摘要
针对山火威胁配电线路稳定运行的问题,建立配电线路火灾风险预测模型具有重要意义,然而山火数据稀缺导致样本不平衡,会影响模型的准确性。为此,首先基于气象、地理、可燃物、社会等影响因子,采用支持向量机,结合代价敏感思想,赋予少数类样本更大权重;然后采用递归特征消除法选择出有利于少数类分类的特征,在此基础上,构建基于随机森林-自适应增强(random forest-adaptive boosting,RF-AdaBoost)算法的配电线路火灾风险预测模型;最后选取四川省西昌市某10 kV线路走廊区域开展实例验证,采用十折交叉验证并与其他算法进行对比,得出所提算法的召回率提高至76.67%,有效减小了样本不平衡问题对模型性能的影响,降低了山火误判,为线路走廊山火防治提供了依据。
In response to wildfires that threaten the stable operation of distribution lines,it is important to establish a fire risk prediction model for distribution lines.However,the scarcity of data on wildfires causes sample imbalance,affecting the accuracy of the model.To this end,based on the influencing factors such as meteorological,geographic,combustible,and social factors,support vector machines and the idea of cost sensitivity are used to assign more weight to minority samples.Recursive feature elimination is used to select features that favor minority class classification.On this basis,a fire risk prediction model for distribution lines based on the random forest-adaptive boosting algorithm(RF-AdaBoost)is constructed.Finally,a 10 kV line corridor area in Xichang City,Sichuan Province,is selected to carry out an example verification.Ten-fold cross validation is used and compared with other algorithms.The results show that the recall rate of the method in this paper increases to 76.67%,which lessens the impact of sample imbalance on the model performance,reduces the misclassification of wildfires,and provides a basis for wildfire prevention and control in line corridors.
作者
田甜
王军
宁鑫
孙章
王鑫
TIAN Tian;WANG Jun;NING Xin;SUN Zhang;WANG Xin(School of Electrical Engineering and Electronic Information,Xihua University,Chengdu 610039,China;Electric Power Research Institute of State Grid Sichuan Electric Power Company,Chengdu 610041,China)
出处
《电力科学与技术学报》
北大核心
2025年第3期45-51,共7页
Journal of Electric Power Science And Technology
基金
四川省自然科学基金重点项目(2022NSFSC0025)。
关键词
配电线路
样本不平衡
山火
风险预测
随机森林-自适应增强
distribution line
sample imbalance
wildfire
risk prediction
random forest-adaptive boosting
