AI-Enhanced Outage Prediction and Restoration Planning for Storm and Extreme-Weather Events
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P118Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Outage Prediction, Restoration Planning, Extreme-Weather Events, Power System ResilienceAbstract
Extreme-weather events and storms are becoming more and more threatening to the reliability and resilience of power systems across the globe, leading to the massive outage, economic damages, and failure of essential services. The classical statistical methods used to predict outages and plan their restoration are not effective to manage non-linear relationships, high dimensional data, and complex spatiotemporal correlation involved in such phenomena. Machine learning, deep learning, and hybrid models are all forms of artificial intelligence (AI) that has become a strong alternative and can be used to make precise outage forecasts, risk assessment, and better restoration plans. In this review, the authors provide a general summary of AI-based methods of outage prediction and outage restoration planning in situations of unstable weather, the methodologies, datasets, feature engineering, evaluation metrics, and practical examples. The main issues that are discussed are data scarcity, heterogeneity, real-time deployment and limitation of generalization. Lastly, there are also indicated research gaps and future directions which include hybrid AI models, spatiotemporal models, resiliency-oriented metrics, and synthetic data generation to enhance predictive performance and operational reliability.
References
1. A. C. R. Gonçalves et al., “Extreme weather events on energy systems: A comprehensive review on impacts, mitigation, and adaptation measures,” Sustainable Energy Research, vol. 11, no. 1, p. 4, 2024.
2. C. Klinger, O. Landeg, and V. Murray, “Power outages, extreme events and health: A systematic review of the literature from 2011–2012,” PLoS Currents, vol. 6, 2014.
3. R. J. Campbell and S. Lowry, Weather-Related Power Outages and Electric System Resiliency. Washington, DC, USA: Congressional Research Service, Library of Congress, 2012.
4. R. Nateghi, S. D. Guikema, and S. M. Quiring, “Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes,” Risk Analysis, vol. 31, no. 12, pp. 1897–1906, 2011.
5. H. A. Habeeb et al., “Design optimization method based on artificial intelligence (hybrid method) for repair and restoration using additive manufacturing technology,” Metals, vol. 13, no. 3, p. 490, 2023.
6. M. Galar et al., “A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 4, pp. 463–484, 2011.
7. G. Hou and K. K. Muraleetharan, “Modeling the resilience of power distribution systems subjected to extreme winds considering tree failures: An integrated framework,” International Journal of Disaster Risk Science, vol. 14, no. 2, pp. 194–208, 2023.
8. T. Krieg, “Substations,” in Springer Handbook of Power Systems. Singapore: Springer, 2019, pp. 867–934.
9. H. Bang, L. Miles, and R. Gordon, “Hurricane occurrence and seasonal activity: An analysis of the 2017 Atlantic hurricane season,” American Journal of Climate Change, vol. 8, no. 4, pp. 454–481, 2019.
10. J. W. Mitchell, “Power line failures and catastrophic wildfires under extreme weather conditions,” Engineering Failure Analysis, vol. 35, pp. 726–735, 2013.
11. F. C. L. Trindade, W. Freitas, and J. C. M. Vieira, “Fault location in distribution systems based on smart feeder meters,” IEEE Transactions on Power Delivery, vol. 29, no. 1, pp. 251–260, 2014.
12. M. Panteli and P. Mancarella, “Influence of extreme weather and climate change on the resilience of power systems: Impacts and possible mitigation strategies,” Electric Power Systems Research, vol. 127, pp. 259–270, 2015.
13. M. Rizvi, “Leveraging deep learning algorithms for predicting power outages and detecting faults: A review,” Advances in Research, vol. 24, no. 5, pp. 80–88, 2023.
14. M. Dong, “Combining unsupervised and supervised learning for asset class failure prediction in power systems,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5033–5043, 2019.
15. C. Zheng et al., “A novel integrated urban flood risk assessment approach coupling GeoDetector–DEMATEL and clustering method,” Journal of Environmental Management, vol. 354, p. 120308, 2024.
16. K. Hoganson and J. Sexton, Eds., The Cambridge History of America and the World. Cambridge, U.K.: Cambridge University Press, 2022.
17. K. Olcay, S. G. Tunca, and M. A. Özgür, “Forecasting and performance analysis of energy production in solar power plants using long short-term memory (LSTM) and random forest models,” IEEE Access, 2024.
18. B. Kar et al., RePOWERD: Restoration of Power Outage From Wide-Area Severe Weather Disruptions, ORNL/TM-2022/2621. Oak Ridge, TN, USA: Oak Ridge National Laboratory, 2022.
19. G. Sullivan et al., Operations & Maintenance Best Practices—A Guide to Achieving Operational Efficiency, Release 3, PNNL-19634. Richland, WA, USA: Pacific Northwest National Laboratory, 2010.
20. D. Moreno-Mateos et al., “The long-term restoration of ecosystem complexity,” Nature Ecology & Evolution, vol. 4, no. 5, pp. 676–685, 2020.
21. Abido, M. A. (2002). Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 24(7), 563–571. https://doi.org/10.1016/S0142-0615(01)00067-9
22. F. Ahmed, “Cloud security posture management (CSPM): Automating security policy enforcement in cloud environments,” ESP International Journal of Advancements in Computational Technology, vol. 1, no. 3, 2023.
23. E. Hawkins et al., “Rescuing historical weather observations improves quantification of severe windstorm risks,” Natural Hazards and Earth System Sciences, vol. 23, no. 4, pp. 1465–1482, 2023.
24. D. Tzelepis et al., “Voltage and current measuring technologies for high voltage direct current supergrids: A technology review identifying the options for protection, fault location and automation applications,” IEEE Access, vol. 8, pp. 203398–203428, 2020.
25. Naeem, M. F., Hashmi, K., Rahman Kashif, S. A., Mansoor Khan, M., Alghaythi, M. L., Aymen, F., … AboRas, K. M. (2022). A novel method for life estimation of power transformers using fuzzy logic systems: An intelligent predictive maintenance approach. Frontiers in Energy Research, 10, Article 977665. https://doi.org/10.3389/fenrg.2022.977665
26. K. Gandhi and P. Verma, “Wind and solar power generation forecasting using hybrid deep-learning models,” Amer. J. Comput. Eng., vol. 5, no. 2, pp. 50–69, 2022.
27. S. H. Elinjulliparambil, “Robust object detection under extreme weather using physics-aware deep learning,” Amer. Int. J. Comput. Sci. Technol., vol. 6, no. 2, pp. 22–33, 2024.
