Change Characteristics of Characteristic Gas for Oil Surface Under PD Insulation Defects in Oilless Equipment
Oilless equipment has the objective characteristics of very low oil quantity,fully sealed steely structure,and lack of explosion-proof measures.Long-term partial discharge(PD)insulation defects can induce equipment explosions,fires and other safety accidents,while oilless equipment has been lacking effective PD insulation defect detection and di-agnosis techniques.Based on this,this paper builds an experimental platform for oil surface gas detection of PD insulation defects in 110 kV positive and vertical current transformers,and experimentally investigates the characteristic oil surface gas variation law and its correlation characteristics with dissolved gas in oil under different PD strengths of oilless equipment.The test results show that the PD characteristic gases H2,CH4 and CO are significantly enriched on the oil surface and the concentration increase rate is much higher than that in the oil,so the PD detection based on the oil sur-face gas has higher sensitivity than the oil chromatography detection.When the PD insulation defect is seriously deteriorated,the dissolved gas concentration in the oil does not increase significantly and the key gas C2H2 does not reach the warning value,while the characteristic gas concentration on the oil surface all show a significant and sharp increase,among which the concentration of H2 and C2H2 is the most significant,with the concentration of C2H2 reaching 17.41×10-6.By capturing this change during the deterioration of the discharge defect,the occurrence of breakdown dis-charge can be effectively warned.The oil surface gas concentration can effectively characterize the generation and development of PD defects within oilless equipment.The results of this paper provide a new solution for the highly sensi-tive monitoring of PD insulation defects in oilless equipment.
oil less equipmentpartial dischargeoil surface gasdissolved gas analysisgas production pattern
国家科技期刊平台
NSTL
万方数据
