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Abstract In order to achieve accurate quantitative evaluation of the ageing state of oil paper insulation, an evaluation method of polymerisation degree (DP) of oil–paper insulation based on Raman spectroscopic plot is presented. Samples covering the full ageing stage of oil–paper insulation were obtained by accelerated thermal ageing experiments. The samples were tested by Raman spectra, and the Raman spectra were converted into Raman spectroscopic plot using Gramian angular field. Feature extraction was performed on the images using local binary pattern (LBP), and 28 feature variables were selected by competitive adaptive reweighted sampling and uninformative variable elimination. Finally, the DP prediction model was constructed by long short‐term memory neural network (LSTM). The training accuracy exceeded 90%, and the mean absolute deviation of DP prediction for 45 test samples was only 40.70. A new visual detection method for transformer ageing diagnosis is presented.

Abstract The feasibility of aqueous zinc‐ion batteries for large‐scale energy storage is hindered by the inherent challenges of Zn anode. Drawing inspiration from cellular mechanisms governing metal ion and nutrient transport, erythritol is introduced, a zincophilic additive, into the ZnSO4 electrolyte. This innovation stabilizes the Zn anode via chelation interactions between polysaccharides and Zn2+. Experimental tests in conjunction with theoretical calculation results verified that the erythritol additive can simultaneously regulate the solvation structure of hydrated Zn2+ and reconstruct the hydrogen bond network within the solution environment. Additionally, erythritol molecules preferentially adsorb onto the Zn anode, forming a dynamic protective layer. These modifications significantly mitigate undesirable side reactions, thus enhancing the Zn2+ transport and deposition behavior. Consequently, there is a notable increase in cumulative capacity, reaching 6000 mA h cm⁻2 at a current density of 5 mA cm−2. Specifically, a high average coulombic efficiency of 99.72% and long cycling stability of >500 cycles are obtained at 2 mA cm−2 and 1 mA h cm−2. Furthermore, full batteries comprised of MnO2 cathode and Zn anode in an erythritol‐containing electrolyte deliver superior capacity retention. This work provides a strategy to promote the performance of Zn anodes toward practical applications.

Abstract Sulphur hexafluoride (SF6) decomposed products analysis is highly critical in the early‐stage fault diagnosis of gas‐insulated switchgear (GIS). Spectrum technology outperforms traditional methods on non‐invasiveness, no sample preparation, and no consumption. Here, the authors present an improved fibre‐enhanced Raman spectroscopy (FERS) as a comprehensive analytical tool to detect a suite of SF6 decomposed products (SO2F2, SOF2, SO2, H2S, CF4, OCS, CO2, and CO). The FERS approach is combined with two iris diaphragms for spatial filtering and a rear‐end reflector for additional Raman signal enhancement. Limits of detection down to 1 × 10−6–8 × 10−6 are achieved for different SF6 decompositions, and quantification of an undefined multigas, sampled from an 800 kV GIS in service, is realised utilising SF6 as the internal standard gas and with a maximum error of 5.5 %. The GIS is diagnosed according to the results and confirmed by an on‐site check. The authors foresee that this technique will provide a route for trace gas analysis in the power industry.

Abstract The intermittent access of renewable energy sources leads to a significant increase in fault short‐circuit currents in power grid. DC superconducting fault current limiters (SFCL) can limit short‐circuit currents in flexible DC transmission systems, which work in combination with direct current circuit breaker (DCCBs) to protect the grid system effectively. However, for the parameter design of DC superconducting fault current limiter (DCSFCL), it is difficult to balance the economics of the device and its adaptability to different fault situations. In this paper, a cross‐mapping design methodology for the DCSFCL is proposed and applied in the Nan'ao flexible DC transmission project, China. A cross‐mapping method is designed combining the critical parameters of DCSFCL and the system parameters. A three‐terminal simulation model of the flexible DC transmission system is constructed through MATLAB/Simulink and the design process of DCSFCL parameters is introduced. A cyclic calculation approach is proposed for the design of DCSFCL parameters through three fault conditions of the power system. Meanwhile, impact current short‐circuits test and artificial ground short‐circuit test were conducted on the prototype. Test results show that the designed DCSFCL meets the requirements of practical applications.

Abstract Arc discharge in the power transformer can bring enormous economic losses and safety risks. The transformer protection system for arc discharge faults is still not ideal. The physical characteristics of the whole process from partial discharge defects transition to arc faults are not fully recognized, mainly because it is difficult to establish and sense the relevant characteristic parameters. This paper built a research platform for studying the transition process from partial discharge to early arc discharge based on fibre‐optic ultrasonic, temperature, pressure sensors with traditional electrical voltage and current sensors. Five discharge models are designed and the changing pattern of multiple parameters in the transition process of different models is studied. The experiment results show that the amplitude and frequency of acoustic waves drop significantly after the arc breakdown. Ultrasonic and high‐frequency current sensing in the early warning of arc fault each has its own limitations. The pressure caused by the arc is not only related to the arc energy but also related to the discharge types. The overall oil temperature change after the early arc discharge is small and hard to detect. This work highlights the promise of using fibre‐optic sensors for physical process research within a harsh electromagnetic environment.

Abstract In order to overcome the shortcomings of traditional methods for corrosive sulfur detection in transformer oil, Raman spectroscopy based detection is proposed in this paper. The widely concerned corrosive sulfur, Dibenzyl Disulfide (DBDS), was chosen as the characteristic molecule to be detected. A series of oil samples with different DBDS concentrations was prepared. And these samples were extracted by 1‐methyl‐2‐pyrrolidinone for DBDS enrichment to improve the sensitivity of detection. Then Raman spectra of samples were obtained, and a linear model was established by analysing the relationship between the characteristic peak and the DBDS concentration. The limit of detection reached 7.98 mg/kg. For determining the DBDS concentrations causing sulfur corrosion, the sulfur weight content on the copper conductor surface was measured by Scanning Electron Microscope‐Energy Dispersive Spectrometer after a corrosion test. The results show that the corrosion limitation highly depends on the type of transformer oil, and the Raman spectroscopy detection can meet the limit of detection requirement in practical condition. Finally, an on‐site oil sample and five Lab‐made samples were detected via our new method and the current Gas Chromatography‐Mass Spectrometry based method. It is found that there is no significant divergence between the measurement results. And good applicability was also demonstrated in on‐site sample test.

Abstract Power equipment detection and localization is the key component of automatic inspection tasks in substations. To solve the challenges such as complex environment and lack of training data, utilization of context information is considered here. For substation scenes, object relation modelling (RM) is proven to be useful, but an end‐to‐end efficient framework which is suitable for non‐Convolutional Neural Networks (CNN) is still missing. Therefore, an extended Transformer network with an elaborately designed RM module is proposed. As the foundation, transformer network is better than CNN in terms of context dependency construction. On top of that, an RM module is plugged to adjust the decoded feature embeddings based on their appearance, position and class information. The module is based on a graph attention neural network which uses similarity as weights of nodes. The experiments show that the proposed method has a 16.2% improvement in accuracy compared to pipeline, and even 6.4% higher than the most recent models, largely promoting the construction of intelligent substations.

While atomically dispersed metals can maximize reaction catalytic sites, it is challenging to achieve high atomic densities without agglomeration. Here, authors prepared Cu single-atoms on black phosphorous using a photochemical strategy and auxiliary H2 as proton reduction electrocatalysts.

Li-ion batteries are expected to become the mainstream devices for green energy storage or power supply in the future due to their advantages of high energy and power density and long cycle life. Monitoring the temperature and strain change characteristics of Li-ion batteries during operation is conducive to judging their safety performance. The hinged differential lever sensitization structure was used for strain sensitization in the design of an FBG sensor, which also allowed the simultaneous measurement of strain and temperature. The temperature and strain variation characteristics on the surface of a Li-ion soft-packed battery were measured using the des.igned sensor. This report found that the charging and discharging processes of Li-ion batteries are both exothermic processes, and exothermic heat release is greater when discharging than when charging. The strain on the surface of Li-ion batteries depends on electrochemical changes and thermal expansion effects during the charge and discharge processes. The charging process showed an increasing strain, and the discharging process showed a decreasing strain. Thermal expansion was found to be the primary cause of strain at high rates.

To achieve omnidirectional sensitive detection of partial discharge (PD) in transformers and to avoid missing PD signals, a fiber optic omnidirectional sensing method for PD in transformers combined with the fiber Bragg grating (FBG) and Fabry-Perot (F-P) cavity is proposed. The fiber optic omnidirectional sensor for PD as a triangular prism was developed. The hollow structure of the probe was used to insert a single-mode fiber to form an F-P cavity. In addition, the three sides of the probe were used to form a diaphragm-type FBG sensing structure. The ultrasound sensitization diaphragm was designed based on the frequency characteristics of PD in the transformer and the vibration model of the diaphragm in the liquid environment. The fiber optic sensing system for PD was built and the performance test was conducted. The results show that the resonant frequency of the FBG acoustic diaphragm is around 20 kHz and that of the F-P cavity acoustic diaphragm is 94 kHz. The sensitivity of the developed fiber optic sensor is higher than that of the piezoelectric transducer (PZT). The lower limit of PD detection is 68.72 pC for the FBG sensing part and 47.97 pC for the F-P cavity sensing part. The directional testing of the sensor and its testing within a transformer simulation model indicate that the proposed sensor achieves higher detection sensitivity of PD in all directions. The omnidirectional partial discharge ultrasound sensing method proposed in this paper is expected to reduce the missed detection rate of PD.

Abstract In order to improve the sensitivity and accuracy of oil‐paper insulation partial discharge detection by the fibre‐optic Fabry–Perot (FP) ultrasonic sensor, this work studied the ultrasonic signal’s frequency characteristics of typical oil‐paper insulation partial discharges and the vibration characteristics of the FP sensor’s diaphragm in the liquid environment. Based on the works above, a multifrequency FP sensor array is proposed, consisting of several FP sensors with different resonant frequencies to detect partial discharges. The experimental results show that the liquid environment has a significant effect on the vibration characteristics of the FP sensor’s diaphragm, and the sensitivity and accuracy of partial discharge detection can be improved based on the multifrequency FP sensing array, which can also be applied in the pattern recognition and localisation of partial discharges.

In this article, with an anti-resonant hollow core fiber (ARHCF), fiber-enhanced Raman spectroscopy (FERS) for trace-gas sensing in a high-concentration gas background is demonstrated for the first time. The performance of the apparatus is verified by detecting trace-gas in the high concentration SF6 and gaseous impurities in the high concentration C2H6. With a 1.5 W laser source and 60 s exposure time, the limit of detection (LOD) of gases at tens of ppm levels is achieved, including carbonyl sulfide (COS), carbon tetrafluoride (CF4), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), acetylene (C2H2), ethylene (C2H4), propyne (C3H4), propylene (C3H6), and propane (C3H8). Quantification of multi-gas with great accuracy exceeding 94% is also realized. It shows that the FERS can demonstrate the ability of multi-gas sensing with high selectivity, sensitivity, and accuracy.

This paper presents the development of a highly sensitive gas detection system based on a resonant photoacoustic cell for detecting dissolved gases in transformer oil. A simulation model of the resonant photoacoustic cell was studied and optimized the buffer chamber volume while ensuring signal enhancement. The volume of the photoacoustic cell was reduced by about 80% compared to the classical model. A resonant photoacoustic cell was then fabricated based on the optimized simulation optimization. The dual-resonance photoacoustic system was constructed by combining the resonant PA cell with a handmade cantilever fiber acoustic sensor. The system’s sensitivity was further improved by using an erbium-doped fiber amplifier, wavelength modulation, and harmonic detection technology. The experimental results showed that the system achieved a detection limit of 6 ppb and an excellent linear range under 1000 ppm for C2H2 gas. The developed gas detection system has potential applications for monitoring the condition of power transformers in power grids.

To expand the range of application of quantum dots in the field of temperature sensing, this paper proposed three kinds of temperature sensors based on CsPbX3 (X = Br, Br/I, and I) quantum dots filling into hollow-core fibers. Based on the photoluminescence properties of quantum dots, quantum dots were prepared by the hydrothermal method and later filled in hollow-core optical fibers to fabricate three kinds of temperature sensors. In this paper, the temperature dependence of photoluminescence of quantum dot sensors has been studied. In detail, temperature variation characteristics of integrated photoluminescence, full width at half maximum, and central wavelength of the photoluminescence spectrum have been investigated. It is found that the integrated photoluminescence, full width at half maximum, and central wavelength all fluctuate regularly with temperature. In this paper, the support vector regression method is employed to determine the mathematical relation between integrated photoluminescence, full width at half maximum, central wavelength, and temperature. Later, the three parameters are used to achieve fast and accurate temperature measurement. The experimental results show that in the range of 30–100⁡°C, the precision of the optical fiber temperature sensors based on quantum dots is below 2⁡°C.

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

In this paper, we experimentally demonstrate that molecules with maximum absorption wavelength corresponding to the first excitation energy of base liquid molecules are able to significantly improve the electrical performances of natural ester oils. The positive lightning impulse breakdown and acceleration characteristics, ac breakdown characteristics, and streamer structure of the natural ester oils, as well as the electronic properties of the molecules, are presented. The molecules with different absorptions in ultraviolet lights were doped into the natural ester oil. The positive lightning impulse breakdown characteristics of natural ester oil with different doped molecules indicated that the doped molecule with great absorption in the medium wavelength ultraviolet irradiation was able to significantly enhance the positive lightning impulse breakdown and acceleration voltages of natural ester oil by 15% and 60%, respectively. In addition, it also enhanced the ac breakdown voltage by 17%. The base liquid molecules exhibited first excitation energy of 4 eV, which corresponds to the maximum absorption wavelength of such molecules, indicating that the improved electric breakdown strength was a result of the reduced the number of excited molecules and weakened ionization of molecules during the streamer propagation.

This study explores the method of applying surface enhanced Raman scattering (SERS) in diagnosing the aging stage of oil-paper insulation equipment in the power system, which provides access to the on-line mornitoring of electrical equipment. Oil-paper samples in different aging stages were obtained by accelerated aging experiments. Effective Raman signals of insulating oil were collected by using the self-made hexagonal sliver nano-plates SERS substrates and the self-assembled Raman detection platform. For the SERS data of oil-paper insulation samples, kernel entropy component analysis (KECA) was applied to extract the Raman spectral features and support vector machine (SVM) was used to recognize the the aging stage of oil-paper insulation. The results demonstrate that the KECA-SVM method exhibits a good diagnostic capacity and the recognition accuracy of the propsed method reached 81.43 % (57/70). In summary, a new spectral method is proposed to diagnose the aging stage of oil-paper insulation, which lays a foundation for the actual diagnosis of the aging condition in running oil-paper insulation equipment by Raman spectroscopy.

Detecting the decomposition components of SF6 insulating gas is recognized as an effective means to monitor the operating status of the SF6 insulating switch. In this paper, the adsorption characteristics of a new two-dimensional material GeSe for five SF6 decomposition gases (SO2, SOF2, SO2F2, H2S and HF) are reported by first-principles simulation. Through the analysis of the change of energy band structure, density of states distribution, and gas desorption time, it is found that GeSe has the potential as a gas-sensitive material for the selective detection of SO2F2, and the computational work in this paper provides theoretical guidance for the development of new gas-sensitive sensors applied in monitoring SF6 insulated switches.

Single-walled carbon nanotubes (SWCNTs) have been widely discussed and applied as novel gas sensing nanomaterials. Hydrogen is one of the remarkable fault characteristic gases in high-voltage oil-paper insulated transformers. In this paper, 3.07 wt% Pd nanoparticles (NPs) were used to decorate SWCNTs. The unloaded, the carboxylated, and the Pd-doped SWCNTs were fabricated into three planar gas sensors, and their gas sensing properties to hydrogen were studied. Gas sensing mechanism was analyzed. Results show that the optimal operating temperature of a Pd-doped SWCNTs-based gas sensor is 125°C lower than that of the unloaded SWCNTs-based gas sensor, and it shows the highest gas sensing response value. This is attributed to the decreasing work function of Pd, which reduces the hole carries in the nanotubes.

The vibration spectroscopy (Raman and infrared) of widely concerned molecules in sulfur corrosion phenomenon (Dibenzyl Disulfide, Dibenzyl Sulphide, and Bibenzyl) is detailedly analyzed based on density functional theory and experimental measurement. The dominant conformations of these molecules are determined according to Boltzmann distribution in relative Gibbs free energy. Additionally, noncovalent interaction analysis is conducted to indicate intramolecular interaction. Vibration normal mode is assigned based on potential energy distribution, which comprehensively reveals the molecular vibrational behaviors. Conformations weighted spectra are obtained and compared with experimentally measured spectra. We found that experimental spectra are in good agreement with the theoretical spectra in B3LYP-D3(BJ)/6-311G** level with a frequency correction factor. Furthermore, the divergence among these molecules is discussed. The vibrational behavior of the methylene group in the molecule shows a trend with the presence of the sulfur atom.

Optical fibre sensing technology is a powerful method for long-term reliable sensing in harsh environments, which means it is particularly suitable for the detection of electrical equipment characteristic state parameters, including characteristic gases, abnormal vibration, ultrasonic produced by partial discharge, and abnormal temperature rise. This paper reviews several individual optical fibre sensors for different state parameters detection, discusses the advantages, limitations and possible improvement methods, and finally presents the most promising optical fibre sensor for each state parameter.

This article presents an extrinsic fiber-optic acoustic sensor applied for partial discharge (PD) detection in air. A Fabry–Perot (F-P) cavity consisting of a single-mode fiber (SMF) and a graphene oxide (GO) film, whose thickness and effective vibration diameter are approximately 500 nm and 4.377 mm, respectively, is used as this sensing core, and the manufacturing process of GO diaphragms and this sensing probe is illustrated to be simple and controllable. Performance tests indicate that this proposed sensor maintains a linear acoustic-pressure response and a flat frequency response in the range of 200 Hz to 20 kHz, while being an omnidirectional sensor and having high working stability during a ten-day test period. Additionally, PD detection results show that the minimum PD size detected by this proposed sensor in air was approximately 100 pC, which demonstrates that this proposed sensor can achieve high-sensitivity PD detection in air.

The long-term stability and the extension of the use time of gas sensors are one of the current concerns. Lowering the working temperature is one of the most effective methods to delay aging. In this paper, pure MoS2 and ZnO-MoS2 nanocomposites were successfully prepared by the hydrothermal method, and the morphological characteristics were featured by scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). Pure MoS2 and ZnO-MoS2 nanocomposites, as a comparison, were used to study the aging characteristic. The sensing properties of the fabricated gas sensors with an optimal molar ratio ZnO-MoS2 (Zn:Mo = 1:2) were recorded, and the results exhibit a high gas-sensing response and good repeatability to the acetylene detection. The working temperature was significantly lower than for pure MoS2. After aging for 40 days, all the gas-sensing response was relatively attenuated, and pure MoS2 exhibits a faster decay rate and lower gas-sensing response than nanocomposites. The better gas-sensing characteristic of nanocomposites after aging was possibly attributed to the active interaction between ZnO and MoS2.

Graphene and its hybrid materials, due to their unique structures and properties, have attracted enormous attention for both fundamental and applied research in the gas sensing field. This review highlights the recent advances in the application of graphene-based gas sensors in fault characteristic gas detection of oil-immersed equipment, which can effectively achieve condition monitoring of the oil-immersed power equipment. In this review, the synthetic methods of graphene hybrid materials with noble metals, metal oxides and their combination are presented. Then, the basic sensing mechanisms of graphene hybrid materials and gas sensing properties of graphene hybrid materials sensors to hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), acetylene (C2H2), ethylene (C2H4), and ethane (C2H6), which are the fault characteristic gas in oil-immersed power equipment, are summarized. Finally, the future challenges and prospects of graphene hybrid materials gas sensors in this field are discussed.

Detecting the dissolving furfural in mineral oil is an essential technical method to evaluate the ageing condition of oil-paper insulation and the degradation of mechanical properties. Compared with the traditional detection method, Raman spectroscopy is obviously convenient and timesaving in operation. This study explored the method of applying surface enhanced Raman scattering (SERS) on quantitative analysis of the furfural dissolved in oil. Oil solution with different concentration of furfural were prepared and calibrated by high-performance liquid chromatography. Confocal laser Raman spectroscopy (CLRS) and SERS technology were employed to acquire Raman spectral data. Monte Carlo cross validation (MCCV) was used to eliminate the outliers in sample set, then competitive adaptive reweighted sampling (CARS) was developed to select an optimal combination of informative variables that most reflect the chemical properties of concern. Based on selected Raman spectral features, support vector machine (SVM) combined with particle swarm algorithm (PSO) was used to set up a furfural quantitative analysis model. Finally, the generalization ability and prediction precision of the established method were verified by the samples made in lab. In summary, a new spectral method is proposed to quickly detect furfural in oil, which lays a foundation for evaluating the ageing of oil-paper insulation in oil immersed electrical equipment.

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 × 10−5, 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.

Currently, in the modern power industry, it is still a great challenge to achieve high sensitivity and uninterrupted-online measurement of large current on the high voltage gridlines. At present, the fiber grating current sensors based on giant magnetostrictive material used in the modern power industry to achieve uninterrupted-online measurement of large currents on high voltage grid lines is a better method, but the sensitivity of this current sensor is relatively low, therefore, it is key to improve the sensitivity of this current sensor. Here we show a sensitivity-enhanced fiber grating current sensor based on giant magnetostrictive material (in the following, simply referred to as the sensitivity-enhanced fiber grating current sensor) that is able to achieve high sensitivity and uninterrupted-online measurement of large currents by means of pressurizing the giant magnetostrictive material. Sampling the power frequency sinusoidal alternating current signals with the amplitudes of 107, 157 and 262 A respectively, based on realistic factors, for the sensitivity-enhanced current sensor, the sensitivities, compared with that of the traditional fiber grating current sensor based on giant magnetostrictive material (in the following, simply referred to as the traditional fiber grating current sensor), were respectively enhanced by 268.96%, 135.72% and 71.57%. Thus the sensitivity-enhanced fiber grating current sensor allows us to solve the issue of high sensitivity and uninterrupted-online measurement of large currents that have been plaguing the power industry in a very simple and low-cost way.

Accurate detection of dissolved aging features in transformer oil is the key to judging the aging degree of oil-paper insulation. In this work, in order to realize in situ detection of furfural dissolved in transformer oil, silver nanoparticles were self-assembled on the surface of gold film with P-aminophenylthiophenol (PATP) as a coupling agent. Rhodamine-6G (R6G) was used as the probe molecule to test the enhancement effect. By optimizing the molecular concentration, molecular deposition time, and silver sol deposition time of PATP, the nanoparticles were made more uniform and compact, and an enhanced substrate with rich hot spots was obtained. The optimum substrate was developed, and surface-enhanced Raman spectroscopy (SERS) detection of trace furfural dissolved in transformer oil was realized. The results showed that the substrate prepared under the conditions of 0.1 mol/L PATP, 5 hours deposition in PATP and 12 hours immersion in silver sol, had the best reinforcement effect (that is, uniform and compact particle arrangement and no particle clusters). By use of this substrate, the minimum detectable concentration of furfural in transformer oil was about 1.06 mg/L, which provides a new method for fast and nondestructive detection of transformer aging diagnosis.

Hierarchical flower-like ZnO nanorods, net-like ZnO nanofibers and ZnO nanobulks have been successfully synthesized via a surfactant assisted hydrothemal method. The synthesized products were characterized by X-ray powder diffraction and field emission scanning electron microscopy, respectively. A possible growth mechanism of the various hierarchical ZnO nanostructures is discussed in detail. Gas sensors based on the as-prepared ZnO nanostructures were fabricated by screen-printing on a flat ceramic substrate. Furthermore, their gas sensing characteristics towards methane were systematically investigated. Methane is an important characteristic hydrocarbon contaminant found dissolved in power transformer oil as a result of faults. We find that the hierarchical flower-like ZnO nanorods and net-like ZnO nanofibers samples show higher gas response and lower operating temperature with rapid response-recovery time compared to those of sensors based on ZnO nanobulks. These results present a feasible way of exploring high performance sensing materials for on-site detection of characteristic fault gases dissolved in transformer oil.

Sulfur dioxide, SO2, is one of the most important decomposition byproducts of sulfur hexafluoride, SF6, under partial discharge in GIS apparatus. The sensing performances of semiconductor gas sensors can be improved by morphology tailoring. This paper reported the synthesis method, structural characterization, and SO2 responsive characteristics of hierarchical flower-shaped ZnO nanostructures. Hierarchical ZnO nanoflowers were successfully prepared via a facile and simple hydrothermal method and characterized by X-ray powder diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy, respectively. Planar chemical gas sensor was fabricated and its responsive characteristics towards SO2 were systematically performed. The optimum operating temperature of the fabricated sensor was measured to be about 260°C, and the corresponding maximum responses were 16.72 and 26.14 to 30 and 60 ppm of SO2. Its saturated gas concentration was 2000 ppm with a response value of 67.41. Moreover, a quick response and recovery feature (7 s and 8 s versus 80 ppm of SO2) and good stability were also observed. All results indicate that the proposed sensor is a promising candidate for detecting SF6 decomposition byproduct SO2.

In this paper, an artificial pollution test is carried out to study the leakage current of porcelain insulators. Fractal theory is adopted to extract the characteristics hidden in leakage current waveforms. Fractal dimensions of the leakage current for the security, forecast and danger zones are analyzed under four types of degrees of contamination. The mean value and the standard deviation of the fractal dimension in the forecast zone are calculated to characterize the differences. The analysis reveals large differences in the fractal dimension of leakage current under different contamination discharge stages and degrees. The experimental and calculation results suggest that the fractal dimension of a leakage current waveform can be used as a new indicator of the discharge process and contamination degree of insulators. The results provide new methods and valid indicators for forecasting contamination flashovers.

Dissolved gas analysis (DGA) has been widely applied to diagnose internal faults in transformer insulation systems. However, the accuracy of DGA technique is limited because of the lack of positive correlation of the fault-identifying gases with faults found in power transformers. This paper presents a laboratory study on the correlation between oil dissolved gas formation and partial discharge (PD) statistical parameters. Canonical correlation analysis (CCA) is employed to explore the underlying correlation and to extract principal feature parameters and gases in the development of different PD defects. This study is aimed to provide more information in assisting the separation, classification and identification of PD defects, which might improve the existing transformer dissolved gas analysis (DGA) schemes. An application of a novel ratio method for discharge diagnosis is proposed. The evaluation of DGA data both in laboratory and actual transformers proves the effectiveness of the method and the correlation investigation.

The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method. The generalization ability of the E-Bagging is enhanced significantly by the comprehensive information entropy. A total of sets of 1200 oil-dissolved gas data of transformers are used as examples of fault prediction. The comparisons between the E-Bagging and the traditional Bagging and individual prediction approaches are presented. The results show that the E-Bagging possesses higher accuracy and greater stability of prediction than the traditional Bagging and individual prediction approaches.

The development of functionalized metal oxide/reduced graphene oxide (rGO) hybrid nanocomposites concerning power equipment failure diagnosis is one of the most recent topics. In this work, WO3 nanolamellae/reduced graphene oxide (rGO) nanocomposites with different contents of GO (0.5 wt %, 1 wt %, 2 wt %, 4 wt %) were synthesized via controlled hydrothermal method. X-ray diffraction (XRD), transmission electron microscopy (TEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), thermogravimetric analyses-derivative thermogravimetric analysis-differential scanning calorimetry (TG-DTG-DSC), BET, and photoluminescence (PL) spectroscopy were utilized to investigate morphological characterizations of prepared gas sensing materials and indicated that high quality WO3 nanolamellae were widely distributed among graphene sheets. Experimental ceramic planar gas sensors composing of interdigitated alumina substrates, Au electrodes, and RuO2 heating layer were coated with WO3 nanolamellae/reduced graphene oxide (rGO) films by spin-coating technique and then tested for gas sensing towards multi-concentrations of acetylene (C2H2) gases in a carrier gas with operating temperature ranging from 50 °C to 400 °C. Among four contents of prepared samples, sensing materials with 1 wt % GO nanocomposite exhibited the best C2H2 sensing performance with lower optimal working temperature (150 °C), higher sensor response (15.0 toward 50 ppm), faster response-recovery time (52 s and 27 s), lower detection limitation (1.3 ppm), long-term stability, and excellent repeatability. The gas sensing mechanism for enhanced sensing performance of nanocomposite is possibly attributed to the formation of p-n heterojunction and the active interaction between WO3 nanolamellae and rGO sheets. Besides, the introduction of rGO nanosheets leads to the impurity of synthesized materials, which creates more defects and promotes larger specific area for gas adsorption, outstanding conductivity, and faster carrier transport. The superior gas sensing properties of WO3/rGO based gas sensor may contribute to the development of a high-performance ppm-level gas sensor for the online monitoring of dissolved C2H2 gas in large-scale transformer oil.

In this paper, pure and Ag-doped SnO2 nanospheres were synthesized by hydrothermal method and characterized via X-ray powder diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), and X-ray photoelectron spectra (XPS), respectively. The gas sensing performance of the pure, 1 at.%, 3 at.%, and 5 at.% Ag-doped SnO2 sensing devices toward hydrogen (H2) were systematically evaluated. The results indicated that compared with pure SnO2 nanospheres, Ag-doped SnO2 nanospheres could not only decrease the optimum working temperature but also significantly improve H2 sensing such as higher gas response and faster response-recovery. Among all the samples, the 3 at.% Ag-doped SnO2 showed the highest response 39 to 100 μL/L H2 at 300 °C. Moreover, its gas sensing mechanism was discussed, and the results will provide reference and theoretical guidance for the development of high-performance SnO2-based H2 sensing devices.

Carbon monoxide (CO) is one of the most important fault characteristic gases dissolved in power transformer oil. With the advantages of high sensitivity and accuracy, long-term stability, and short detection time, photoacoustic spectroscopy (PAS) has been proven to be one promising sensing technology for trace gas recognition. In this investigation, a tunable PAS experimental system based on a distributed-feedback (DFB) diode laser was proposed for recognizing dissolved CO in transformer oil. The molecular spectral line of CO gas detection was selected at 1.567 μm in the whole experiment. Relationships between the photoacoustic (PA) signal and gas pressure, temperature, laser power, and CO gas concentration were measured and discussed in detail, respectively. Finally, based on the least square regression theory, a novel quantitative identification method for CO gas detection with the PAS experimental system was proposed. And a comparative research about the gas detection performances performed by the PAS system and gas chromatography (GC) measurement was presented. All results lay a solid foundation for exploring a portable and tunable CO gas PAS detection device for practical application in future.

The detection of furfural in transformer oil through surface enhanced Raman spectroscopy (SERS) is one of the most promising online monitoring techniques in the process of transformer aging. In this work, the Raman of individual furfural molecules and SERS of furfural-Mx (M = Ag, Au, Cu) complexes are investigated through density functional theory (DFT). In the Raman spectrum of individual furfural molecules, the vibration mode of each Raman peak is figured out, and the deviation from experimental data is analyzed by surface charge distribution. In the SERS of furfural-Mx complexes, the influence of atom number and species on SERS chemical enhancement factors (EFs) are studied, and are further analyzed by charge transfer effect. Our studies strengthen the understanding of charge transfer effect in the SERS of furfural molecules, which is important in the online monitoring of the transformer aging process through SERS.

Analyzing the concentration of low molecular acids dissolved in oil is vital in the oil-paper insulation aging diagnostic procedure of power transformers. The existing methods cannot distinguish between different acid types and their strengths. In this study, an improved solution Raman detection platform is fabricated. The direct measurement of dissolved acetic acid, a kind of low molecular acids, is observed in transformer oil without extraction. The Raman shift line of oil-dissolved acetic acid at 891 cm−1 corresponding to H–C–H symmetrical swing and O–H swing modes is taken as its characteristic value. Taking Raman shift line of pure oil at 932 cm−1 as an internal standard, a linear regression curve for quantitative analysis is obtained with a slope of 0.19. The best platform parameter of accumulation number is 300, which is determined by Allan deviation analysis. The current concentration detection limit and accuracy for oil-dissolved acetic acid are obtained at about 0.68 mg/mL and 91.66%, separately. The results show that Raman spectroscopy could be a useful alternative method for evaluation insulation aging state of an operating power transformer in the future.

The detection of partial discharge and analysis of the composition and content of sulfur hexafluoride SF6 gas components are important to evaluate the operating state and insulation level of gas-insulated switchgear (GIS) equipment. This paper reported a novel sensing material made of pure ZnO and NiO-decorated ZnO nanoflowers which were synthesized by a facile and environment friendly hydrothermal process for the detection of SF6 decomposition byproducts. X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), high resolution transmission electron microscopy (HRTEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS) were used to characterize the structural and morphological properties of the prepared gas-sensitive materials. Planar-type chemical gas sensors were fabricated and their gas sensing performances toward the SF6 decomposition byproducts SO2, SO2F2, and SOF2 were systemically investigated. Interestingly, the sensing behaviors of the fabricated ZnO nanoflowers-based sensor to SO2, SO2F2, and SOF2 gases can be obviously enhanced in terms of lower optimal operating temperature, higher gas response and shorter response-recovery time by introducing NiO. Finally, a possible gas sensing mechanism for the formation of the p–n junctions between NiO and ZnO is proposed to explain the enhanced gas response. All results demonstrate a promising approach to fabricate high-performance gas sensors to detect SF6 decomposition byproducts.

Various morphologies of low dimensional ZnO nanostructures, including spheres, rods, sheets, and wires, were successfully synthesized using a simple and facile hydrothermal method assisted with different surfactants. Zinc acetate dihydrate was chosen as the precursors of ZnO nanostructures. We found that polyethylene glycol (PEG), polyvinylpyrrolidone (PVP), glycine, and ethylene glycol (EG) play critical roles in the morphologies and microstructures of the synthesized nanostructures, and a series of possible growth processes were discussed in detail. Gas sensors were fabricated using screen-printing technology, and their sensing properties towards acetylene gas (C2H2), one of the most important arc discharge characteristic gases dissolved in oil-filled power equipments, were systematically measured. The ZnO nanowires based sensor exhibits excellent C2H2 sensing behaviors than those of ZnO nanosheets, nanorods, and nanospheres, indicating a feasible way to develop high-performance C2H2 gas sensor for practical application.

Acetylene (C2H2) gas sensors were developed by synthesizing a reduced graphene oxide (rGO)-loaded SnO2 hybrid nanocomposite via a facile two-step hydrothermal method. Morphological characterizations showed the formation of well-dispersed SnO2 nanoparticles loaded on the rGO sheets with excellent transparency and obvious fold boundary. Structural analysis revealed good agreement with the standard crystalline phases of SnO2 and rGO. Gas sensing characteristics of the synthesized materials were carried out in a temperature range of 100–300 °C with various concentrations of C2H2 gas. At 180 °C, the SnO2–rGO hybrid showed preferable detection of C2H2 with high sensor response (12.4 toward 50 ppm), fast response-recovery time (54 s and 23 s), limit of detection (LOD) of 1.3 ppm and good linearity, with good selectivity and long-term stability. Furthermore, the possible gas sensing mechanism of the SnO2–rGO nanocomposites for C2H2 gas were summarized and discussed in detail. Our work indicates that the addition of rGO would be effective in enhancing the sensing properties of metal oxide-based gas sensors for C2H2 and may make a contribution to the development of an excellent ppm-level gas sensor for on-line monitoring of dissolved C2H2 gas in transformer oil.

Pure and M-doped (M = Pt, Fe, and Zn) SnO2 nanospheres were successfully synthesized via a simple and facile hydrothermal method and characterized by X-ray powder diffraction, field-emission scanning electron microscopy, and energy dispersive spectroscopy. Chemical gas sensors were fabricated based on the as-synthesized nanostructures, and carbon monoxide sensing properties were systematically measured. Compared to pure, Fe-, and Zn-doped SnO2 nanospheres, the Pt-doped SnO2 nanospheres sensor exhibits higher sensitivity, lower operating temperature, more rapid response and recovery, better stability, and excellent selectivity. In addition, a theoretical study based on the first principles calculation was conducted. All results demonstrate the potential of Pt dopant for improving the gas sensing properties of SnO2-based sensors to carbon monoxide.

The aging of oil-paper insulation in power transformers may cause serious power failures. Thus, effective monitoring of the condition of the transformer insulation is the key to prevent major accidents. The purpose of this study was to explore the feasibility of confocal laser Raman spectroscopy (CLRS) for assessing the aging condition of oil-paper insulation. Oil-paper insulation samples were subjected to thermal accelerated ageing at 120 °C for up to 160 days according to the procedure described in the IEEE Guide. Meanwhile, the dimension of the Raman spectrum of the insulation oil was reduced by principal component analysis (PCA). The 160 oil-paper insulation samples were divided into five aging stages as training samples by clustering analysis and with the use of the degree of polymerization of the insulating papers. In addition, the features of the Raman spectrum were used as the inputs of a multi-classification support vector machine. Finally, 105 oil-paper insulation testing samples aged at a temperature of 130 °C were used to further test the diagnostic capability and universality of the established algorithm. Results demonstrated that CLRS in conjunction with the PCA-SVM technique provides a new way for aging stage assessment of oil-paper insulation equipment in the field.