Dynamic Measurement and Prediction of Sulfur Hexafluoride Gas Weight Based on Non-Ideal Gas State Equation and Improved Random Forest

Abstract

Real-time knowledge of sulfur hexafluoride (SF6) gas weight can effectively manage and maintain the power equipment using SF6. Firstly, the gas pressure and temperature sensors are utilized to measure SF6 gas pressure and temperature dynamically. Secondly, the measured gas pressure and temperature values are converted to the SF6 gas weight using equations for calculating non-ideal gas states. Finally, the dynamic prediction model of the SF6 gas weight is developed by combining the improved chimp optimization algorithm and random forest algorithm. The dynamic prediction model is trained using measured pressure and temperature data as inputs and the calculated SF6 gas weight as outputs. The trained prediction model enables the dynamic prediction of SF6 gas weight. The experimental results show that the mean absolute error, root mean square error, and mean absolute percentage error of the proposed prediction model reaches 51.54 kg, 103.59 kg, and 11.62%.