摘要
山火灾害易诱发架空输电线路跳闸事故,严重危害电网的正常稳定运行。为降低山火灾害对电网运行的影响,提高架空输电线路山火防治水平,本文提出了一种基于图模型的架空输电线路山火风险等级预测模型。该模型首先在森林火险气象指数的基础上,综合考虑地表燃烧因子和历史火情因子的影响,构建了输电线路山火风险等级评估指标体系;然后结合专家评分采用图模型和最优理论降低赋权中的主观随意性,确定了各因子权重系数,并利用3 km×3 km分辨率的降水预报数据对山火风险等级进行修正,实现了输电线路山火风险等级预测;最后以南方电网山火高发期卫星监测热点分布情况,验证了所提方法的准确性。该方法目前已成功应用于南方电网山火监测预警中心,可指导输电运维人员提前巡查重点隐患线路,降低架空输电线路山火跳闸风险,提高电网安全稳定性能。
The wildfire is one of the main causes leading transmission line tripping,which endangers the operation of power grid seriously.In order to improve the wildfire prevention level of overhead transmission lines,a fire risk level prediction model of overhead transmission lines is proposed.This model is established based on the weather index of forest fire model as well as the impact of surface environment and historical fire behavior.Then a graph-based optimization theory is introduced to determine the weight of indexes.Finally,the forecast precipitation data of 3 km×3 km resolution is used to modify the risk level of the wildfire.This model has been successfully applied to Wildfire Monitoring and Early Warning Center of China Southern Power Grid.Based on the risk prediction,the transmission operation and maintenance staffs can inspect the high fire-risk transmission lines in advance.It reduces the probability of the overhead transmission line tripping caused by wildfires,which improves the safety and stability performance of the power grid.
作者
周恩泽
黄勇
陈洁
田翔
魏瑞增
周游
ZHOU Enze;HUANG Yong;CHEN Jie;TIAN Xiang;WEI Ruizeng;ZHOU You(Electric Power Research Institute of Guangdong Power Grid Co.,Ltd.,Guangzhou 510080,China;National Satellite Meteorological Center,Beijing 100081,China;Hunan Province 2011 Collaborative Innovation Center of Clean Energy and Smart Grid,School of Electrical&Information Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《南方电网技术》
CSCD
北大核心
2020年第4期8-16,共9页
Southern Power System Technology
基金
国家自然科学基金项目(41775162)
中国南方电网公司科技项目(GDKJXM20173024)
湖南省自然科学基金项目(2019JJ50658)。
关键词
架空输电线路
山火预测
图模型
overhead transmission lines
wildfire prediction
graph theory