Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
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DOI: 10.1016/j.rser.2023.113913
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- Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
- Li, Yang & Zhang, Meng & Chen, Chen, 2022. "A Deep-Learning intelligent system incorporating data augmentation for Short-Term voltage stability assessment of power systems," Applied Energy, Elsevier, vol. 308(C).
- Shair, Jan & Li, Haozhi & Hu, Jiabing & Xie, Xiaorong, 2021. "Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
- Hasan, Kazi Nazmul & Preece, Robin & Milanović, Jovica V., 2019. "Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 168-180.
- Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
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Keywords
Power system; Short-term voltage stability assessment; Transformer architecture; Class imbalance; Renewable energy penetration; Conditional wasserstein generative adversarial network with gradient penalty (CWGAN-GP);All these keywords.
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