memory network whose output is a probabilistic voltage instability indicator. When the value of the indicator is non-zero, persistent voltage disturbances are most likely to occur even after the fault clearance. The proposed method enables the microgrid to carry out remedial or preventive actions, such as event-triggered protection and control of distributed energy resources (DERs), which are advantageous to the resilient operation of the microgrids. Extensive and detailed electromagnetic transient (EMT) simulations of a low-voltage hybrid AC/DC microgrid benchmark are analyzed, and the results confirm the effectiveness of the proposed method for online prediction and fast voltage regulation." @default.
• W4205628987 created "2022-01-26" @default.
• W4205628987 creator A5008486848 @default.
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• W4205628987 date "2022-09-01" @default.
• W4205628987 modified "2023-10-02" @default.
• W4205628987 title "A Data-Driven Method for Prediction of Post-Fault Voltage Stability in Hybrid AC/DC Microgrids" @default.
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• W4205628987 doi "https://doi.org/10.1109/tpwrs.2022.3142110" @default.
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