A gas sensor is used to detect SF6 decomposed gases, which are related to insulation faults, to accurately assess the insulated status of electrical equipment. Graphene films (GrF) modified with Au nanoparticles are used as an adsorbent for the detection of H2S and SOF2, which are two characteristic products of SF6 decomposed gases. Sensing experiments are conducted at room temperature. Results demonstrate that Au-modified GrF yields opposite responses to the tested gases and is thus considered a promising material for developing H2S- and SOF2-selective sensors. The first-principles approach is applied to simulate the interaction between the gases and Au-modified GrF systems and to interpret experimental data. The observed opposite resistance responses can be attributed to the charge-transfer differences related to the interfacial interaction between the gases and systems. The density of states and Mulliken population analysis results confirm the apparent charge transfer in Au-modified GrF chemisorption, whereas the van der Waals effect dominates the pristine graphene adsorption systems. Calculation results can also explicate the significant SOF2 responses on Au-modified GrF. This work is important in graphene modulation and device design for selective detection.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.201500101

Experimental Sensing and Density Functional Theory Study of H2S and SOF2 Adsorption on Au-Modified Graphene.

SF6 insulation devices are important components in the power system, wherein the SF6 acts as the insulating gas protecting the operation state of devices effectively. However, the inevitable decomposition of SF6 under partial discharge in a long-running device would deteriorate the insulation property of SF6 largely. In this paper, we investigate the application of a Ru-doped InN (Ru-InN) monolayer as a novel gas adsorbent scavenging SF6 decomposed species based on a first-principles theory. Results indicate that the Ru-InN monolayer possesses quite strong adsorption behaviors upon three SF6 decomposed gases: SO2, SOF2, and SO2F2, wherein chemisorption can be identified. This allows exploration of the Ru-InN monolayer-based adsorbent for removing pollutant gases from SF6 insulation devices. Meanwhile, giving the obvious changes in conductivity of the Ru-InN monolayer caused by gas adsorption, the exploration of gas sensor using the Ru-InN monolayer would also be a means to evaluate the operation status of SF6 insulation devices. Our investigation indicates that the Ru-InN monolayer can be a good candidate for sensing or adsorbing to prevent paralysis of a power system caused by SF6 decomposition.

Ru-InN Monolayer as a Gas Scavenger to Guard the Operation Status of SF 6 Insulation Devices: A First-Principles Theory

Pristine and Cu decorated hexagonal InN monolayer, a promising candidate to detect and scavenge SF6 decompositions based on first-principle study.

First-principles study of SF 6 decomposed gas adsorbed on Au-decorated graphene

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.

Correlation analysis between formation process of SF 6 decomposed components and partial discharge qualities

Partial discharge (PD) is usually aroused before the failure of gas insulated switchgear (GIS) caused by defects, which results in the decomposition of the SF6 used as insulating gas. Analyzing SF6 decomposition products can aid in PD detection. Before recognizing PD types by analyzing SF6 decomposition products, the decomposition characteristics of SF6 under different types of PD should be investigated first. In this paper, a gas chamber and four typical types of artificial defects were designed to simulate the SF6 decomposition phenomenon under PD in GIS. A gas chromatography system was established to detect SF6 decomposition products. By selecting the chromatographic column, SF6 decomposition products were successfully separated and their concentrations were measured. Numerous SF6 decomposition experiments under four kinds of PD generated by four kinds of artificial defects were carried out and the SF6 decomposition products produced by each experiment were detected. The decomposition characteristics of SF6 under the four defects were different in terms of decomposition amount, generation rate, and concentration ratio, among others. It is feasible to recognize the type of PD by analyzing SF6 decomposition products.

Partial discharge recognition through an analysis of SF 6 decomposition products part 1: decomposition characteristics of SF 6 under four different partial discharges

The decomposition characteristics of the SF6 under the different kinds of partial discharges (PD) should be understood first when recognizing PD by analyzing SF6 decomposition products in gas insulated switchgear (GIS). Moreover, the characteristic quantities used for recognition must be found. In this paper, the concentration and concentration ratio of SF6 decomposition products were each selected as characteristic quantities. Fuzzy c-means clustering algorithm was adopted to assess the performance of the two types of characteristic quantities, which was based on the data of SF6 decomposition products under the four kinds of PD in Part 1. Concentration ratio had better performance than concentration as a characteristic quantity in PD recognition. The concentration ratio method for PD recognition was established based on the decision tree theory, in which the three concentration ratios, namely c(SOF2)/c(SO2F2), c(CF4)/c(CO2), and c(SOF2 +SO2F2)/c(CO2+CF4), were used as characteristic quantities. The physical significance of the three concentration ratios was also analyzed. Finally, the concentration ratio method was applied to test the performance of PD recognition. The method has a good performance and can successfully recognize different kinds of PD.

Partial discharge recognition through an analysis of SF 6 decomposition products part 2: feature extraction and decision tree-based pattern recognition

Qinghong Meng

Papers

Partial discharge recognition through an analysis of SF 6 decomposition products part 1: decomposition characteristics of SF 6 under four different partial discharges

Partial discharge recognition through an analysis of SF 6 decomposition products part 2: feature extraction and decision tree-based pattern recognition