Search

How to use SF 6 decomposition feature component information to judge the form and degree of gas-insulated equipment (GIE) field discharge and overthermal faults quickly is a problem that remains unresolved. Based on the existing experimental data on SF 6 typical fault decomposition, this study considers the SF 6 decomposition mechanism under typical faults and uses the maximum correlation minimum redundancy criterion to filter out three decomposition feature components characterizing GIE typical fault attributes: SOF 2 +SO 2 , CF 4 , and SO 2 F 2. The weight of extracted feature components is optimized by the "area equivalence principle," and the triangle fault diagnosis method of the SF 6 decomposition component that is suitable for the rapid diagnosis of a GIE field is constructed. The diagnosis method is comprehensively tested using faulty data in different conditions, and the comprehensive recognition rate of lab tests reaches 96.2%. Results show that the constructed triangle fault diagnosis method of the SF 6 decomposition component can diagnose the internal fault nature of a GIE and identify the types of insulation defects that induce partial discharge faults. Moreover, the constructed method in this research is simple, effective, and suitable for field maintenance and online intelligent monitoring of GIE. [ABSTRACT FROM AUTHOR]

To investigate the difference between the sulfur hexafluoride (SF6) decomposition characteristics initiated by two sorts of insulation defects — free metal particles and metal protrusion defects — under negative DC partial discharge (PD), SF6 decomposition data was obtained under different DC voltages on the basis of constructing a SF6 decomposition experimental platform. The differences between the characteristic components and the characteristic ratios of the two defects at the same discharge quantity and voltage were systematically analyzed. Results show that the two defects have significant differences in the type of decomposition components, the yield sizes of the components, and the changes in the component ratios. This paper proposed c(CF4 + CS2)/c(CO2) and c(CF4)/c(SO2F2) as the characteristic ratios to characterize the fault severity of the two defects, and their physical nature were revealed. The combination of the above ratios with the existing c(SO2F2)/c(SOF2 + SO2) can construct a feature quantity for identifying the two defects. [ABSTRACT FROM AUTHOR]

To study the correlation characteristics between SF6 decomposition components and discharge strength under negative DC partial discharge (PD) initiated by free metal particle defect, SF6 decomposition data were obtained under different voltages on the basis of constructing SF6 decomposition experimental platform. The variation law of SF6 decomposition components and characteristic ratios were systematically analyzed. The study shows that SF6 decomposes into CO2, SO2F2, SOF2, SO2, CF4, and a very small amount of CS2 under PD. The SOF2 concentration and its formation rate have the highest correlation with the PD discharge quantity among all components. The yield relationship of decomposition components is different at different discharge quantities. In addition, the fluctuation of each characteristic ratio is small after 72 h and can achieve a certain degree of steady state. c(CF4+CS2)/c(CO2) and the proposed c(SO2)/c(SO2F2) can be used as the fault severity characteristic ratio in free metal particle defect. [ABSTRACT FROM PUBLISHER]

SF6 decomposition characteristics under partial discharge (PD) are not only associated with insulation fault type and its severity but also closely related to the H2O content of in the equipment. This paper studies the decomposition characteristics of mixture gas SF6/H2O with six different gradients of trace H2O under the same PD energy, selects SO2F2, SOF2, CO2, and CF4 as the characteristic components, and analyzes the influence regularity of trace H2O on each of the component concentration, effective formation rate and characteristic ratio. Results show that H2O creates favorable conditions for generating SO2F2 and SOF2 owing to the capacity to capture F atom and inhibit low fluorine sulfides from recombining into SF6. As a result, the concentration and effective formation rate of SO2F2 and SOF2is positively related with H2O. Whereas H2O has no evident influence on CO2, it can consume intermediate by-products CF2 and CF3 which restrain CF4 generation. Each characteristic ratio decreases sharply and then becomes stable with increment of H2O. The defined effective characteristic ratio effectively reflects its relationship with H2O, whereas obtained mathematical expression between each effective characteristic ratio and trace H2O lays solid foundation for fault diagnosis of SF6 insulated equipment based on SF62 decomposition characteristics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) with different energy always occurs before a serious breakdown in sulfur hexafluoride (SF6) insulating equipment. The high electromagnetic energy of PD can cause the insulation gas SF6 to decompose into various components. Component types and their formation rate are directly related with insulation fault severity. Hence, PD energy can be evaluated based on the characteristics of SF6 decomposition, which could be further used to diagnose insulation status. This paper focuses on the SF6 decomposition mechanism under PD, establishes the feature parameter to describe PD energy, and clarifies the feature parameter?s physical significance. In addition, this paper defines the effective energy characteristic ratio ERRMS and analyzes the relationship between ERRMS and PD energy. Finally, this paper discusses the influence regularity of trace H2O and O2 on ERRMS. [ABSTRACT FROM AUTHOR]

To obtain relationship between concentration of SF6 decomposed characteristic components (or their concentration ratio) during the formation process and partial discharge qualities and pulse repetition rate, this paper used needle-plate electrode to simulate outthrust insulation fault and conducted a serial of experiments. First, the SF6 decomposition characteristic under different PD strengths is obtained. Then this paper defines average discharge qualities per second as QSEC. It considers both apparent discharge qualities and discharge pulse repetition rate, and analyzes the relation feature between SF6 decomposition characteristic (both concentration of component and their effective formation rates RRMS) and QSEC. Besides, it proposes C(SO2F2)/C(SOF2) as the energy characteristic ratio (ER) to represent PD energy, and defines the effective energy characteristic ratio as (ERRMS). Moreover, it achieves the inner correlation feature between QSEC and all the characteristic component concentration, the RRMS of each characteristic component and ERRMS. The results show that: since number of C atoms from stainless steel surface and content of trace H2O and O2 in gas chamber were limited, both decomposed component concentration and component concentration ratio accords with the famous "Logistic Population Model" growth trend in ecology, which means they present ???S???-curve increase with QSEC. The proposed ER can effectively indicate the amount of PD energy and the defined ERRMS can objectively reveal the variance regularity of PD energy with QSEC. Hence, the correlation mathematic formula between ERRMS and QSEC is obtained and ERRMS can be used as the new characteristic parameter to represent PD energy. It establishes the foundation for the further fault diagnosis research. [ABSTRACT FROM PUBLISHER]