侯慧, 刘超, 陈希, 吴细秀, 魏瑞增, 何浣. 基于网格化多源异构数据的台风灾害下杆塔受损数量预测[J]. 高电压技术, 2023, 49(3): 1205-1212. DOI: 10.13336/j.1003-6520.hve.20220566
引用本文: 侯慧, 刘超, 陈希, 吴细秀, 魏瑞增, 何浣. 基于网格化多源异构数据的台风灾害下杆塔受损数量预测[J]. 高电压技术, 2023, 49(3): 1205-1212. DOI: 10.13336/j.1003-6520.hve.20220566
HOU Hui, LIU Chao, CHEN Xi, WU Xixiu, WEI Ruizeng, HE Huan. Prediction on Number of Damaged Towers Under Typhoon Disasters Based on Gridded Multi-source Heterogeneous Data[J]. High Voltage Engineering, 2023, 49(3): 1205-1212. DOI: 10.13336/j.1003-6520.hve.20220566
Citation: HOU Hui, LIU Chao, CHEN Xi, WU Xixiu, WEI Ruizeng, HE Huan. Prediction on Number of Damaged Towers Under Typhoon Disasters Based on Gridded Multi-source Heterogeneous Data[J]. High Voltage Engineering, 2023, 49(3): 1205-1212. DOI: 10.13336/j.1003-6520.hve.20220566

基于网格化多源异构数据的台风灾害下杆塔受损数量预测

Prediction on Number of Damaged Towers Under Typhoon Disasters Based on Gridded Multi-source Heterogeneous Data

  • 摘要: 台风灾害下受灾区域杆塔受损数量预测对提高电网公司防灾减灾能力具有重要意义。为此从全新的统计学习角度出发,基于气象数据、电网数据、地理数据等多源异构数据原始样本,对数据进行分类变量处理、特征筛选、标准化等预处理,并以网格内的杆塔受损数量为响应变量,基于梯度提升决策树算法构建台风灾害下杆塔受损数量预测模型,对台风灾害下配网10 kV杆塔受损数量进行预测。利用广东省徐闻县的数据样本对模型进行训练测试与验证,仿真结果表明所提方法可较为准确地预测台风灾害下配网杆塔受损数量,为电网公司的灾前风险量化评估、制定应急方案等提供辅助决策参考。

     

    Abstract: The prediction of the damaged number of towers in typhoon-affected areas is of great significance to improving the disaster prevention and mitigation capacity of power grid. From a perspective of brand-new statistical learning, based on the original samples of multi-source heterogeneous data such as meteorological data, power grid data, and geographical data, we pre-process the data such as classification variable processing, feature screening, standardization, etc. Taking the damaged number of towers in the grid as the response variable, we construct the prediction model of damaged number of towers under typhoon disasters based on the gradient lifting decision tree algorithm, and predict the damage quantity of 10 kV tower in distribution network under typhoon disasters. The model is trained, tested and verified by using the data samples of Xuwen County, Guangdong Province, China. The simulation results show that the proposed method can be adopted to accurately predict the number of damaged distribution towers under typhoon disasters, and provide auxiliary decision-making reference for pre-disaster risk quantitative assessment and emergency plan-making.

     

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