Method for Recognizing Partial Discharge Types in Air-Insulated Switchgear Based on CO/NO2 Gas Component Ratio

The safe and stable operation of air-insulated switchgear (AIS) in high-altitude and low-pressure environments is significantly affected by partial discharge (PD), which accelerates insulation aging and may threaten power system reliability. Therefore, effective online monitoring and fault diagnosis methods are of considerable engineering importance. This paper proposes a PD-type recognition method based on the concentration ratio of two characteristic decomposition gases, CO and NO2. First, a hybrid numerical model coupling fluid dynamics and plasma chemistry was established to simulate the microscopic decomposition mechanism of air discharge. The simulation results indicate that CO and NO2 are relatively stable and detectable among the considered air-discharge products and that their generation is promoted by increased average electron energy under low-pressure conditions. Subsequently, an experimental platform was developed to simulate three typical insulation defects, namely point discharge, air-gap discharge, and surface discharge, under different simulated altitudes. Quantitative analysis using Fourier-transform infrared spectroscopy and gas chromatography revealed clear correlations between defect type and gas concentration characteristics. Based on these results, a diagnostic criterion was established under the tested conditions: a CO/NO2 concentration ratio less than 1 indicates the epoxy-resin-based surface discharge model, whereas a ratio greater than 1 indicates point discharge or air-gap discharge. The latter two types can be further distinguished according to the time-dependent increasing trend of the ratio for air-gap discharge. Finally, based on the observed diffusion characteristics of these gases in the laboratory switchgear model, a low-cost online detection prototype using semiconductor gas sensors was developed. Laboratory validation using three typical single-defect models showed that the proposed method achieved 100% recognition accuracy when sufficient time-series data were available. However, further field validation is required before large-scale industrial application. The proposed CO/NO2 ratio method provides a potential low-cost auxiliary diagnostic approach for AIS insulation monitoring, particularly under high-altitude and low-pressure conditions.