Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 15, 2024
Article Number 24
Number of page(s) 12
DOI https://doi.org/10.1051/smdo/2024024
Published online 15 November 2024
  1. Z. Lu, S. Lu, M. Xu, B. Cui, A robust stochastic stability analysis approach for power system considering wind speed prediction error based on Markov model, Comput. Stand. Interfaces 75, 2–12 (2020) [Google Scholar]
  2. J. Geng, X. Sun, F. Li, X. Wu, Prediction method of important nodes and transmission lines in power system transactive management, Electric Power Syst. Res. 208, 10–18 (2022) [Google Scholar]
  3. J. Shah, B. Mishra, IoT-enabled low power environment monitoring system for prediction of P M2. 5, Pervasive Mobile Comput. 67, 10–26 (2020) [Google Scholar]
  4. Y. Hu, H. Wang, Y. Zhang, W. Buying, Frequency prediction model combining ISFR model and LSTM network, Int. J. Electr. Power Energy Syst. 139, 108–118 (2022) [Google Scholar]
  5. B. Wang, X. Peng, L. Zhang, S. Peng, Adaptive bus-voltage control of ship power system with online fluctuation prediction, IET Generat. Trans. Distrib. 16, 3109–3118 (2022) [CrossRef] [Google Scholar]
  6. S. Chang, C. Peng, Y. Hu et al., An improved prony prediction compensation-based wide-area damping control approach for power system low-frequency oscillation suppression, J. Sensors 2021, 1–10 (2021) [Google Scholar]
  7. Z. Tian, H. Wang, Wind power system reliability and maintenance optimization considering turbine and wind uncertainty, J. Qual. Mainten. Eng. 28, 252–273 (2020) [Google Scholar]
  8. M.R. Esmaili, A. Khodabakhshian, M. Gholipour, M.R. Esmaili, M. Malekpour, Approach for prediction of cold loads considering electric vehicles during power system restoration, IET Generat. Trans. Distrib. 14, 5249–5260 (2020) [CrossRef] [Google Scholar]
  9. A.R. Sobbouhi, A. Vahedi, Transient stability prediction of power system; a review on methods, classification and considerations, Electric Power Syst. Res. 190, 1068–1079 (2021) [Google Scholar]
  10. W. Cui, W. Li, C. Wang, N. Yang, Y. Zhu, X. Bai, C. Xue, Prediction of primary frequency regulation capability of power system based on deep belief network, Int. Conf. Pioneering Comput. Sci. Eng. Educ. 20, 423–435 (2020) [Google Scholar]
  11. F. Chengwei, Y. Fei, X. Wang, Steady frequency prediction algorithm for power system with governor deadband, Eur. Trans. Electr. Power 28, 1–14 (2018) [Google Scholar]
  12. Q. Wang, C. Zhang, Y. Lü, Y. Zhihong, T. Yi, Data inheritance-based updating method and its application in transient frequency prediction for a power system, Int. Trans. Electr. Energy Syst. 29, 2–17 (2019) [Google Scholar]
  13. A.R. Sobbouhi, A. Vahedi, Transient stability improvement based on out-of-step prediction, Electric Power Syst. Res. 194, 2–13 (2021) [Google Scholar]
  14. Y. Fang, B. Luo, T. Zhao et al., ST-SIGMA: spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting, CAAI Trans. Intell. Technol. 7, 744–757 (2022) [CrossRef] [Google Scholar]
  15. Z. Chen, Research on internet security situation awareness prediction technology based on improved RBF neural network algorithm, J. Comput. Cogn. Eng. 1, 103–108 (2022) [Google Scholar]
  16. A. Kazemian, Y. Basati, M. Khatibi, M. Tao, Performance prediction and optimization of a photovoltaic thermal system integrated with phase change material using response surface method, J. Cleaner Prod. 290, 12–28 (2021) [Google Scholar]
  17. G. Wang, Z. Zhang, Z. Bian, X. Zheng, A short-term voltage stability online prediction method based on graph convolutional networks and long short-term memory networks, Int. J. Electr. Power Energy Syst. 127, 10–19 (2021) [Google Scholar]
  18. J. Lin, J.A. Fernandez, R. Rayhana, F. Mengge, Predictive analytics for building power demand: day-ahead forecasting and anomaly prediction, Energy Build 15, 255–272 (2022) [Google Scholar]
  19. X. Liu, H. Wang, Q. Wang, G. Tongyao, Research on fault scenario prediction and resilience enhancement strategy of active distribution network under ice disaster, Int. J. Electr. Power Energy Syst. 135, 10–27 (2022) [Google Scholar]
  20. Y. Wang, X. Qi, Y. Chen, Enhanced fault localization in multi-terminal transmission lines using novel machine learning, Int. J. Simul. Multidisci. Des. Optim. 15, 15 (2024) [CrossRef] [EDP Sciences] [Google Scholar]
  21. S.D. Milić, Ž. Đurović, M.D. Stojanović, Data science and machine learning in the IIoT concepts of power plants, Int. J. Electr. Power Energy Syst. 145, 108711–108712 (2023) [CrossRef] [Google Scholar]
  22. V. Govindaraj, P. Palpandian, V. Varun, M. Ram, Automated power factor correction and predictive energy monitoring using machine learning, in 2024 International Conference on Science Technology Engineering and Management (ICSTEM) (2024), pp. 1–7 [Google Scholar]

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