Abstract
Disaster prediction can buy valuable time for power grid disaster prevention and reduction. In this study, we firstly propose a physical statistical method for substation inundation prediction, which can comprehensively consider geographic features, real-time water level height of substation, precipitation amount, substation water collection capacity and drainage capacity. Nine substations in Changsha City, Hunan Province, are then analyzed separately and inundation prediction models are established using the rainstorm and substation inundation process in May 2022. The accuracy of the models are finally verified by using the heavy rainfall process in July 2022. The results show that the model can reproduce the process of water level variation in the substation, with a height error of less than 5cm. Subsequently, those models can combine the refined precipitation prediction data to carry out real-time prediction of substation inundation risk and hopefully improve the substation rainstorm disaster resistance.
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Acknowledgement
The present research is supported by the Science and Technology Project of the State Grid Hunan Electric Power Company Limited (Grant Number: 5216AF21N00L).
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Wang, L., Feng, T., Cai, Z., Li, L., Xu, X. (2024). A Physical-Statistical Model for Rainstorm Inundation of Substation. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_3
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DOI: https://doi.org/10.1007/978-981-97-2757-5_3
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