Estimation And Prediction Of Transmission Line Load Capacity Based On Big Data Analysis

With electricity energy plays an increasingly important role in national economic production,electricity demand continues to expand,the technology of long-distance,high-capacity electricity transmission becomes increasingly prominent.The current transmission line ampacity limits restrict the transmission capacity line,estimation and prediction of transmission line load capacity based on big data analysis can enhance the efficiency of electricity transmission line and ensure adequate power supply under the premise of the security and stability operation of power grid.The article analyzes and compares the existing models,standards and formulas for transmission line load capacity estimation.Using statistical analysis methods to discuss the principles of boundary conditions for transmission line load capacity estimation based on big data analysis,then calculate the steady-state and transient-state transmission line capacity of different specifications of wire in Shandong province as an example.Aimed at the key parameter of transmission line load capacity estimation,the article sets up the model of transmission line temperature and sag using data correlation.Considering temperature,wind speed,load current,and other factors,the article realizes the multi-time scale prediction of transmission dynamic line load capacity based on online learning neural network and analyzes prediction error in Monte Carlo method.Compare the transmission line potential load capacity and operating load to reveal the important significance of estimation and prediction of transmission dynamic line load capacity,the result of estimation and prediction can provide reference for grid scheduling and line maintenance.Finally,a prediction system for dynamic line load capacity based on climate model using the innovational principle and Existing hardware is realized,test results confirm the feasibility of the system.