Abstract: To study the concentration law of pyrolysis particles and optimize the selection of pyrolysis particles detection parameters during the early stage of ignition of typical electrical insulation materials in Distribution Cabinets, this study established a pyrolysis particles detection test platform for three typical insulating materials in distribution cabinets. The platform simulates the low-temperature pyrolysis process of electrical materials, obtaining the number concentration laws of particles ranging from 0.3 to 10.0 μm and the mass concentration laws of PM0.5, PM1.0 and PM2.5. A pyrolysis particles concentration dataset was established. and based on this dataset, the random forest algorithm was used to obtain the importance of particle concentrations in different particle size ranges for early pyrolysis, thereby optimizing the selection of pyrolysis particle detection parameters. The results show that all three pyrolysis materials exhibit significant changes in the concentration of pyrolytic solid particles. The trend of the number concentration of pyrolysis particles is to maintain the baseline value first, then increases, and finally reaches saturation or decreases. The mass concentration shows a trend of first increasing and then decreasing. The particle size distribution is mainly concentrated within the range of 1.0 μm. Compared with ABS and FR-4 epoxy resin materials, PVC is more likely to generate large-sized particles. In the design of pyrolysis particle detection, the number concentration of particles larger than 0.3 μm is preferred as a single detection parameter, and the combination of the number concentration of particles larger than 1.0 μm and the PM2.5 mass concentration is recommended as dual detection parameters. This research can provide a basis for the application of the electrical fire detection technology of measuring pyrolysis particles in electrical fire monitoring system.

Key words: electrical insulation materials, particle number concentration, particle mass concentration, electrical fire detection, detection parameter