基于非线性状态估计的电缆隧道火灾隐患监控预警方法

Cable Tunnel Fire Risk Monitoring and Early Warning Method Based on Nonlinear State Estimation

电缆隧道中由于设备运行状态或线路老化问题造成的电缆隧道火灾对其运维影响最为严重,为了保证电缆隧道的可靠运行,文章提出基于非线性状态估计的电缆隧道火灾隐患监控预警方法。通过对电缆隧道总故障发生几率进行降序排序,确定隧道状态,采用非线性状态估计算法构建电缆隧道状态预估模型,使用嵌入式监控设备采集电缆隧道状态数据,为电缆隧道状态预估模型提供数据来源。将模型数据引入数据库中,计算数据聚类距离,使用K均值聚类方法实现对火灾隐患数据的区分,并将其传输到主控终端,完成火灾隐患的预警工作。仿真实验结果表明,此方法无论在电缆隐患情况下还是环境隐患情况下,其预警误报率均在10%以内,且电缆运行状态估计精准度较高,火灾隐患数据传输时延较短。

In the cable tunnel,the cable tunnel fire caused by the running state of the equipment or the aging of the line has the most serious impact on the operation and maintenance of the cable tunnel.In order to ensure the reliable operation of cable tunnel,a monitoring and early warning method of cable tunnel fire hazards based on nonlinear state estimation is proposed.Through the descending order of the total failure probability of the cable tunnel,the tunnel state is determined.The nonlinear state estimation algorithm is used to build the cable tunnel state prediction model.The embedded monitoring equipment is used to collect the cable tunnel state data,which provides the data source for the cable tunnel state prediction model.The model data is introduced into the database,the data clustering distance is calculated,the K-means clustering method is used to distinguish the fire hazard data,and it is transmitted to the main control terminal to complete the fire hazard early warning work.The simulation results show that the early warning false alarm rate of this method is less than 10%in the case of cable hidden danger or environmental hidden danger,and the accuracy of cable operation state estimation is higher,and the data transmission delay of fire hidden danger is shorter.