Sulfur Hexafluoride Gas Leakage Monitoring and Early-Warning Method for Electrical Power Facilities

Abstract

Most transformer substations in power supply facilities rely on sulfur hexafluoride electrical equipment. A sulfur hexafluoride gas leak can cause serious health concerns if effective measures are not adopted in time. Therefore, in this study, a sulfur hexafluoride gas leakage monitoring, early-warning, and emergency disposal model was established. First, taking the main transformer chamber of an underground transformer substation as the research object, a 3D-model was built, and a numerical simulation was performed. Second, the simulation results were utilized to determine the dispersion and concentration distribution of the sulfur hexafluoride gas, identify concentration-sensitive areas, and arrange sensors based on the simulation results, to ensure early-warning in case of leaks. Then, a sulfur hexafluoride gas leakage monitoring and early-warning model was built based on the data collected using sensors at the monitoring points; thereafter, a construction method was developed for a sulfur hexafluoride gas leakage emergency disposal model, which can be referenced to establish a leakage gas recycling system. This paper also provides some recommendations regarding the determination of the optimal conditions for this emergency recycling device, which can be utilized to maintain the concentration of sulfur hexafluoride gas below a specified value and to construct a recycling time prediction model. The results of the study can provide a theoretical basis for sulfur hexafluoride gas leakage early-warning and emergency disposal, which will contribute to the prevention of suffocation-related accidents.

Power-Sensitive Early Warning Data Conversion Method Combined with Multiobjective Differential Evolution Algorithm

“…e multiobjective gap evolutionary algorithm is one of the evolutionary heuristic algorithms. e algorithm mainly uses the differential method to complete the evolution of the species population, improve the search speed, and enhance the convergence [10,11]. Figure 1 shows the power-sensitive early warning data conversion process of the multiobjective gap evolution algorithm [12].…”