Abstract
Dynamic thermal rating (DTR) of transmission lines is related to wind speed, wind direction, ambient temperature, and so on. Among the environmental parameters, there is a difference between the obtained environmental parameters and the true value. Therefore, only the deterministic values of environmental parameters and DTR are not accurate enough. Considering the environmental parameters obtained with uncertainty, the uncertainty of environment parameters based on Monte Carlo Method (MCM) is studied in this paper. According to the heat balance equation of transmission lines, the uncertainty analysis of transmission line ampacity is realized based on CIGRE standard. The best estimation value, standard uncertainty, and confidence interval are obtained under a given confidence level of environmental parameters. The experimental results show that DTR can fully improve the transmission capacity of transmission lines, and MCM is an effective method to assess uncertainty of DTR.
Highlights
Dynamic thermal rating (DTR) of transmission lines based on actual environmental parameters can greatly improve line capacity [1]
According to the International Council on Large Electric Systems (CIGRE) standard based on the environmental
6 Conclusions In order to verify the reliability of the DTR of transmission lines, the DTR model based on CIGRE standard is given
Summary
Dynamic thermal rating (DTR) of transmission lines based on actual environmental parameters can greatly improve line capacity [1]. Without reconstructing the existing transmission lines, DTR can ease the contradiction between electricity consumption and power supply and improve line utilization with great economic benefits. DTR can be determined by line ampacity calculation model based on CIGRE standard [2,3,4]. The ambient environmental parameters of transmission lines are significant factors that affect the DTR, but the difference between the measured value and the true value cannot be ignored, and the uncertainty of DTR needs to be evaluated [5,6,7,8]. Guide to the expression of uncertainty in measurement (GUM) gives the basic method of assessing uncertainty [9, 10]. The method is limited by certain conditions: (1) the probability distribution of the input quantity is assumed to be symmetrical, approximately normal distribution or T distribution; (2)
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