Title :
Quantitative analysis of the influence of regularity of SF6 decomposition characteristics with trace O2 under partial discharge
Author :
Zeng, Fanzheng ; Tang, Ju ; Sun, Hongbin ; Pan, Jeng-Shyang ; Yao, Qiang ; He, Jinwei ; Hou, Xingzhe
Author_Institution :
State Key Laboratory of Power Transmission Equipment & System Security and New Technology
Abstract :
To obtain the influence regularity of trace O2 on SF6 decomposition characteristics under partial discharge (PD) and establish the foundation of using SF6 decomposed components to diagnose the insulation status of SF6 electrical equipment, first, a series of PD decomposition experiments with different O2 concentrations (%)were conducted on a special experiment platform. The variation regularity between the concentrations of characteristic components and the different content of O2 were obtained quantitatively. Second, the correlation coefficient r testing method was applied to identify the associated influence of trace O2 on the decomposed characteristic components and characteristic ratios. Based on this, an exponential regression model of the related characteristics was developed. Finally, the influence mechanism of trace O2 on SF6 decomposition progress was explained reasonably. The results show that under the PD caused by metal protrusion insulation fault without organic solid insulated materials, CO2, SO2F2, and SOF2 were the mainly decomposed components in the specific range of trace O2. Although no obvious influences of the trace O2 on the concentrations of CO2 and SOF2 as well as the ratio c(CO2)/c(SO2F2+SOF2), it is notable that the trace O2 is negatively and positively related to the concentration of SO2F2 and the ratio c(SOF2)/c(SO2F2), respectively. The exponential regression model can minimize the effect of trace O2 on the fault diagnostic result when using SF6 decomposed components.
Keywords :
Circuit faults; Correlation coefficient; Discharges (electric); Monitoring; Partial discharges; Sulfur hexafluoride; SF6; characteristic components; influenceregularity; partial discharge; regression model.; trace O2;
Journal_Title :
Dielectrics and Electrical Insulation, IEEE Transactions on
DOI :
10.1109/TDEI.2014.004255