广东电网遭台风泰利侵袭的输配电设备受损分析及评估

Analysis and Evaluation on Damage to Transmission and Distribution Equipment in Guangdong Power Grid Caused by Typhoon Talim

近年来致灾台风频率呈增加趋势,以2023年影响广东电网约百万用户的第4号台风“泰利”为例,分析广东电网遭台风侵袭受灾情况,建立输配电杆塔受损预测模型,识别关键特征变量与因素,为电网防灾减灾提供支持。首先,分析台风“泰利”气象特征,具有“台前对流活跃,风力强度大,降水范围广”等特点,对输配电设备均产生一定程度破坏。其次,利用随机森林、支持向量机、梯度决策树、神经网络等4种机器学习算法建立输配电杆塔受损预测模型,并对比部分算法针对不平衡样本优化前后模型表现。算例表明,随机森林优化后提升最大,综合考虑时间指标及预测质量指标,梯度决策树为最优算法。最后,基于shapley additive explanations法等对最优模型解释性进行评估,分析表明最大风速、温度、杆塔数量等对预测结果有显著影响。所得结论有助于深入理解台风对广东电网的影响机制,为提升区域电网抵御复杂自然灾害能力提供参考。

In recent years,the frequency of devastating typhoons has been on the rise.Typhoon “Talim” affected the power grid in Guangdong and impacted approximately a million users in 2023.We analyze the damage of Guangdong power grid and establish a prediction model for the destruction of transmission and distribution towers.Key features are identified to support disaster prevention.Firstly,we analyze the typhoon's characteristics.It has “active convection in front of the typhoon,strong wind strength and wide precipitation range”,causing varying degrees of damage to transmission and distribution equipment.Then,using typhoon “Talim”,prediction models for predicting pole damage in the transmission and distribution network are established by Random Forest,Support Vector Machine,Gradient Boosting Decision Tree,and Neural Networks.The performance comparison is conducted before and after optimizing for imbalanced samples.Case studies indicate that Random Forest exhibits the greatest improvement after optimization,and considering both time and prediction quality indicators,the Gradient Decision Tree is the optimal algorithm.Finally,feature analysis and Shapley Additive Explanations are performed.The findings show that the maximum wind speed,temperature and the number of towers have significant influence on the prediction results.It is helpful to further understand the influence mechanism of typhoon on Guangdong power grid and provide reference for improving the ability of regional power grid to withstand complex natural disasters.