Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear.

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With the increase of both voltage level and the transmission capacity, more attention has been paid to the external insulation of transmission lines. As the main external insulation medium of transmission line, the air gap’s dielectric strength will change when there are floating conductors. In this paper, we established an experiment platform to study the effects of floating conductors, simulated by floating rod electrode, on breakdown voltage, breakdown time, electric field distribution and discharge physical process of long air gaps under lightning impulse. The results showed that the intervention of floating electrode reduced the breakdown voltage and breakdown time of long air gaps, and the position with the longest breakdown time was the same as the position with the lowest breakdown voltage. In addition, the intervention of floating electrode improved the spatial electric field and complexity of discharge physical process.

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