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Frequency response analysis (FRA) is a popular tool to detect transformer winding deformation faults. It has been recently used to diagnose the winding inter-turn short circuit fault of the electric machine. The application of FRA is based on the fact that the winding can be equivalent to an electrical circuit consisting of resistance, inductance, and capacitance. Therefore, to obtain an accurate broadband equivalent circuit model of synchronous machine and perform the fault analysis and diagnosis, this paper introduces the gray box model technique based on the state space and genetic algorithm. The electrical circuit of a form-wound coil of a 4 kW machine with known electrical parameters is used to verify the proposed method primarily. Besides, the equivalent circuit modeling is carried out and verified on an actual 5 kW synchronous machine. Finally, the fault simulation analyses of winding inter-turn short circuits are performed based on the proposed equivalent electrical circuit. The verification result shows that the circuit parameters identified by the proposed method are accurate. Moreover, the fault simulation results indicate that the inter-turn short circuit faults can cause apparent variations in the frequency response curves. It is feasible to detect the winding fault by the FRA method.

The accurate and fast diagnosis of transformer winding deformation faults is of significance to power suppliers and utilities. An improved winding mechanical deformation fault classification method is proposed. In this study, the transformer frequency response data is used to draw polar plots, and then its texture features are extracted for fault classification. The classification model constructed by multiple support vector machines is successfully obtained and shows good classification effect. Besides, this article uses an improved genetic algorithm based on the Emperor-Selective mating scheme and catastrophic operation, to optimize the parameters of support vector machine. The feasibility and accuracy of the proposed method are verified with experimental data obtained from a model transformer, and the proposed method is demonstrated to exhibit better performance compared with the traditional method.

Impulse Frequency Response Analysis (IFRA) provides an alternative technique for detection of mechanical type faults in power transformers among researchers. However, the conventional frequency response curve obtained from the time domain transient response only contains the frequency domain information of signal, and the time domain information is neglected. Furthermore, the traditional approach of using the fast Fourier transform (FFT) in processing the non-stationary signal could result in lack of useful information. This paper proposes a novel winding deformation fault diagnostic technique which is modified from IFRA, the detected transient signals of pulse response are processed based on the continuous wavelet transform (CWT) algorithm, and the wavelet time-frequency diagram are plotted and analyzed. The simulation of a simple transformer high frequency model and experiment analysis show that the various types of winding deformation can be distinguished by the proposed technique. The data processing result by the wavelet transform conforms with the variation pattern of frequency response to different winding fault types. Besides, the verification result of experiment indicates that the wavelet time-frequency diagram obtained by the proposed technique shows better performance than the frequency response curves obtained by the conventional FFT on processing the transient signals.

Modular multilevel converter (MMC)-based high-voltage direct current (HVDC) grid is a promising technology for multiple offshore wind farms (OWFs) to form interconnected offshore grids. However, in the MMC-HVdc grid, the converter terminals connecting the OWFs could only be passively pre-charged by the ones connecting active ac networks via the dc lines during the start-up process. Under this condition, the start-up process of HVdc grid of the passive-side converters becomes significantly complicated. This paper, considering an HVdc grid integrating multiple passive-side converters, investigates its start-up process with three main contributions: 1) a modified nearest level modulation (NLM) method is proposed for the passive-side converters to mitigate the inrush currents after they are deblocked; 2) based on the dc-side equivalent models, the inter-converter resonances and oscillations between the passive-side converters are analyzed after they are deblocked, and; 3) an oscillation damping controller is proposed to provide the electrical damping of inter-converter oscillations between the passive-side converters. The simulation results in PSCAD/EMTDC from a four-terminal MMC-HVDC grid with two terminals connecting the OWFs verify the effectiveness of the proposed start-up control schemes.

Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.

As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.

The existence of moisture has a greatly negative influence on the insulation properties of insulation oil. In this paper, the molecular dynamics simulation method was used to study the diffusion behavior of moisture in vegetable oil. The oil model with water-containing was constructed in the paper, and molecular dynamics simulations were performed at a temperature range of 343K to 423K. The simulation results show that when the temperature increases from 343K to 423K, the number of hydrogen bonds decreases by 44.23%, and the interaction energy increases from -82.826kJ/mol to -42.196kJ/mol. And the simulation results of moisture motion trajectories show that the increased temperature causes intensified moisture movements. This paper explains that vegetable oils have a stronger water soluble from a micro perspective.

This paper presents a low-cost, highly flexible hexagonal ROV that meets the requirement of oceanic exploration, discovery and particularly attracting for the marine science education. “ROVer” is an open-frame remote-controlled underwater vehicle with redundant safe design that has a protective design in terms of power drive and is suitable for students to construct ROVs in schools or off-campus environments and for underwater detection. The common low-cost raw PVC (Polyvinyl chloride) material is used as the frame support to reduce the weight and cost. The main structure with the hexahedron significantly improves the space utilization rate of the ROV’s inner cabin and payload capability. The well designed control and communication system enables the real time observation and control functionality for users. This ROV is equipped with an image processing module to the software design for the shape recognition function of the vehicle. Several water pool tests have successfully proved ROVer’s remote operation, image detection and video recording capabilities.

TiO 2 based gas-sensing materials show outstanding sensitivity to SF 6 decomposition products, which can be used to preparing on-line monitoring gas sensor. This study reviewed the research progress of TiO 2 based gas-sensing materials in SF 6 decomposition products detection by DFT calculation and experimental measurement. The gas-sensing properties of intrinsic, oxygen defective, Pt, Au, and N −doped anatase TiO 2 (101) and anatase TiO 2 (001) gas sensor were carefully reviewed. By comparison, it is found that surface modification can be an effective way to enhance the gas sensitivity of TiO 2 based gas-sensing materials.

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.

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.

Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types.

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.

Winding deformation is a critical power transformer issue that needs to be detected as soon as it emerges due to its progressive nature and severe consequences it may lead to. Frequency response analysis (FRA) is the only reliable technique currently used to detect such deformation. However, the technique is conducted offline that may cause interruption to the electricity grid when taking an operating transformer out of service for testing. Moreover, as the current FRA technique is only conducted on suspected units, a mechanical fault may emerge and progress before the utility becomes aware of. To overcome these limitations, this paper presents a practical feasibility study to detect transformer winding deformations online. The technique relies on utilizing transient overvoltage signals that a transformer is subjected to during its normal operation as a natural variable frequency excitation source. The feasibility of utilizing various transient overvoltage signals as excitation source for transformer FRA signature measurement is firstly investigated and optimum signal parameters are recommended based on signal voltage and energy spectrum analyses. Secondly, the proposed technique is verified through simulation analysis by comparing the transformer frequency response signatures when transient overvoltage and conventional sweep frequency voltage are used as excitation sources. Also, experimental testing is conducted to assess the feasibility of the proposed technique to identify various winding deformations such as radial buckling, telescoping and short circuit turns. Custom-made capacitive voltage divider is developed to accurately detect the response of transient overvoltage signals generated due to environmental conditions or power system switching operations. The proposed technique along with the developed capacitive voltage divider is implemented on a 3-phase Wye-Delta, 50 Hz, 31.5 MVA, 110/10.5 kV power transformer to measure the frequency response signatures of the high voltage winding.

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.

Transfer function method is now a widely acceptable tool to diagnose transformer winding deformations. A sweep frequency sine wave generator is often used to excite the different modes of resonance and anti-resonances. However, it is time consuming. Nanosecond square wave pulse signal offers an alternative that can serve the same objective. However, as so far, there is no certain criterion for selecting pulse parameters. This paper provides a comprehensive method for the determination of nanosecond square wave pulse parameters for transfer function evaluation of power transformer for winding deformation studies.

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.

The accurate establishment of the equivalent circuit model of the synchronous machine windings’ broadband characteristics is the basis for the study of high-frequency machine problems, such as winding fault diagnosis and electromagnetic interference prediction. Therefore, this paper proposes a modeling method for synchronous machine winding based on broadband characteristics. Firstly, the single-phase high-frequency lumped parameter circuit model of synchronous machine winding is introduced, then the broadband characteristics of the port are analyzed by using the state space model, and then the equivalent circuit parameters are identified by using an optimization algorithm combined with the measured broadband impedance characteristics of port. Finally, experimental verification and comparison experiments are carried out on a 5-kW synchronous machine. The experimental results show that the proposed modeling method identifies the impedance curve of the circuit parameters with a high degree of agreement with the measured impedance curve, which indicates that the modeling method is feasible. In addition, the comparative experimental results show that, compared with the engineering exploratory calculation method, the proposed parameter identification method has stronger adaptability to the measured data and a certain robustness. Compared with the black box model, the parameters of the proposed model have a certain physical meaning, and the agreement with the actual impedance characteristic curve is higher than that of the black box model.

Detecting the early signs of mechanical failures of power transformer winding is necessary and is possible with online monitoring techniques. Online impulse frequency response analysis (IFRA) is a promising diagnostic method when a transformer is in service. This paper examines the unrevealed problem existing in the method, namely, the impact of bushing capacitive coupling circuit on online impulse frequency response. An equivalent electrical model of capacitive coupling circuit and transformer winding is established. The frequency response of the capacitive coupling circuit is obtained to study its influence on online impulse frequency response. The parameter variations of capacitive coupling circuit caused by coupling capacitance variation and bushing dielectric breakdown are simulated to investigate their influence on online impulse frequency response signatures. A few experiments are eventually performed to verify the theoretical analysis and simulation results. This paper contributes to the application of online IFRA.

Since the power transformers are indispensable in power systems, their reliable operation are of significance. Winding deformation is one of the most common failures within transformers. At present, many methodologies have been proposed to detect these faults, and frequency response analysis (FRA) technique is most frequently used because of its low cost, convenience, simplicity and effectiveness. However, there is no standard and reliable code to interpret the mechanical deformations from FRA traces as so far. In this study, an actual transformer experimental platform was used to simulate variable winding deformation faults in order to standardize the interpretation of winding fault degree from FRA data. The measured FRA raw data are processed from the perspective of statistics, and some numerical indices are found to be more suitable for the diagnosis of transformer winding deformation degree.

Reactive molecular dynamics was used to investigate the atomic-level mechanism of formic acid-accelerated deterioration of meta-aramid (PMIA) fibers. The simulation results showed that formic acid promoted PMIA decomposition. The activation energy of a composite system (PF) consisting of formic acid and PMIA was 106.94 kJ/mol at 2000-3000 K, which is 11.95% lower than that of pure PMIA. The main small-molecule products of the PF system were H/C/O-containing molecules (H2O, CO, and CO2), hydrocarbon molecules (e.g., CH4, •C2H, C2H4, and C3H4), N-containing molecules (N2, NH3, and HCN), H2, and various free radicals. Formic acid can promote the production of small molecules such as CO, CO2, and H2O. The N-H bonds, C-N bonds and the amide C═O double bond of PMIA were vulnerable to CO, H ions, and free radicals produced by formic acid decomposition, and this decreased the PMIA stability. Temperature is an important factor in the thermal decomposition of PMIA and can accelerate reactions in the PF system. The initial reaction rate of PMIA at 3000 K was 8.1 times that at 2000 K, and the intermediate reaction rate was 6.2 times that at 2200 K; temperature also affects the types of pyrolysis products, for example, hydrocarbons are high-temperature products.

Polymethylsilsesquioxane (PMSQ) nanoparticles with mass percentages of 0, 2.5, 5.0, 7.2, 9.4 wt %, respectively, were constructed by molecular dynamics methods in this paper. Composite molecular models were established using PMSQ and MPIA (poly-metaphenylene isophthalamide) fiber. The influence of different PMSQ contents on the thermal stability of meta-aramid insulation paper was analyzed from the parameters of mechanical property, interaction energy, and mean square displacement. The results showed that the trend of mechanical properties decreased with the increase of PMSQ content. When the PMSQ content was 2.5 wt %, the mechanical properties of the composited model were the best, which was about 24% higher than that of the unmodified model. From an intermolecular bonding and nonbonding point of view, the energy parameters of composite model with the 2.5 wt % content was better than those of the composite model with other contents. Therefore, it is considered that MPIA can interact better with the 2.5 wt % content PMSQ composite model. When the PMSQ content is 2.5 wt %, the overall chain movement in the composite model is slower than that of the unmodified model, which can effectively inhibit the diffusion movement of the MPIA chain. In general, the thermal stability of composite molecular models MPIA and PMSQ (2.5 wt %) was better improved.

A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method.

Seven hundred fifty kilovolt extra-high voltage (EHV) shunt reactors are important power equipment that stabilize the normal operation of an entire 750 kV power grid. Internal winding faults of a reactor could be a difficult issue when the reactor is in service, and result in sudden equipment failure and significant economic losses. To prevent sudden catastrophic failure of equipment, online detection should be implemented to evaluate the state of the reactor properties when requested. In this paper, a noninvasive method based on online impulse frequency analysis was proposed and developed to detect internal winding faults of a 750 kV EHV shunt reactor. Initially, the safe implementation of the online detecting device was verified before field installation. Unlike in previous work, the online detecting device was modified and improved according to the specific situation of shunt reactors. Original fingerprints of 750 kV EHV shunt reactors were obtained and analyzed after site installation and normal operation of the detecting device. A fault simulation test was implemented to validate the method. This paper provides guidance on actual implementation of online impulse frequency response analysis on power equipment.

Power transformers have important roles as voltage converters in substations, which directly affect the safe operation of power grids. Online monitoring of winding deformation, which is one of the most common transformer faults, is crucial in protecting transformers from damage, particularly those induced by short-circuit current failures, and ensuring their safe operation. This study proposes an improved online monitoring method for winding deformations based on the Lissajous graphical analysis of voltage and current. A practical measurement system is also presented and discussed in this paper. A load normalization method is proposed to solve the problem of Lissajous diagrams changing as load varies. Finally, transformer and winding experiments are performed to verify the validity of the proposed load normalization method and the feasibility of the proposed monitoring method in distinguishing winding deformations with different degrees and locations.

The electromagnetic radiation effect of high-voltage, subnanosecond pulsed electric fields attracts strong interest from researchers because of its significant development potential in biological treatments, particularly in noninvasive diagnosis and treatment. But up to now, high-voltage subnanosecond pulse generators are widely used for national defense and military, and they are difficult to implement successfully in the civil medical field. A compact, self-contained, repetitive frequency, high-voltage subnanosecond pulsed power source is proposed in this paper. It was designed, built, and tested successfully. Based on a four-stage, low-inductance Marx generator, the pulsed source produces subnanosecond rise-time pulses. A chopping switch was designed to cut off unformed signals and generate subnanosecond pulses. A measurement device, based on the principle of a capacitive voltage divider, was also constructed to determine both the amplitude and the rise-time of the pulse delivered by the source. Preliminary tests show that the source can produce repetitive frequency pulses with a peak value that exceeds 30 kV, as well as rise-time and pulse width (full wave at half maximum) within 1 ns under atmospheric pressure conditions. The pulse amplitude may be extended to hundreds of kilovolts by filling the switch system with an inert gas. The rise-time would be shortened as well. The pulsed power source shows optimistic prospects in the biological fields.

Transformer winding deformation is common among all sorts of transformer failures. Cumulative deformation can eventually burgeon into catastrophic faults and result in entire network outage. It is possible to detect the early signs of faults with continuous online monitoring transformer. However, frequency response analysis (FRA) is considered to be a useful and accurate tool for sufficient detection. This paper aims at proposing a novel diagnostic system for online monitoring power transformer winding deformation based on FRA. In order to realize online monitoring transformer winding, the system uses the capacitive coupling method to inject controllable nanosecond pulses, which function as the excitation signal of winding, and to obtain the response signal. This proposed method may extend frequency range for analysis and perhaps could early detect minor winding movement and looseness. Transformer experiments show great prospect in the application of the system.

The power transformer is considered as the most critical and expensive device in substation, however, the irreversible transformer winding mechanical deformation can eventually develop into catastrophic failure if no further steps are taken in a proper way, which would cause the outage of transformer and the significant economic losses. Online frequency response analysis (FRA) has been proven to be a promising tool for condition monitoring and diagnosing of winding deformation. Online FRA relies on graphic comparison of signatures, but up to now, there is no standard and practical interpretation code for signatures classification and quantification. This paper particularly studies the characteristic of online FRA signatures under the winding axial displacement mode, in which the 3D finite element electromagnetic analysis and online transformer equivalent high frequency electrical model are established as auxiliary tools to precisely emulate winding axial displacement. Results of this simulation will provide guidance on understanding online frequency response signatures.