Nhận Dạng Ổn Định Quá Độ Hệ Thống Điện dựa trên Mạng Neural Bộ Nhớ Dài Ngắn Hạn Hai Chiều - Kết Nối Đầy Đủ

J. Xie, “A Review of Machine Learning Applications in Power System Resilience Jian,” pp. 1–5, 2019, [Online]. Available: https://doi.org/10.1109/PESGM41954.2020.9282137. DOI: https://doi.org/10.1109/PESGM41954.2020.9282137

B. A. Carreras, P. Colet, J. M. Reynolds-Barredo, and D. Gomila, “Assessing Blackout Risk with High Penetration of Variable Renewable Energies,” IEEE Access, vol. 9, pp. 132663–132674, 2021, doi: 10.1109/ACCESS.2021.3114121. DOI: https://doi.org/10.1109/ACCESS.2021.3114121

Q. H. Wu, Y. Lin, C. Hong, Y. Su, T. Wen, and Y. Liu, “Transient Stability Analysis of Large-scale Power Systems: A Survey,” CSEE J. Power Energy Syst., vol. 9, no. 4, pp. 1284–1300, 2023, doi: 10.17775/CSEEJPES.2022.07110. DOI: https://doi.org/10.17775/CSEEJPES.2022.07110

M. Pavella, D. Ernst, and D. Ruiz-Vega, “Transient Stability of Power Systems A Unified Approach to Assessment and Control,” Kluwer Acad. Publ., pp. 1–254, 2000. DOI: https://doi.org/10.1007/978-1-4615-4319-0_1

J. N. Xin Li a, Chenkai Liu a b, Panfeng Guo a c, Shengchi Liu a, “Deep learning-based transient stability assessment framework for large-scale modern power system,” Int. J. Electr. Power Energy Syst., vol. 139, no. July, 2022. DOI: https://doi.org/10.1016/j.ijepes.2022.108010

W. Z. Songyan Wang, Jilai Yu, “Transient Stability Assessment Using Individual Machine Equal Area Criterion PART I: Unity Principle,” IEEE Access, vol. 6, pp. 77065–77076, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2854227

U. Shahzad, “Artificial Neural Network For Transient Stability Assessment: A Review,” 2022, [Online]. Available: http://arxiv.org/abs/2206.06800.

Z. Xie, D. Zhang, X. Han, and W. Hu, “Power system transient stability preventive control optimization method driven by Stacking Ensemble Learning,” Energy Reports, vol. 9, pp. 757–765, 2023, doi: 10.1016/j.egyr.2023.05.106. DOI: https://doi.org/10.1016/j.egyr.2023.05.106

O. A. Alimi, K. Ouahada, and A. M. Abu-Mahfouz, “A Review of Machine Learning Approaches to Power System Security and Stability,” IEEE Access, vol. 8, pp. 113512–113531, 2020, doi: 10.1109/ACCESS.2020.3003568. DOI: https://doi.org/10.1109/ACCESS.2020.3003568

Bhanu Pratap Soni; Vikas Gupta; Rajesh Kumar; Akash Saxena; S.L. Surana, “Application of ANN for stability assessment of large power system by post-fault rotor angle measurements,” IEEE, 2018.

R. Yousefian and S. Kamalasadan, “A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control,” pp. 1–14, 2017, [Online]. Available: http://arxiv.org/abs/1701.01214.

U. Shahzad, “Support vector machine for transient stability assessment : A review,” no. June, 2022.

R. T. Dabou, I. Kamwa, C. Y. Chung, and C. F. Mugombozi, “Time Series-Analysis Based Engineering of High-Dimensional Wide-Area Stability Indices for Machine Learning,” IEEE Access, vol. 9, pp. 104927–104939, 2021, doi: 10.1109/ACCESS.2021.3099459. DOI: https://doi.org/10.1109/ACCESS.2021.3099459

Y. Yang, Y. Huang, J. Liu, Y. Liu, T. Liu, and Y. Xiang, “Measurement-based cell-DT method for power system transient stability classification,” CSEE J. Power Energy Syst., vol. 3, no. 3, pp. 278–285, 2017, doi: 10.17775/cseejpes.2015.01230. DOI: https://doi.org/10.17775/CSEEJPES.2015.01230

S. Akhtar, M. Adeel, M. Iqbal, A. Namoun, A. Tufail, and K. H. Kim, “Deep learning methods utilization in electric power systems,” Energy Reports, vol. 10. Elsevier Ltd, pp. 2138–2151, Nov. 01, 2023. doi: 10.1016/j.egyr.2023.09.028. DOI: https://doi.org/10.1016/j.egyr.2023.09.028

F. Bellizio, J. L. Cremer, M. Sun, and G. Strbac, “A causality based feature selection approach for data-driven dynamic security assessment,” Electr. Power Syst. Res., vol. 201, 2021, doi: 10.1016/j.epsr.2021.107537. DOI: https://doi.org/10.1016/j.epsr.2021.107537

Y. Zhao et al., “A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study,” 2022 IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf. ISGT 2022, no. 1839684, 2022, doi: 10.1109/ISGT50606.2022.9817531. DOI: https://doi.org/10.1109/ISGT50606.2022.9817531

M. Ramirez-Gonzalez, F. R. Segundo Sevilla, P. Korba, and R. Castellanos-Bustamante, “Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment,” Electr. Power Syst. Res., vol. 211, p. 108203, Oct. 2022, doi: 10.1016/j.epsr.2022.108203. DOI: https://doi.org/10.1016/j.epsr.2022.108203

S. Patel and A. Patel, “Deep Learning Architectures and its Applications A Survey,” Int. J. Comput. Sci. Eng., vol. 6, no. 6, pp. 1177–1183, Jun. 2018, doi: 10.26438/ijcse/v6i6.11771183. DOI: https://doi.org/10.26438/ijcse/v6i6.11771183

J. Kim, H. Lee, S. Kim, S. H. Chung, and J. H. Park, “Transient Stability Assessment Using Deep Transfer Learning,” IEEE Access, vol. 11, no. October, pp. 116622–116637, 2023, doi: 10.1109/ACCESS.2023.3320051. DOI: https://doi.org/10.1109/ACCESS.2023.3320051

S. Wu, L. Zheng, W. Hu, R. Yu, and B. Liu, “Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment,” J. Mod. Power Syst. Clean Energy, vol. 8, no. 1, pp. 27–37, 2020, doi: 10.35833/MPCE.2019.000058. DOI: https://doi.org/10.35833/MPCE.2019.000058

L. Zheng and W. Hu, “Real-Time Transient Stability Assessment Based on Deep Recurrent Neural Network,” pp. 6–10, 2017. DOI: https://doi.org/10.1109/ISGT-Asia.2017.8378321

B. Li, J. Wu, L. Hao, M. Shao, R. Zhang, and W. Zhao, “Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network,” IEEE Access, vol. 8, pp. 35231–35244, 2020, doi: 10.1109/ACCESS.2020.2974915. DOI: https://doi.org/10.1109/ACCESS.2020.2974915

E. A. Tapia, D. G. Colomé, and J. L. Rueda Torres, “Recurrent Convolutional Neural Network-Based Assessment of Power System Transient Stability and Short-Term Voltage Stability,” Energies, vol. 15, no. 23, 2022, doi: 10.3390/en15239240. DOI: https://doi.org/10.3390/en15239240

S. Cheng, Z. Yu, Y. Liu, and X. Zuo, “Power system transient stability assessment based on the multiple paralleled convolutional neural network and gated recurrent unit,” Prot. Control Mod. Power Syst., vol. 7, no. 1, 2022, doi: 10.1186/s41601-022-00260-z. DOI: https://doi.org/10.1186/s41601-022-00260-z

T. McKerahan, “Transient stability preventive control based on graph convolution neural network and transfer deep reinforcement learning,” pp. 1–13, 2022, doi: 10.17775/CSEEJPES.2022.05030. DOI: https://doi.org/10.17775/CSEEJPES.2022.05030

J. Huang, L. Guan, Y. Su, H. Yao, M. Guo, and Z. Zhong, “Recurrent graph convolutional network-based multi-task transient stability assessment framework in power system,” IEEE Access, vol. 8, pp. 93283–93296, 2020, doi: 10.1109/ACCESS.2020.2991263. DOI: https://doi.org/10.1109/ACCESS.2020.2991263

P. Kundur, Power System Stability And Control. McGraw-Hill, Inc.: Electric Power Research Institute, 1994.

T. Yang, B. Li, and Q. Xun, “LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization,” IEEE Access, vol. 7, pp. 171471–171484, 2019, doi: 10.1109/ACCESS.2019.2954290. DOI: https://doi.org/10.1109/ACCESS.2019.2954290

Z. Cui, R. Ke, Z. Pu, and Y. Wang, “Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” pp. 1–11, 2018, [Online]. Available: http://arxiv.org/abs/1801.02143.

Z. Yu et al., “Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech,” 2015 IEEE Work. Autom. Speech Recognit. Understanding, ASRU 2015 - Proc., pp. 338–345, 2016, doi: 10.1109/ASRU.2015.7404814. DOI: https://doi.org/10.1109/ASRU.2015.7404814

M. Bekkar, H. K. Djemaa, and T. A. Alitouche, “Evaluation Measures for Models Assessment over Imbalanced Data Sets,” J. Inf. Eng. Appl., vol. 3, no. 10, pp. 27–38, 2013, [Online]. Available: http://www.iiste.org/Journals/index.php/JIEA/article/view/7633.

MATLAB R2021b.