Neural Network-based CF4 and SF6/CF4 Detection in High Altitude and Extreme Cold Regions

Abstract

In extreme cold regions, the need to carry multiple instruments to meet the demands for detecting varying concentration levels of CF4 gas within SF6 gas leads to inefficient field operations and high costs for instrument acquisition. To overcome this, an SF6 gas CF4 concentration detector utilizing pyroelectric detection technology was initially developed, capable of automatically switching among different ranges by selecting appropriate amplification resistances. Subsequently, two neural network models for temperature-pressure collaborative compensation, BP and PSO-BP, were introduced. Data for model predictions were supported by an effective simulated experimental platform, with results indicating the PSO-BP neural network's superiority over the BP network. The PSO-BP neural network's temperature-pressure collaborative compensation model was then embedded within the multi-range detection instrument for CF4 gas concentration. Simulation experiments demonstrated that the instrument maintains a detection error and repeatability within ±2% and 1.6% across small and large ranges, and within ±0.5% and 0.2% for mixed ratio ranges, respectively, under varying temperatures and pressures. This technological advancement significantly enhances maintenance operations within the power grids of cold regions.

Keywords

• cf4 gas concentration detection
• pyroelectric detection technology
• high altitude and extreme cold regions
• three-range
• pso-bp neural network model
• collaborative temperature-pressure compensation