Timely identification of the aging stage of the transformer can effectively ensure the safe operation of the transformer and prevent the aging fault of the transformer. This work proposes the aging diagnosis of transformer using the Raman spectroscopic recurrence plot combined with learning vector quantization (LVQ) neural network. Oil-paper insulation samples at different aging stages are obtained by accelerated thermal aging tests. Acquisition of Raman spectra of insulating oil is from 800 to 3000 cm−1 and converts to the Raman spectroscopic recurrence plot. The gray-level cooccurrence matrix (GLCM) is used to extract the characteristic information of the Raman spectroscopic recurrence plot. The optimal number of neurons in competitive layer of LVQ neural network is determined by the experimental method. A transformer aging diagnosis model based on the Raman spectroscopic recurrence plot is established. The results show that the diagnostic accuracy of the proposed diagnostic model reached 95%. The Raman spectroscopic recurrence plot has more abundant aging “fingerprint” characteristic information and the proposed diagnostic method shows potential for monitoring oil-paper insulation.

A Novel Recognition Method of Aging Stage of Transformer Oil-Paper Insulation Using Raman Spectroscopic Recurrence Plots

Power transformer is an important equipment of power system. The inner high voltage will cause transformer insulation deterioration fault, which can cause large-scale power outage. Partial discharge (PD) is an important phenomenon of transformer insulation degradation, so the research, diagnosis and identification of transformer partial discharge is very necessary. In this paper, recognition of partial discharge based on a time domain feature extraction is proposed. We collected PD signals through a high-voltage partial discharge device, using high-frequency current sensors. The discharge types include four kinds of defects: oil gap defect, floating defect, tip defect and surface defect. Wavelet transform was used to remove noise interference, and convolutional Neural Network was utilized to extract and identify signal features.

Recognition of Transformer High Frequency Partial Discharge Based on Time Domain Feature

An Electrical life is an important way to assess the insulation properties of dielectric materials. The breakdown voltage of insulating paper is an important electric feature. In this study, the electrical and physical properties of oil-impregnated insulation paper are investigated experimentally. For various periods, the breakdown voltage and time of mineral oil and vegetable oil-impregnated paper insulation were determined. The impact of insulation paper thickness on electrical and mechanical characteristics has been investigated. The results showed that as the time of immersing insulation paper in oil at room temperature increased, the breakdown voltage reduced and vice versa. As the thickness increases, breakdown voltage increases and decreases for a reduced thickness. 

Investigation Of Different Solid Insulation Impregnated In Liquid Dielectrics

The largest part of the water control project belongs to the low-head riverbed floodgate project, and its discharge capacity can be accurately determined in design, which can avoid the inundation of the upstream fertile land and at the same time achieve the purpose of reducing the scale of flood discharge structures and saving project investment. In this paper, the influence of different height difference between the dam floor and the upstream and downstream riverbeds on the discharge capacity is studied through the model test of the discharge capacity and its related parameters of the spillway gate of the orifice outflow dam, and the three-dimensional flow field of the discharge flow of the spillway gate is numerically simulated by FLUENT commercial software. The Reynolds equation is closed by using the standard K-e turbulence numerical model, and the free liquid level is tracked by using the volume of fluid (VOF) method. The tetrahedral unstructured grid is used to divide the grid, and the calculation results of the discharge capacity, flow pattern, water level elevation, velocity field, and pressure field of the floodgate are obtained. Compared with the observation results of the model test, they are in good agreement, which shows that the model used in the numerical simulation is reasonable and the method is correct.

Study on Water Flow Capacity and Related Parameters of Spillway Gate of Orifice Outflow Dam

Power transformer is an important equipment of power system. The inner high voltage will cause transformer insulation deterioration fault, which can cause large-scale power outage. Partial discharge (PD) is an important phenomenon of transformer insulation degradation, so the research, diagnosis and identification of transformer partial discharge is very necessary. In this paper, recognition of partial discharge based on a time domain feature extraction is proposed. We collected PD signals through a high-voltage partial discharge device, using high-frequency current sensors. The discharge types include four kinds of defects: oil gap defect, floating defect, tip defect and surface defect. Wavelet transform was used to remove noise interference, and convolutional Neural Network was utilized to extract and identify signal features. 

There is a relative lack of research on the development of creepage discharge in aged oil-paper insulation subjected to AC-DC combined voltage in converter transformers. In this study, based on the physical and chemical properties of oil-paper, we propose a sphere-plate electrode model to study the creepage discharge parameters and characteristic spectra of aged oil-paper subjected to AC-DC combined voltage. The results show that the creepage discharge inception voltage of aged oil-paper is related to the AC voltage component of the AC-DC combined voltage. However, when the DC voltage component is 60 kV, the flashover phenomenon was found to occur in one of the severely aged samples before the AC voltage was applied. Despite being subjected to the same DC voltage, the flashover phenomenon did not occur in three other samples aged to different degrees. Large-amplitude discharges were found to appear earlier in the severely aged samples. At the same creepage discharge stage, as the aging time of the oil-paper increased, the number of large-amplitude discharges was found to rapidly increase, and the time interval between adjacent discharges was found to gradually decrease. At each stage of the creepage discharge, the small-amplitude discharge has a “hump” shaped distribution. The larger the DC voltage component, the shorter the endurance time of aged oil-paper, and the more severe the damage to the oil-paper. The DC voltage component was found to have an accelerating effect on the development of creepage discharge in aged oil-paper insulation.

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Influence of Aging on Creepage Discharge Characteristics of Oil-Paper Insulation Under AC-DC Combined Voltage

The recognition of the development stage of creepage discharge of oil-paper insulation under AC-DC combined voltage is the basis of fault monitoring and diagnosis of converter transformers, and there are few related studies. In this paper, the AC-DC combined voltage with a ratio of 1:1 was used to study the development stage recognition method of creepage discharge of oil-paper insulation under the cylinder-plate electrode structure. Firstly, the pulse current method was used to collect the discharge signals in the process of creepage discharge development. Finally, based on the online sequential extreme learning machine (OS-ELM), the above characteristic parameters were used to recognize the development stage of oilpaper insulation creepage discharge. The research results show that when the size of the sample training set in the OS-ELM algorithm is closed to the number of hidden layer neurons, higher recognition accuracy can be obtained, and the type of activation function has less influence on it. Based on the OS-ELM algorithm, the developmental stage of the creepage discharge is recognized. The development process of the creepage discharge is recognized as four stages, which is the same as the trend of the characteristic parameters of the whole creepage discharge development process, and the recognition accuracy is 91.4%. The algorithm has fast rate, high accuracy and batch training data characteristics, which can be widely used in the field of online monitoring and evaluation of electrical equipment status.

/pdf/pattern-recognition-of-development-stage-of-creepage-l2bcmn6cxs.pdf

Pattern Recognition of Development Stage of Creepage Discharge of Oil-Paper Insulation under AC-DC Combined Voltage based on OS-ELM

This paper describes the relationship between Lyapunov index and the chaos of the dynamics system. Mainly describe the Lorenz system and Rossler system, and calculated the Lyapunov index of these two systems with nonlinear dynamics method. Analyses the iterative chaotic characteristic of Logistic system. And then calculate the Lyapunov index with the discrete system Lyapunov index computing method, and then calculated the correlation dimension.

/pdf/numerical-calculations-for-nonlinear-characteristics-of-11p0pxw2xj.pdf

Liang Bin

Papers

Influence of Aging on Creepage Discharge Characteristics of Oil-Paper Insulation Under AC-DC Combined Voltage

Pattern Recognition of Development Stage of Creepage Discharge of Oil-Paper Insulation under AC-DC Combined Voltage based on OS-ELM

Numerical Calculations for Nonlinear Characteristics of Chaotic Systems