Research Article
Forecast of the Ice Disaster in Hunan Power Grid in Late January 2022 Using a High Resolution Global NWP Model
@INPROCEEDINGS{10.1007/978-3-031-31733-0_8, author={Lei Wang and Tao Feng and Zelin Cai and Xunjian Xu and Li Li and Jinhai Huang}, title={Forecast of the Ice Disaster in Hunan Power Grid in Late January 2022 Using a High Resolution Global NWP Model}, proceedings={Smart Grid and Innovative Frontiers in Telecommunications. 7th EAI International Conference, SmartGIFT 2022, Changsha, China, December 10-12, 2022, Proceedings}, proceedings_a={SMARTGIFT}, year={2023}, month={5}, keywords={grid line icing numeric weather forecast model prediction performance}, doi={10.1007/978-3-031-31733-0_8} }- Lei Wang
Tao Feng
Zelin Cai
Xunjian Xu
Li Li
Jinhai Huang
Year: 2023
Forecast of the Ice Disaster in Hunan Power Grid in Late January 2022 Using a High Resolution Global NWP Model
SMARTGIFT
Springer
DOI: 10.1007/978-3-031-31733-0_8
Abstract
The icing of power grid has great influence on the safe and stable operation of power grid. Under the background of global warming, the frequency and duration of grid line icing events have increased. Under the influence of warm and humid air flow and special terrain in winter, it is very easy to have ice cover in Hunan Province. From the winter of 2021 to the spring of 2022, Hunan experienced five rounds of grid line icing, and the icing event lasted for more than three days totally. In this study, a global numeric weather forecast model with a resolution of 10 km is used to explore its prediction performance for the ice events at the end of January 2022 (27–29thJanuary). The results show that it has a good prediction performance for the ice cover in Hunan, and the spatial distribution characteristics of the ice cover can be predicted 6 days in advance, although the intensity is slightly weak. A test also shows that the ability to predict heavy icing events can be improved by fusing icing observation information at the initial time of icing prediction, which emphasizes the importance of icing observation to icing prediction.
