Research On Dynamic Capacity Increase Of Transmission Line Based On Weather Forecasting

With the development of society and economy,people’s demand for electricity is also growing.How to improve the transmission capacity of transmission lines is a problem that the power sector is more concerned about it.New or expanding transmission lines that not only requires more investment costs,but also is a longer construction period.Based on transmission dynamic capacity increase of on-line monitoring device is based on on-line monitoring system of line,dynamically analyzing the line can promote maximum ampacity of the line by monitoring the and fully tap the carrying capacity of transmission lines by monitoring temperature of the conductor and the ambient temperature,wind speed and sunshine intensity along the line.This paper first compares different calculation formulas of ampacity,then analyzes the factors that affect the current carrying capacity,mainly including sunshine intensity,conductor surface coefficient,wind speed,conductor allowable temperature and ambient temperature.Then,analyzes the feasibility of dynamic capacity technology,mainly from the perspectives of increasing the maximum allowable temperature of the wire,current carrying calculated boundary conditions the high temperature performance of metal materials to illustrate.Finally,through the study and prediction of historical meteorological data,establishing the main work based on gas,from the probabilistic model to analyze the maximum ampacity of the line which included the following three aspects:Firstly,this paper introduces the principle and algorithm of BP neural network,and then uses BP neural network to predict the meteorological factors of an area,such as ambient temperature,wind speed and sunshine intensity.Because BP algorithm predicted the error of ambient temperature,wind speed and sunshine intensity within the acceptable range,so the predicted temperature and wind speed data are used as source data for the subsequent model of the ampacity.Secondly,in order to obtain more accurate prediction results,the paper uses the optimized combination kernel limit learning machine(EMD-PSO-KELM)to predict the weather factors.Compared with EMD-KELM algorithm,PSO-KELM algorithm and BP algorithm,it can be seen that building up forecasting model based on ambient temperature,wind speed and sunlight intensity of EMD-PSO-KELM,the algorithm is more accurate in forecasting meteorological parameters and provided more reliable source data for establishment of the subsequent capacity probability model.Finally,introduces the definition of the Gaussian mixture model and its solution method to expect maximum algorithm(EM)to effectively solve the parameters in the Gaussian mixture model.The forecasting result of BP neural network and EMD-PSO-KELM as source data for the subsequent model of the ampacity.Based on the probabilistic modeling of the Gaussian mixture distribution of current carrying capacity,proposed a method of calculating the maximum current carrying capacity of transmission line based on meteorological parameters.The results of application analysis of a regional power grid showed that according to the prediction results of meteorological parameters to dynamically adjust the capacity of transmission lines during the peak period of power supply,and the transmission capacity of transmission lines can improve under the premise of ensuring the safety and reliability of transmission lines.