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The accurate and effective detection of SF6 decomposition components inside a gas-insulated switchgear (GIS) is crucial for equipment fault diagnosis and condition assessment. The current method for detecting SF6 decomposition components involves gas extraction at the GIS inlet, which only provides limited information on the decomposition component content. Therefore, there is a need to explore more effective ways to obtain internal gas component information within GIS. In this study, we propose a graphene-coated microfiber gas detection method for SO2. We establish a physical simulation model of the microfiber and analyze the sensing mechanism of the microfiber diameter and cladding refractive index changes in its evanescent field. A graphene-coated microfiber gas sensor was prepared using a drop-coating method, and a fiber loop ring-down (FLRD) gas detection system was constructed for the experimental studies on SO2 gas detection. The results demonstrated that the graphene-coated microfiber exhibits an excellent gas-sensitive response to SO2 and achieves trace-level detection at room temperature. The concentration range of 0 to 200 ppm showed good linearity, with a maximum detection error of 4.76% and a sensitivity of 1.24 ns/ppm for SO2. This study introduces an all-fiber method for detecting SF6 decomposition components, offering a new approach for online monitoring of SF6 decomposition components in GIS equipment using built-in fiber-optic sensors.

With the development of two-dimensional materials such as graphene, transition metal dichalcogenides (TMDs), MXenes and MBenes, these materials have received extensive attention from scholars in the field of gas sensing due to their unique and superior properties. Based on first-principles calculations, the adsorption energy, charge transfer, density of states and deformation charge density of CrB, an MBene successfully synthesized under laboratory conditions, were investigated for the adsorption of the decomposition components (CF4, C3F6 and COF2) of an insulating medium, C4F7N. The calculation results revealed strong chemisorption with an adsorption energy as high as −3.336 eV between CrB and COF2, as well as physical adsorption with CF4 and C3F6. However, the excessive interaction strength makes it difficult for COF2 molecules to escape from the binding of the CrB substrate, making CrB more suitable as an adsorbent to remove COF2 gas. Compared with COF2 and CF4, CrB has appropriate adsorption energy and charge transfer for C3F6 adsorption, and its theoretical recovery performance is acceptable, indicating its potential as a sensor for detecting C3F6.

Accurate detection of the internal decomposition components of SF6 electrical equipment plays an important role in the evaluation of equipment status. However, gas samples are usually taken out for detection at present, which makes it difficult to understand the real situation inside the equipment. In this paper, a carbon nanotube-coated fiber gas sensor is proposed, which has the potential to be applied as a built-in gas sensor. The fiber loop ring-down (FLRD) gas detection system based on the carbon nanotube-coated fiber gas sensor was built, and the detectable decomposition components among the four typical SF6 decomposition components of SO2, SO2F2 and SOF2 and CO were analyzed. The results showed that the fiber gas sensor was most sensitive to CO. Based on density functional theory, it was found that single-walled carbon nanotubes had the best adsorption effect on CO molecules under the same conditions, with the adsorption energy reaching −0.150 Ha. The detection performance of the system for CO was studied, and the results showed that there was a good linear relationship between CO concentration and ring-down time: R2 was 0.984, the maximum inversion error of 0~200 ppm CO was 1.916 ppm, and the relative error was 4.10%. The sensitivity of the system was 0.183 ns/ppm, and the detection limit of the system was 19.951 ppm. The system had good stability, with the standard deviation of single-point repeatability being 0.00356, and the standard deviation of the long period of the experiment being 0.00606. The research results provide a new idea for the detection of SF6 decomposition components, and lay the foundation for the component detection method of built-in fiber sensor of SF6 electrical equipment.

Detection of Clostridioides difficile toxins in patients with gastroenteritis has increasingly been accomplished through the use of enteric multiplex syndromic panels. Comparisons of the performance of these panels to both direct-from-stool (DFS) and culture-enriched stools followed by polymerase chain reaction (PCR) methods in pediatric populations are limited. Here, we compare the performance of the Luminex xTAG® Gastrointestinal Pathogen Panel (GPP) to our DFS in-house real-time PCR (DFS RT-PCR) assay for the detection of C. difficile toxin gene, tcdB, using 2641 stool specimens collected from children enrolled in the Alberta Provincial Pediatric EnTeric Infection Team (APPETITE) study in Alberta, Canada. We used culture enrichment followed by in-house RT-PCR to resolve discordant results between the two assays. We found excellent agreement (k = 0.89) between the GPP and our DFS RT-PCR assay: the positive percent agreement between the two assays was 97%, and the negative percent agreement was 99%. GPP, a multi-analyte platform can easily be implemented into a routine diagnostic laboratory for detecting enteric pathogens including C. difficile.

Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision boundary, resulting in a decrease in the accuracy of fault diagnosis. Given this issue, this paper proposed a genetic algorithm (GA) optimized probabilistic SVM (GAPSVM) integrated with the fuzzy three-ratio (FTR) method, in which the GAPSVM can judge whether a sample is near the decision boundary according to its output probabilities and diagnose the samples which are not near the decision boundary. Then, FTR is used to diagnose the samples which are near the decision boundary. Combining GAPSVM and FTR, the integrated model can accurately diagnose samples near the decision boundary of SVM. In addition, to avoid redundant and erroneous features, this paper also used GA to select the optimal DGA features. The diagnostic accuracy of the proposed GAPSVM integrated with the FTR fault diagnosis method reached 86.80% after 10 repeated calculations using 118 groups of IEC technical committee (TC) 10 samples. Moreover, the robustness is also proven through 30 groups of DGA samples from the State Grid Co. of China and 15 practical cases with missing values.

Norovirus is a major pathogen identified in children with acute gastroenteritis (AGE), little is known about the strain’s diversity and their clinical severity. Stool and/or rectal swabs were collected from children ≤18 years of age recruited at emergency departments (ED), and a provincial nursing advice phone line due to AGE symptoms in the province of Alberta, Canada between December 2014 and August 2018. Specimens were tested using a reverse transcription real time PCR and genotyped by Sanger sequencing. The Modified Vesikari Scale score (MVS) was used to evaluate the disease severity. The objectives are to identify the Genogroup and Genotype distribution and to compare illness severity between the GI and GII genogroups and to complete further analyses comparing the GII genotypes identified. GII.4 was the genotype most commonly identified. Children with GII.4 had higher MVS scores (12.0 (10.0, 14.0; p = 0.002)) and more prolonged diarrheal (5 days (3.0, 7.8)) and vomiting (3.2 days (1.7, 5.3; p < 0.001)) durations compared to other non GII.4 strains. The predominant strain varied by year with GII.4 Sydney[P31] predominant in 2014/15, GII.4 Sydney[P16] in 2015/16 and 2017/18, and GII.3[P12] in 2016/17. Genogroup II norovirus strains predominated in children with AGE with variance between years; clinical severity associated with different strains varied with episodes being most severe among GII.4 infected children.

The healthy state of insulation in oil-impregnated bushings is traditionally evaluated by tanδ and capacitance at power frequency and mostly at 10 kV in the test standard. However, there has frequently been insulation accidents induced by moisture ingress (MI) for bushings that have passed the standard. The mechanism and new diagnostic features for MI into bushings were not distinct enough and an accurate test method is urgently needed research. To address this technical gap, a bushing model with a transparent sheath was designed and an ultrasonic humidifier device was adopted to simulate the environment of MI in bushings and recorded by digital camera. The parameters of dielectric dissipation factor, capacitance, partial discharge (PD), frequency domain response, and moisture content in oil were measured at room temperature with time. The results presented that both the increment dissipation factor at low frequency of 0.001 Hz and the increment dissipation factor of 1.2 Um could be used for detecting the earlier insulation defect of oil-impregnated paper (OIP) bushings. The phase resolved partial discharge (PRPD) can serve as the diagnostic basis of the severe state (S3) of insulation deterioration caused by MI into bushings around the phases of 0–117°, 151–303°, and 325–360°. The research findings would provide a useful reference for the condition diagnosis and maintenance of OIP bushings. Especially, the increment detection of Frequency Domain Spectroscopy (FDS) at the frequency of 1 mHz and 10 kHz was recommended firstly for the operative bushings in real sites.