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• W4315694900 abstract "As the complexity of power systems increases, together with the increasing integration of phasor measurement units, an intelligent assessment for short-term voltage stability (STVS) becomes necessary and feasible. Especially for post-fault STVS assessment, the task requires a fast and accurate conclusion. Given this, an intelligent post-fault STVS assessment method is proposed in this research. Based on Gramian Angular Field transform, two-dimensional convolutional neural network, and adaptive confidence interval, the proposed method with an offline-online data-driven framework shows better performance when the amount of input data is limited. The related simulations are conducted on the New England 10-machine 39-bus system." @default.
• W4315694900 created "2023-01-12" @default.
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• W4315694900 date "2022-11-01" @default.
• W4315694900 modified "2023-10-16" @default.
• W4315694900 title "A Gramian Angular Field Transform-Based Higher-Dimension Data-Driven Method for Post-Fault Short-Term Voltage Stability Assessment" @default.
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• W4315694900 doi "https://doi.org/10.1109/isgtasia54193.2022.10003631" @default.
• W4315694900 hasPublicationYear "2022" @default.
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