Search

Power transformer is regarded as one of the crucial part of electrical power transmission and distribution system. The quality of transformer oil can directly affect the operation of the power transformer, and breakdown voltage (BDV) and water content are the two main parameters of transformer oil quality. Monitoring the BDV and water content of transformer oil is considered as an important method to evaluate the safe operation of power systems. This work proposes the measurement of BDV and water content in transformer oil using multi frequency ultrasonic and generalized regression neural network (GRNN). The BDV and water content of all 210 samples were firstly tested according to the traditional testing methods and the multi frequency ultra-sonic technology, separately. And then the 210 samples were randomly divided into training sets and test sets. The obtained multi frequency ultrasonic data were set as the input of GRNN, and the BDV and water content as the output of GRNN. Moreover, the 20-fold-cross-validation was incorporated to obtain the best smoothing factor δ for GRNN. Finally, the GRNN model was trained by the training sets with δ =4.54 and was evaluated with the test sets. All results show that the lower BDV or the higher water content of the sample will cause greater ultrasonic sound attenuation, and the prediction accuracy of the prediction model for BDV and water con-tent in oil is up to 95%. It provides a new method for evaluating the health of transformer oil.

SF6decomposition product analysis is one of the most convenient and efficient methods to diagnose the potential faults of SF6insulated electric equipment in the early stage. Based on SF6decomposition characteristic gas analysis, the operation state of SF6insulated power equipment can be judged by on-line monitoring, so as to ensure its normal operation. Raman spectrum analysis technology can realize the nondestructive detection of gas samples with a single wavelength laser. It has excellent applicability and high efficiency for the detection of SF6decomposition characteristic components. In this paper, molecular configurations of CF4, CO, H2S, SO2were obtained by B3LYP functional that based on density functional theory (DFT), and the Raman frequency and intensity characteristics were calculated by 6-31G (2df, p) basis group. The results were compared with National Institute of Standards and Technology (NIST) standard frequencies and it was found that the optimized configuration has no virtual frequency. The characteristic peaks of CO, CF4, SO2and H2S that were identified by Raman spectrum are respectively 2221.11, 908.97, 1175.24 and 2688.82 cm-1, which are basically consistent with the corresponding NIST standard values. This study not only shows that the Raman spectrum of SF6decomposition products calculated by B3LYP functional function is reliable, but provides a reference for the quantitative detection of SF6decomposition products based on Raman spectrum.

Four MoO3 samples with the Zn element doped mole ratio of 0%, 3%, 6% and 9% were prepared by applying a convenient one-step hydrothermal synthesis method, and the samples presented three-dimensional microflower-like structures assembled by nanosheets. The systematic analysis results indicate that the 6 mol% Zn-doped sample had the most amazing detection characteristics for 50 ppm CO, with a response of 31.23 (4 times of the pure MoO3) at a lower temperature (240 °C). The large specific surface area, big pore size and tiny particle size were proved to be beneficial to the gas sensitivity, which promotes gases transfer and provides more gases landing points during the gas-sensitive reaction. Besides, based on the density functional theory (DFT), the facilitation effect of doped Zn on the adsorption ability of MoO3 to CO molecule was verified from the perspective of electronic properties. The excellent gas-sensing performances indicate that the 6 mol% Zn-doped MoO3 has potential to be applied as a reliable CO sensor.

Cross-calibration is a wildly used approach to radiometrically calibrate satellite sensors to meet uncertainty requirements. However, its accuracy is greatly influenced by bidirectional reflectance distribution function (BRDF) effects. To understand such influences, we analyzed the long-term BRDF characteristics of two commonly used cross-calibration targets (‘bright’ desert and ‘dark’ forest) using long-term (2002 – 2016) Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF products. BRDF characteristics derived by MODIS observations of both land cover types had 365-day cycles. Furthermore, both solar and sensor zenith angles showed more influence on BRDF than relative azimuth angles, and BRDF variations were approximately 10 times sensitive when solar or sensor zenith angles surpassed the boundary of 75°, which is therefore not recommended for selecting image pairs between target and reference satellite sensors for cross-calibration. Due to cloud cover, three prevailing interpolation methods (nearest, linear, and spline interpolation) were also assessed in their capacity to fill temporally missing BRDF products. Linear interpolation was preferred when valid MODIS scenes were more than two in five days, while nearest interpolation outperformed the other two methods for no more than two valid MODIS scenes in five days. In addition, BRDF look up tables (LUTs) were established for radiometrically cross-calibrating satellite sensors when BRDF products were unavailable. The LUTs were further validated with Landsat-8/Operational Land Imager (OLI) top of atmosphere (TOA) radiance simulated by the Second Simulation of the Satellite Signal in the Solar Spectrum model (6S) with BRDF effects corrected using the BRDF LUTs and MODIS BRDF products for typical geometries at two sites. High consistency was achieved with mean biases less than 0.3%, which suggests that BRDF LUTs could be used as alternatives when BRDF products are unavailable. These results provide guidelines to improve the accuracy of cross-calibration including image-matchup selecting criteria, efficient BRDF effect removal, and selection of potential calibration sites.

As an insulating medium, sulfur hexafluoride (SF6) is extensively applied to electrical insulation equipment to ensure its normal operation. However, both partial discharge and overheating may cause SF6 to decompose, and then the insulation strength of electrical equipment will be reduced. The adsorption properties and sensing mechanisms of four SF6 decomposed components (HF, SO2, SOF2 and SO2F2) upon an Au-modified InN (Au-InN) monolayer were studied in this work based on first-principles theory. Meanwhile, the adsorption energy (Ead), charge transfer (QT), deformation charge density (DCD), density of states (DOS), frontier molecular orbital and recovery property were calculated. It can be observed that the structures of the SO2, SOF2 and SO2F2 molecules changed significantly after being adsorbed. Meanwhile, the Ead and QT of these three adsorption systems are relatively large, while that of the HF adsorption system is the opposite. These phenomena indicate that Au-InN monolayer has strong adsorption capacity for SO2, SOF2 and SO2F2, and the adsorption can be identified as chemisorption. In addition, through the analysis of frontier molecular orbital, it is found that the conductivity of Au-InN changed significantly after adsorbing SO2, SOF2 and SO2F2. Combined with the analysis of the recovery properties, since the recovery time of SO2 and SO2F2 removal from Au-InN monolayer is still very long at 418 K, Au-InN is more suitable as a scavenger for these two gases rather than as a gas sensor. Since the recovery time of the SOF2 adsorption system is short at 418 K, and the conductivity of the system before and after adsorption changes significantly, Au-InN is an ideal SOF2 gas-sensing material. These results show that Au-InN has broad application prospects as an SO2, SOF2 and SO2F2 scavenger and as a resistive SOF2 sensor, which is of extraordinary meaning to ensure the safe operation of power systems. Our calculations can offer a theoretical basis for further exploration of gas adsorbent and resistive sensors prepared by Au-InN.

The detection of oil-dissolved gases (such as CO, CH4 and H2) in transformers on-line is an important technology to predict and judge the operating status of transformers. Microstructure and gas sensing performance of Au-MoS2 microspheres synthesized by hydrothermal method were investigated in the present research. The results indicated Au-MoS2 based gas sensor has excellent response to CO, CH4 and H2 gases with maximum responses of 25.48, 16.75 and 8.48 at 30 ppm in their optimum working temperature (150 °C, 150 °C, 175 °C). In addition, First-principle calculation was carried out to expound the sensing mechanism in detail. The conclusions of this study suggest that Au-MoS2 monolayer exhibits a promising applicable future in sensor detecting of oil-dissolved gases in power transformer.

The influence of transition metal (TM) atoms (Au, Ag) doping on the structure parameters and electronic properties of MoS2 monolayer was studied based on DFT calculations. In order to realize the adsorption mechanism, the adsorption structures, adsorption energy, charge transfer, density of states (DOS), and electron density difference of water (H2O) molecule absorbed on two kinds of doped-MoS2 monolayer were analyzed. The results show that the electronic properties of TM (Au, Ag) doped-MoS2 monolayer have significantly changes and the chemical activity is obviously enhanced compared with the MoS2 monolayer. Moreover, Au or Ag doped-MoS2 monolayer both exhibits excellent adsorption capacity to H2O molecule and the processes of absorption both are chemical.

In this work, we reported the successful synthesis of a novel two-dimensional porous NiO nanosheet via a one-step hydrothermal route and subsequent calcination. It was surprise found that such NiO nanosheet exhibits a superior gas response towards H2. The enhanced sensing ability may be benefited from the porous nanostructure, resulting in more sufficient pathways for gas diffusion and larger surface for chemical reaction. We hoped our findings could be in favor of further investigations on the fabrication of perfect two dimensional architectures.

In this paper, we reported the morphology controllable synthesis of hierarchical WO3 nanostructures , i.e. nanorods , nanospheres and nanoflowers, via a facile hydrothermal route. All the obtained WO 3 nanostructures were characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET), respectively. A possible growth mechanism for the three various nanomaterials was proposed in detail. WO 3 based gas sensors were fabricated with the synthesized nanomaterials and the gas sensing performances to acetylene (C2H2 ), one of the fault characteristic gases dissolved in power transformer oil, were systematically measured. It was found that the sensor based on nanosheet-assembled nanoflowers with largest surface (56.74 m 2g−1) exhibits the highest sensing performance including gas response (32.31) and response-recovery time (12 s, 17 s) to 200 ppm C2H2. The results indicate that WO3 sensing materials could be a promising choice for synthesizing high-performance C2H2 sensors for the judgement of the early latent faults of the oil immersed transformer.

This work studies the adsorption properties of Pd(1 1 1) surface to various SF6 decomposition components (H2S, SO2, SOF2, and SO2F2). Due to the affluent active d electronic of Pd transition metal, the density functional theory has been used to analyze the adsorption properties of Pd(1 1 1) surface to H2S, SO2, SOF2, and SO2F2, including the adsorption structures, adsorption energy, and charge transfer. The results show that the adsorption abilities of these gases on the Pd(1 1 1) surface is in the following order: SO2 > H2S > SOF2 > SO2F2, and all of the adsorption processes are exothermic. Besides, the density of states is further calculated to analyze the interaction mechanism during the adsorption processes. We conclude that SO2 and H2S interact with Pd(1 1 1) surface by physisorption. SOF2 and SO2F2 tend to adsorb on Pd(1 1 1) by molecular adsorption, as the energy barriers of dissociation adsorption process is too high for the SOF2 and SO2F2 molecules to cross.

Transformer oil is an important insulating material in power transformers. The detection of transformer oil is an important means to ensure the normal operation of the power system, which is recognized by the public. Interfacial tension (IFT) is an important parameter to characterize the flow properties of liquids. Too low interfacial tension of transformer oil can cause major accidents in transformers. Therefore, it is of great practical significance to achieve effective detection of interfacial tension. This work proposes the measurement of the IFT of transformer oil using multi frequency ultrasonic and a support vector machine that was optimized by particle swarm optimization algorithm (PSO-SVM). 210 samples, which were detected by multi frequency ultrasonic and the Ring Method, were divided into training sets (200 samples) and test sets (10 samples). Then multi-frequency ultrasonic data is subjected to MDS dimensionality reduction to obtain 11 dimensional low-dimensional data which as the input of SVM, and the IFT, which was detected by the Ring Method, as the output of SVM. After that, a particle swarm optimization algorithm (PSO) was incorporated to optimize the parameters (C, g) for a support vector machine (SVM).Then, the SVM model with the optimized parameters (g=1.356 and C=2.831) was trained with the training sets, and the model was verified with the test sets. Results show that the model, which proposed in this article, that describes the nonlinear relationship between multi frequency ultrasonic data and the interfacial tension of transformer oil shows higher accuracy.

In order to find an excellent sensing material for dissolved gases in transformer oil, the adsorption structures of intrinsic graphene (IG), Ge-doped graphene (GeG), and Cr-doped graphene (CrG) to H2 and C2H2 gas molecules were built. It was found that the doping site right above C atom (T) was the most stable structure by studying three potential doping positions of the Ge and Cr atom on the graphene surface. Then, the structural parameters, density of states, and difference state density of these adsorption systems were calculated and analyzed based on the density functional calculations. The results show that the adsorption properties of GeG and CrG systems for H2 and C2H2 are obviously better than the IG system. Furthermore, by comparing the two doping systems, CrG system exhibits more outstanding adsorption performances to H2 and C2H2, especially for C2H2 gas. Finally, the highest adsorption energy (&minus, 1.436eV) and the shortest adsorption distance (1.981 &Aring, ) indicate that Cr-doped graphene is promising in the field of C2H2 gas-sensing detection.

First-principle calculations were carried out to simulate the three decomposition gases (SO2, SOF2, and SO2F2) of sulfur hexafluoride (SF6) on Ga-doped MoS2 (Ga-MoS2) monolayer. Based on density functional theory (DFT), pure MoS2 and multiple gas molecules (SF6, SO2, SOF2, and SO2F2) were built and optimized to the most stable structure. Four types of Ga-doped positions were considered and it was found that Ga dopant preferred to be adsorbed by the top of Mo atom (TMo). For the best adsorption effect, two ways of SO2, SOF2, and SO2F2 to approach the doping model were compared and the most favorable mode was selected. The adsorption parameters of Ga-MoS2 and intrinsic MoS2 were calculated to analyze adsorption properties of Ga-MoS2 towards three gases. These analyses suggested that Ga-MoS2 could be a good gas-sensing material for SO2 and SO2F2, while it was not suitable for SOF2 sensing due to its weak adsorption. This work provides a theoretical basis for the development of Ga-MoS2 materials with the hope that it can be used as a good gas-sensing material for electrical equipment.

During the COVID-19 epidemic in Wuhan, China, a series of measures were implemented by the government to prevent the spread of disease, including the lockdown policy and construction of emergency h...

It is essential to detect the characteristic gas products of SF 6 decomposition for fault diagnosis of gas-insulated switchgear (GIS). The model of Rh doped MoS 2 was built by Materials Studio software. The micro process of three SF 6 partial dissociation products (SO 2 , SOF 2 , SO 2 F 2 ) approaching the Rh-MoS 2 surface and reaching a stable state was simulated. The modification effect of Rh doped MoS 2 was studied by the key adsorption parameters such as the adsorption structure, bandgap, absorption energy and charge transfer. As a result, the electrical property and chemical activity are significantly improved in comparison with the pristine MoS 2 monolayers by selecting the most stable Rh doping site. Simultaneously, it was found that Rh-MoS 2 monolayer had good gas sensitivity to SO 2 and SOF 2 with chemical adsorption.

Power transformers are important assets in power grid. Winding deformation is one of main fault types of transformer internal failures. The accurate diagnosis of transformer winding deformation is significant and meaningful. In this study, an improved method of classifying winding deformation types is proposed. The polar plot is first plotted by using the amplitude and phase information of the measured frequency response analysis (FRA) traces, then the digital image processing technology is used to extract three image texture features from polar plot, and three support vector machines (SVMs) are independently trained by using the extracted texture features. As a result, a strong classifier is eventually obtained by combining the three trained SVMs, for fault type classification and recognition. The proposed method is verified on the experimental FRA data obtained from an actual model transformer, which demonstrates that the proposed method has more excellent performance compared with the traditional method based on the FRA trace and single SVM.

As a typical n-type semiconductor, MoO3 has been widely applied in the gas-detection field due to its competitive physicochemical properties and ecofriendly characteristics. Volatile organic compounds (VOCs) are harmful to the atmospheric environment and human life, so it is necessary to quickly identify the presence of VOCs in the air. This review briefly introduced the application progress of an MoO3-based sensor in VOCs detection. We mainly emphasized the optimization strategies of a high performance MoO3, which consists of morphology-controlled synthesis and electronic properties functional modification. Besides the general synthesis methods, its gas-sensing properties and mechanism were briefly discussed. In conclusion, the application status of MoO3 in gas-sensing and the challenges still to be solved were summarized.

SnO2 based sensors has received extensive attention in the field of toxic gas detection due to their excellent performances with high sensitivity, fast response, long-term stability. Volatile organic compounds (VOCs), originate from industrial production, fuel burning, detergent, adhesives, and painting, are poisonous gases with significant effects on air quality and human health. This mini-review focuses on significant improvement of SnO2 based sensors in VOCs detection in recent years. In this review, the sensing mechanism of SnO2-based sensors detecting VOCs are discussed. Furthermore, the improvement strategies of the SnO2 sensor from the perspective of nanomaterials are presented. Finally, this paper summarizes the sensing performances of these SnO2 nanomaterial sensors in VOCs detection, and the future development prospect and challenges is proposed.

Oil-immersed power transformers are considered to be one of the most crucial and expensive devices used in power systems. Hence, high-performance gas sensors have been extensively explored and are widely used for detecting fault characteristic gases dissolved in transformer oil which can be used to evaluate the working state of transformers and thus ensure the reliable operation of power grids. Hitherto, as a typical n-type metal-oxide semiconductor, tungsten trioxide (WO3) has received considerable attention due to its unique structure. Also, the requirements for high quality gas detectors were given. Based on this, considerable efforts have been made to design and fabricate more prominent WO3 based sensors with higher responses and more outstanding properties. Lots of research has focused on the synthesis of WO3 nanomaterials with different effective and controllable strategies. Meanwhile, the various morphologies of currently synthesized nanostructures from 0-D to 3-D are discussed, along with their respective beneficial characteristics. Additionally, this paper focused on the gas sensing properties and mechanisms of the WO3 based sensors, especially for the detection of fault characteristic gases. In all, the detailed analysis has contributed some beneficial guidance to the exploration on the surface morphology and special hierarchical structure of WO3 for highly sensitive detection of fault characteristic gases in oil-immersed transformers.

High-precision radiometric calibration (RC) coefficients are required to retrieve reliable water quality parameter products in turbid inland/coastal waters. However, unreliable RC coefficients when satellite sensors lack accurate and in-time RC may lead to pronounced uncertainties in the products through error propagation. To address this issue, a novel approach for estimating water quality parameters, taking suspended particulate matter (SPM) as a case, was proposed by coupling the procedures of RC and SPM model development. The coupled models were established using digital numbers (DNs) from target sensors and “in-situ” SPM measurements from concurrent well-calibrated reference sensors, with the RC coefficients introduced as unknown model parameters. The approach was tested and validated in varied Chinese inland/coastal regions, including the Hongze lake (HL), Taihu lake (TL), and Hangzhou bay (HB). The results show: (1) the DN-based SPM models can achieve a degree of accuracy comparable to reflectance-based SPM models with determination coefficients (R2) of 0.94, 0.92, and 0.72, and root-mean-square errors (RMSE) of 7.02 mg/L, 15.73 mg/L, and 619.2 mg/L for the HL, TL, and HB, respectively, and the biases less than 3% between the derived and official gain RC coefficients; (2) the uncertainty of SPM products increases exponentially as the RC uncertainty increases for exponential reflectance-based SPM models; (3) the DN-based SPM models are less sensitive to the uncertainties of atmospheric correction and RC coefficients, while the reflectance-based models suffer deeply. This study provides encouraging results to the improvement of SPM retrieval using the DN-based models by coupling RC and SPM retrieving processes, especially for sensors without precise RC coefficients.

C2H6 and C2H4 gases are two typical decompositions produced by partial discharge of transformer oil. To fully evaluate the feasibility of MoS2-based materials for the detection of C2H6 and C2H4 gases, the adsorption of C2H6 and C2H4 molecules on intrinsic and Au-doped MoS2 monolayer have been studied in this paper by the First-principle of Density Functional Theory (DFT). The adsorption mechanism of MoS2-based monolayer were investigated carefully in terms of adsorption energy, adsorption distance, bandgap structure, charge transfer and density of states (DOS). The calculated results show that the adsorption structures of the C2H6 and C2H4 molecules on Au-doped MoS2 monolayer with larger adsorption energies were stable, and have shorter adsorption distance, higher charge transfer, and stronger orbital hybridization compared with the corresponding MoS2 monolayer adsorption structures. It is concluded that the doped-Au atom affects the electronic structure of MoS2 monolayer to enhance the adsorption capacity. From this aspect, the present research offers a theoretical guidance to the application of Au-doped MoS2 materials as the sensing material for C2H6 and C2H4 gases.

SF6 decomposition components detection plays an important role in diagnosing and evaluating the electric discharge faults in SF6-insulated equipment. In this paper, Pt3-supported TiO2(1 0 1) was used to study the influence of the Pt clusters modification on the gas sensing property of TiO2(1 0 1) material to the characteristic decomposition components of SF6: H2S, SO2, SOF2 and SO2F2. The theoretical calculation results show that the intrinsic TiO2(1 0 1) only shows adsorption performance to H2S. Due to the strong chemical activity of Pt3, it significantly increases the electrical conductivity and gas sensing properties of TiO2(1 0 1). SF6 decomposition components adsorption decreases the electrical conductivity of the adsorption system. According to the different change degrees of electrical conductivity, it can be used to recognize the type and concentration of each SF6 decomposition component. By building the relationship between the type and concentration of SF6 decomposition components, and the type and severity of discharge faults, it provides an effective online way to diagnose and evaluate the discharge faults in SF6-insulated equipment.

SF6 insulation gas decomposes to various decomposition products: SO2, SOF2, SO2F2, because of partial discharge in SF6-insulated equipment. In order to ensure the running stability of equipment, Ni(1 1 1) adsorbent material is applied to remove these decomposition products. The adsorption properties of SF6 decomposed gas (SO2, SOF2, SO2F2) on Ni(1 1 1) surface are calculated to analyze its adsorption capacity and mechanism based on the density functional theory. The calculation results show that all adsorption processes are exothermic. The adsorption capacity of Ni(1 1 1) surface to SO2 is obviously larger than that to SOF2 and SO2F2. Though there are 0.54 eV and 0.61 eV reaction energy barrier for SOF2 and SO2F2 respectively, it is still easy to be crossed, resulting in the good adsorption of Ni(1 1 1) to SOF2 and SO2F2. The charge transfer, the density of states and the molecular orbital during the adsorption process were analyzed in order to analyze the adsorption mechanism. This study provides constructive suggestions to prepare Ni(1 1 1) sorbent for SF6 decomposition component removal.

A systematic investigation on the effects of grain boundary configuration and characteristics on the demagnetization process and magnetic properties of NdFeB magnets is carried out by micromagnetic simulation based on a periodic anisotropic model. The results indicate that, when the grain boundary phase is perpendicular to the easy axis, the demagnetization field is along the positive Z axis and the magnetic moments rotate difficultly due to the dipolar coupling. When the grain boundary phase is parallel to the easy axis, the demagnetization field is along the negative Z axis and the magnetic moments rotate easily. The best hard magnetic properties can be obtained by reducing the thickness and saturation magnetization of grain boundary phase distributed parallel to the easy axis and reducing the exchange stiffness of grain boundary phase perpendicular to the easy axis.

Hierarchical three dimensional MoS2nanostructures composed of small sheet-like nanostructures are successfully prepared using a simple two-step hydrothermal process with sodium dodecyl sulfate (SDS), as a surface modifier. The as-preparedMoS2nanospheres are characterized by XRD, BET, XPS, EDS, SEM and HRTEM. The gas–sensing properties of as–prepared MoS2nanostructures are tested at 175–230 °C temperatures for 50–500 ppm concentration range, using an efficient ceramic-tube sensor system via an intelligent gas testing system. A high response and selectivity towards carbon monoxide than other gases is observed. Comparative studies of MoS2nanostructures for different concentration of carbon monoxide gas and definite sensor-stability for different gases are studied, as well. The sensor fabrication and sensing mechanism is discussed as well.

In order to monitor the insulation status of SF6-insulated equipment on-line, SOF2 and SO2F2, two typical decomposition products of SF6 under electric discharge condition, are chosen as the target gases to evaluate the type and severity of discharge. In this work, single N atom doping method is adopted to improve the gas sensitivity of single wall carbon nanotubes to SOF2 and SO2F2. Single and double gas molecules adsorptions are considered to completely analyze the adsorption properties of N-doped single wall carbon nanotubes. Calculation results show that N atom doping enhances the surface activity of carbon nanotubes. When gas molecules physically adsorbed on N-doped single wall carbon nanotubes, the weak interaction between gas molecules and N-doped single wall carbon nanotubes nearly not changes the electrical property according to analysis of the density of states and molecular orbitals. While the chemisorption between gas molecules and N-doped single wall carbon nanotubes distinctly decreases the conductivity of adsorption system.

Herein, we report the synthesis, characterization, and gas sensing applications of Pt nanoparticles-decorated SnO 2 nanoneedles synthesized through a facile hydrothermal process. The synthesized nanoneedles were characterized for their morphological, structural, compositional and sensing properties using different characterization techniques. The morphological and structural characterizations confirmed the synthesis of well crystalline Pt nanoparticles decorated SnO 2 nanoneedles with tetragonal rutile crystal phase. X-ray photoelectron spectroscopic analysis (XPS) confirmed the spatial distribution of Pt metal into SnO 2 nanoneedles. Further, gas sensor applications of the synthesized nanoneedles were studies at different operating temperatures and concentrations of the CO gas. The detailed CO gas sensing analysis revealed that at an optimized temperature of 250 °C, the sensor exhibited 23.18 gas response with the response and recovery times of 15 s and 14 s, respectively. The long-term stability and the selectivity of the 3.125 at% Pt-decorated SnO 2 nanoneedles were also explored. Finally, a plausible gas sensing mechanism was also proposed.

Herein, a facile single-step hydrothermal method is reported for the synthesis of Ni and Zn doped SnO 2 nanomaterials, i.e. nanoparticles and nanosheets. The synthesized pure and doped SnO 2 nanomaterials were characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The diffraction planes from the XRD analysis correspond to the tetragonal rutile crystal phase of doped SnO 2 . A slight shift in the diffraction angles for doped SnO 2 nanomaterials as compared to pure SnO 2 confirms the replacement of the Sn 4+ with Ni 2+ and Zn 2+ ions. The SEM analysis revealed that SnO 2 and Ni-doped SnO 2 possess nanoparticle shaped morphologies while the Zn doped SnO 2 exhibited sheet-like structures. The compositions of the pure SnO 2 , Ni and Zn doped SnO 2 nanomaterials were ascertained by XPS studies which confirmed the purity and doping of SnO 2 nanomaterials. Further, gas sensor applications of synthesized Ni and Zn doped SnO 2 nanomaterials were evaluated at different operating temperatures and concentrations of the CO gas. The optimized temperatures for pure SnO 2 and doped SnO 2 nanomaterials based gas sensors were 310 °C and 280 °C, respectively. For an even very low concentration of 50 μL/L, Ni and Zn doped SnO 2 gas sensors showed gas responses of 7.28 and 5.90, respectively at 280 °C. Thus, Ni-doped SnO 2 nanoparticles based gas sensor exhibited better gas response than pure SnO 2 and Zn doped SnO 2 based gas sensors though response and recovery times were comparable with those of Zn doped SnO 2 gas sensor.

Online impulse frequency response analysis (IFRA) has proven to be a promising method to detect and diagnose the transformer winding mechanical faults when the transformer is in service. However, the existing fast Fourier transform (FFT) is actually not suitable for processing the transient signals in online IFRA. The field test result also shows that the IFRA signature obtained by FFT is easily distorted by noise. An improved method to obtain the online IFRA signature based on multi-scale complex continuous wavelet transform is proposed. The electrical model simulation and online experiment indicate the superiority of the wavelet transform compared with FFT. This paper provides guidance on the actual application of the online IFRA method.

Recently, spinel cobalt oxide (Co3O4) structure has been widely investigated due to its excellent sensitivity towards various noxious gases and good response/recovery speed at low concentration. In this work, we designed and synthesized two kinds of different Co3O4 micro-structure (cube and octahedron) with a similar size. After fabricating them into gas sensors, we found that the crystal plane structure of Co3O4 has an important effect on its gas sensing performance. Furthermore, the {111} planes of Co3O4may be more sensitive than {100} planes to various testing gases. Co3O4 octahedrons micro-structure exhibits an excellent sensitivity (about 12.6), good response/recovery speed and cycling stability (no decline even after 2 days) under 50 ppm ethanol gases at working temperature of 200 °C. As such, thisCo3O4 octahedrons micro-structure is a promising candidate for a high-performance gas sensing material.

In the present work, nanostructures of metal-oxide semiconductors (TiO2 and Co3O4) were combined to obtain a novel chemoresistive sensor. The experimental results show that there is a linear relationship between gas flow rate and adsorption response. The adsorption response of the composite can reach 15.993, and has good selectivity, linearity(R2 = 0.96077) and repeatability at the same gas flow (300 sccm) and temperature(140 °C). Besides, based on the first principles, the promotion effect of Co3O4 supported TiO2 on CO adsorption was verified from the electronic properties. The experimental and theoretical indicate that the Co3O4@TiO2 nanocomposites is an excellent carbon monoxide gas sensing material and has a wide application prospect.

In this study, we explored the adsorption of three industrial toxic gases (Cl2, NO2, and NH3) on Ag3-WSe2 monolayers using density functional theory calculations. The possible application of Ag3-WSe2 as an industrial toxic gas sensor and adsorbent is discussed through our analyses of the density of states, adsorption energy, band structures, and charge transfer of the three gas molecules absorbed on the Ag3-WSe2 and WSe2 monolayers. The results show that the Ag3-WSe2 monolayer exhibits better adsorption for these gas molecules than the WSe2 monolayer. Thus, Ag3-WSe2 might be a candidate material for the detection of industrial toxic gases. In addition, it provides guidance for the further investigation of transition metal cluster-doped WSe2 monolayers for gas detection.

Frequency response analysis (FRA) has been accepted as a widely used tool for the power industry. The interpretation of FRA can be achieved by the conventional mathematical indicators-based method, which is mostly used in the past. The newly developing artificial intelligence (AI)-based method also provides an alternative interpretation. However, in most reported AI techniques, the features of FRA signatures are directly input into the AI model to obtain the classification results. Few studies have concentrated on the separability of winding deformation faults. In this context, a spectral clustering algorithm is studied to aid in FRA interpretation. The electrical model simulation and experimental tests are performed. The FRA data processing results verify the feasibility, effectiveness and superiority of the proposed method.

Transition metal doping plays a significant role to regulate the electronic properties of the material substrates. In this paper, the effect of Cr dopant on InP3 monolayer and its adsorption performances to H2, C2H2 and CH4 were studied to explore the application potential of Cr-InP3 for three dissolved gases in transformer oil with density functional theory (DFT). The adsorption behavior of intrinsic and Cr-doped InP3 monolayers to three gas molecules were simulated in terms of the charge transfer, density of states and molecular orbital, etc. Results illustrated that the electronic structure of InP3 monolayer could be sharply swayed with Cr dopant, as confirmed by the obviously asymmetry of densities of states around the Fermi level and the generation of magnetic moment of whole system. Besides, the Cr doping with high activation could effectively improve the adsorption properties of InP3 monolayer for three gas molecules. The Cr-InP3 monolayer shows more perfect adsorption performance towards C2H2 molecule in all Cr-InP3 adsorption systems, and might be a promising sensitive material for detection of C2H2 gas. The electrical conductivity and recovery time of Cr-InP3 adsorption systems were discussed to evaluate the applicability of the proposed material in three dissolved gases.

The detection of SF6 decomposed gases is essential link for the fault diagnosis of gas-insulated switchgear (GIS). In this paper, the gas sensing properties of a new planar 2D material indium triphosphide (InP 3) had been proposed and studied towards four types of SF6 decomposed gases (H2S, SO2, SOF2and SO2F2 ) by DFT methods. The structural parameters, density of states and frontier molecular orbitals were calculated and analyzed via building the models of four adsorption systems. The simulation results of MoS 2, graphene and InP3 systems adsorbing four gas molecules were compared, and it was found that the InP3 monolayer is a promising sensing material for detecting SF6 characteristic gases. Desorption time of all InP3 adsorption systems was investigated in various working temperature to evaluate its potential application in four main SF6 decomposed gases detection.

In this paper, a humidity sensor with tricobalt tetroxide (Co3O4)/poly(sodium styrene sulfonate) (PSS) film coated was fabricated using layer-by-layer (LbL) nano self-assembly method. The nanostructural, surface morphology and the specific surface area of the LbL self-assembled Co3O4/PSS nanocomposite were examined by scanning electron microscopy (SEM), X-ray diffraction (XRD), nitrogen sorption analysis and transmission electron microscopy (TEM) measurements. The humidity sensing properties of the sensor were investigated by exposing to a wide relative humidity range of 11–97% at room temperature. The experimental results indicate that the presented sensor not only exhibited favorable response of up to 2479.97, but also possessed fast response and recovery time, acceptable repeatability and stability. The sensing performance was greatly better than that of pure Co3O4. Moreover, the possible humidity sensing mechanism can ascribed to the synergistic effect of Co3O4 and PSS toward water molecules, as well as the layered nanostructure. This work highlights the LbL self-assembled Co3O4/PSS film is a candidate material for constructing humidity sensors with high performance for various applications.

Hollow structured nanofibers have attracted much attention in diverse domains owing to their unique physicochemical properties and characteristics. Gas sensing is one of the most promising applications. In electrical engineering, the detection of SF6 decomposing gas products is significant to monitor the insulation status online of gas insulated switchgear (GIS). This mini-review presents the developments of hollow structured nanofibers in synthesis strategies and gas sensing application, especially in the detection of SF6 decomposing gas products, including hydrogen disulfide (H2S), sulfur dioxide (SO2), thionyl fluoride (SOF2), and sulfuryl fluoride (SO2F2). In addition, the gas sensing mechanism of metal oxide hollow nanofibers based gas sensor are discussed.

In this work, Cr2O3 nanoparticles and SnO2 nanofibers were fabricated by a sol–gel process and an electrospinning method, respectively. Gas sensitive materials with high sensitivity to C2H2 gas were obtained by coating Cr2O3 nanoparticles on SnO2 nanofibers. The prepared Cr2O3 nanoparticle-coated SnO2 nanofibers (Cr2O3 NPs. coated SnO2 NFs.) were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), X-ray energy dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and the gas sensing behaviors to C2H2 were studied. The Cr2O3 NPs. coated SnO2 NFs. exhibited low optimal operating temperature, high sensing response, excellent response-recovery time and long-term stability to C2H2. The optimal operating temperature of the measured material to 20 ppm C2H2 was about 220℃ and the C2H2 concentration had a good linear relationship with the response value when the concentration was less than 60 ppm. In addition, a reasonable gas sensing mechanism was proposed which may enhance the gas sensing performances for the Cr2O3 NPs. coated SnO2 NFs. to C2H2.

The water content in oil is closely related to the deterioration performance of an insulation system, and accurate prediction of water content in oil is important for the stability and security level of power systems. A novel method of measuring water content in transformer oil using multi frequency ultrasonic with a back propagation neural network that was optimized by principal component analysis and genetic algorithm (PCA-GA-BPNN), is reported in this paper. 160 oil samples of different water content were investigated using the multi frequency ultrasonic detection technology. Then the multi frequency ultrasonic data were preprocessed using principal component analysis (PCA), which was implemented to obtain main principal components containing 95% of original information. After that, a genetic algorithm (GA) was incorporated to optimize the parameters for a back propagation neural network (BPNN), including the weight and threshold. Finally, the BPNN model with the optimized parameters was trained with a random 150 sets of pretreatment data, and the generalization ability of the model was tested with the remaining 10 sets. The mean squared error of the test sets was 8.65 &times, 10&minus, 5, with a correlation coefficient of 0.98. Results show that the developed PCA-GA-BPNN model is robust and enables accurate prediction of a water content in transformer oil using multi frequency ultrasonic technology.