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Uncertainty Analysis of Dynamic Thermal Rating of Overhead Transmission Line
Xing Zhou,Yanling Wang,Xiaofeng Zhou,Weihua Tao,Zhiqiang Niu,Ailing Qu 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.2
Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambienttemperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty isan important parameter to evaluate the reliability of measurement results. This paper presents the uncertaintyanalysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probabilitydensity function of the input parameter value, the probability density function of the output value is determinedby probability distribution random sampling. Through the calculation and analysis of the transient thermalbalance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, thetransient current carrying capacity, the standard uncertainty and the probability distribution of the minimumand maximum values of the conductor under 95% confidence interval are obtained. The simulation resultsindicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, andincrease the validity and reliability of the uncertainty evaluation.
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Uncertainty Analysis of Dynamic Thermal Rating of Overhead Transmission Line
Zhou, Xing,Wang, Yanling,Zhou, Xiaofeng,Tao, Weihua,Niu, Zhiqiang,Qu, Ailing Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.2
Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambient temperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty is an important parameter to evaluate the reliability of measurement results. This paper presents the uncertainty analysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probability density function of the input parameter value, the probability density function of the output value is determined by probability distribution random sampling. Through the calculation and analysis of the transient thermal balance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, the transient current carrying capacity, the standard uncertainty and the probability distribution of the minimum and maximum values of the conductor under 95% confidence interval are obtained. The simulation results indicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, and increase the validity and reliability of the uncertainty evaluation.
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