Electron-avalanche breakdown in solids is explained by a theory that is predictive and agrees with experimental results for the magnitude of the breakdown field and its temperature dependence, pulse-duration dependence, material-to-material variation, and wavelength dependence for λ > 1 μm. The good agreement between experiment and theory with no parameters adjusted is obtained by using improved magnitudes and energy dependences of the electron-phonon relaxation frequencies. The contributions of both optical and acoustical phonons to electron loss and diffusion must be included. The breakdown field E B is calculated by solving an eigenvalue equation obtained from the diffusion transport equation. Simple models and interpretations of the diffusion equation afford physical insight into breakdown and render the breakdown conditions predictable. Preliminary results indicate that the diffusion approximation fails for wavelengths considerably shorter than 1 μm, but that E B decreases with decreasing X as a result of multiphoton absorption before the diffusion approximation fails.

Theory of electron-avalanche breakdown in solids

Dissolved gas analysis (DGA) of insulating oil can provide an important basis for transformer fault diagnosis. To improve diagnosis accuracy, this paper presents a new transformer fault diagnosis method based on deep belief networks (DBN). By analyzing the relationship between the gases dissolved in transformer oil and fault types, the Non­code ratios of the gases are determined as the characterizing parameter of the DBN model. DBN adopts multi-layer and multi-dimension mapping to extract more detailed differences of fault types. In this process, the diagnosis parameters are pre-trained. A back-propagation algorithm adjusts them with the labels of the samples and optimizes the parameters. To verify the effect of the proposed method, the diagnostic DBN model is constructed and tested using various oil chromatographic datasets collected from the State Grid Corporation of China and previous publications. The performances of the DBN diagnosis model are analyzed by different characterizing parameters, different training datasets and sample datasets. In addition, the influence of discharge and overheating multiple faults on the diagnosis model is studied. The performance of the proposed approach is compared with that derived from support vector machine (SVM), back-propagation neural network (BPNN) and ratio methods respectively. The results show that the proposed method significantly improves the accuracy of power transformer fault diagnosis.

Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network

Transformers are the most important equipment in power systems, and their failure can cause serious problems. In order to avoid hazardous operating conditions and reduce outage rates, fault detection in the incipient stage is necessary. Incipient faults cause thermal or/and electrical stresses on the transformer with a major consequence on insulation decomposition. The insulation decomposition causes the evolution of gases which can be dissolved in oil. Dissolved gas analysis (DGA) interpretation is one of the main techniques used for fault diagnosis in oil-immersed transformers. In this paper, DGA interpretation is evaluated in detecting different faults and the techniques considered as conventional methods of DGA are investigated. The evaluation is based on DGA data obtained from oil samples of real transformers.

Dissolved gas analysis evaluation in electric power transformers using conventional methods a review

On the vibrations of the imperfect sandwich higher-order disk with a lactic core using generalize differential quadrature method

Melting process of nanoparticle enhanced PCM through storage cylinder incorporating fins

This paper focuses on the influence of repetitive square wave voltage rise time on partial discharge pulse characteristics in the ultra-high frequency range. Paerial Discharge (PD) tests on single-point contact crossed pairs were carried out using repetitive square wave voltages with a wide range of rise times, from 200 ns to 400 μs, including also 50 Hz sinusoidal voltage waveforms. Experimental results show that rise time has a significant influence on magnitude and frequency spectrum of PD pulses. Voltages of shorter rise times generate larger PD pulses with a predominance of high frequency components in the energy spectrum. Interpretations are presented for this phenomenon.

Effect of rise time on PD pulse features under repetitive square wave voltages

This paper presents a novel approach for power transformer incipient fault diagnosis through the analysis of dissolved gas in oil. The proposed approach is implemented for improving the diagnosis accuracy by dissolved gas analysis (DGA) of power transformer based on the combined use of a multi-classification algorithm self-adaptive evolutionary extreme learning machine (SaE-ELM) and a simple arctangent transform (AT). On the one hand, the SaE-ELM algorithm has the ability to approximate any nonlinear functions with its structure parameters, i.e. hidden node biases and output weights, optimized self-sufficiently. On the other hand, the AT can alter the data structure of the experiment data, which will enhance the generalization capability for SaE-ELM as well as other machine learning algorithms. Thus, the combination of SaEELM and AT can complement each other and improve the diagnosis accuracy from the aspect of both algorithm and data structure. The performances of the proposed approach are compared with that derived from ANN, SVM, and ELM methods, respectively. Experimental results with both published and power utility provided data indicate that the developed approach can significantly improve the accuracies for power transformer fault diagnosis.

Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform

Carbon/copper composites are widely used in collector brushes, pantograph sliders, collector shoes and other fields due to their excellent mechanical friction, wear, thermal conductivity and electr...

Interfacial bonding enhancement and properties improvement of carbon/copper composites based on nickel doping

As a core component of AC drive electric locomotive, performance of inverter-fed motor directly influences the stability and reliability of locomotive running. However, insulation early failures occur frequently. Presently, polyimide film, as a special type of engineering plastic film, widely applied in turn to turn insulation and turn to ground insulation of inverter-fed motors is a kind of basic insulating material. This paper investigates charge transport mechanism and space charge characteristics of polyimide films based on measuring and comparing space charge accumulation threshold field between corona-resistant and common polyimide films and analyzing the effect of temperature on space charge distribution. The results reveal that space charge accumulation threshold field of corona-resistant polyimide film is higher than that of common polyimide film; the adding of nano-particles effectively increases dielectric properties of corona-resistant film. Additionally, raising temperature promotes the electrode injecting charge and increases the charge energy and conductivity which gradually enhances space charge number and extends trapping position into the bulk, closely related to polyimide film insulation aging and breakdown which is an important characteristic.

Study on charge transport mechanism and space charge characteristics of polyimide films

As one of the most important electrified railway components, the pantograph-catenary (PC) system plays a key role in the energy transfer for the train through the sliding electric contact. However, the performance of the PC system is severely limited by pantograph arcing, and the study on the dynamic characteristics of pantograph arcing is still far away from satisfactory. In this article, a magnetohydrodynamic (MHD) model of pantograph arcing is first established, which focused on the impact of crosswind as well as the input current on the arcing dynamics. Once the model was validated by laboratory experiment, the influences of the crosswind and input current on the arc temperature and voltage are studied in detail. Two parameters, namely the diameter of the catenary and the gap distance between the pantograph strip and the catenary, were varied to see what effects that might have. An interesting periodic phenomenon for both the temperature and voltage fall has been found, which was explained based on the Karman vortex street of fluid theory.

Guangning Wu

Papers

Effect of rise time on PD pulse features under repetitive square wave voltages

Interpretation of DGA for transformer fault diagnosis with complementary SaE-ELM and arctangent transform

Interfacial bonding enhancement and properties improvement of carbon/copper composites based on nickel doping

Study on charge transport mechanism and space charge characteristics of polyimide films

Influence of the Crosswind on the Pantograph Arcing Dynamics