Thermal Rating Estimation Of Transmission Line Based On Parameter Identify

The lack of the capacity of the transmission line can not only cause thetransmission bottleneck problem, but also impact on the utilization efficiency of theoverall operation of the grid. In order to prove the efficiency of the overall operation ofthe grid and solve the problem of transmission bottlenecks, we must enhance thetransmission capacity of transmission lines. The rated capacity is a conservative valuebased on the worst weather conditions, so the actual carrying capacity of thetransmission line compared to the line rated capacity has a very large margin. Therefore,in the situation of the meteorological information data collection faces difficulties or thedata loss by transmission, it is important and significant to research on the assessmentmethods of circuit current carrying capacity.In this paper, according to the research on the capacity model of transmission line,we analyzed two calculation standards of carrying capacity, and introduced andanalyzed the main factors of the impact on conductor heat balance, such as sunshinefactor, lead resistance, cooling factor and so on.Reference simplified Morgan formulas and the equation of transmissionline dynamic heat balance, the dynamic simulation model of transmission line heatingand radiating is builded, which could truly reflect the process of transmissionline’s temperature and environment changes while operating.In the case of the monitoring equipment cannot be installed and the loss of datatransmission, in order to ensure the reliability of the assessment of the current carryingcapacity of transmissionlines and the decrease of the measuring devices cost, aassessment method of Identifying the transmission lines carrying capacity, which basedon parameter identification of the sunshine has been proposed.Based on the method basis of identify the parameters and assess the currentcarrying capacity of the transmission line. In this paper, we propose a online assessmentmethod of the current carrying capacity of the transmission line based on identifymulti-meteorological parameter. Using on-line temperature (line temperature andambient temperature) and line real-time current information use BP neural network torecognize sunshine and wind speed parameters to achieve line real-time assessment ofthe current carrying capacity.