Youyuan Wang

Simulation of transient earth voltages aroused by partial discharge in switchgears

Switchgears are the key equipments in the distribution network. Operating switchgear with active Partial Discharge (PD) increases the risk of failure occurring at the time of switching leading to increased risk to the operator. PD in switchgears can be considered to take two forms, surface discharge and internal discharge, if allowed to continue, eventually causes the insulation to break down catastrophically. The electromagnetic pulses produced by PD are in large part conducted away by the surrounding metalwork but a small proportion impinges onto the inner surface of the casing. These charges escape through joints in the metalwork, or a gasket on a gas insulated switch, and pass, as local raised voltages, across the surface of the switch to earth. These pulses of charge were named by Transient Earth Voltages (TEV), and the level of these TEV signals are proportional to the condition of the insulation for switchgear of the same type and model, measured at the same point. So TEV is a very powerful comparative technique for noninvasively checking the condition of switches. In this paper, the Finite-Difference Time-Domain (FDTD) method was employed to simulate the propagation law of TEV in the switchgear. The simulation results show that, the TEV amplitude is proportional to the PD pulse amplitude, increases with PD pulse width decrease and decrease with the distance between detection point and PD source.

Glass transition temperature and mechanical properties in amorphous region of transformer insulation paper by molecular dynamic simulations

The glass transition temperature in amorphous region of insulation paper is one of the most important characteristics for its thermal stability. In order to study the microscopic mechanism of the glass transition process for transformer insulation paper which may provide some information for thermal aging, molecular dynamic simulations has been performed on two micro-structural models: pure amorphous cellulose and amorphous cellulose with water. Using the method of specific volume versus temperature curve, the glass transition temperatures of pure cellulose and cellulose-water were determined as 448K, 418K respectively. Simulation results also show that during the glass transition process, both the chain movement (characterized by mean square displacement, end-to-end distance, and mean square bend) and mechanical properties of cellulose are changing significantly. The addition of water also reduces the mechanical strength of cellulose and increases its ductility and plasticity slightly. In addition, Fox and Florys free volume theory was also used to explain the nature of glass transition which shows a sudden change of free volume between 400 K and 450 K that provides more space for chain movement. The water molecules that immersed in amorphous region of insulation paper can disrupt hydrogen bonds between cellulose chains, which lead to a significant reduction in glass transition temperature.

Calculation and analysis of the current carrying capability of electric cable based on finite element method

A common characteristic of modern power transmission and distribution is the extensive use of underground electric cables. Because of tight economical constraints and limitation on space availability, public utilities around the world are striving to attain higher cable current carrying capability (ampacity), by means of improved designs and, at the same time, trying to achieve better accuracy of cable parameter values so that the simulated results would match as closely as possible the real-life situation. The traditional method of computing cable ampacity is based on IEC-60287, and a much more accurate and versatile approach would use numerical method. In this paper a newer approach to cable thermal field and ampacity computation using finite element method is formulated. In this method, a temperature field distribution model was constructed to analyze the temperature distribution of electric cable area, and the linear interpolation method is employed to calculate the cable ampacity. The developed model was applied to the 8.7/15KV YJV1 x 400 XLPE electric cable, from the example, one can note that the cable ampacity depends on many of the installation properties and conditions, of which the soil thermal resistance and the environment temperature exercised predominant influences. The cable ampacity increases as the soil thermal resistance increases and seems to follow a hyperbolic function, while it decreases linearly with the increases of the environment temperature.

Recognition of UHF PD signals in transformers based on wavelet and fractal theory

This paper presents a new recognition method for ultra-high-frequency (UHF) signal radiated by partial discharge (PD) occurred in transformers, based on wavelet transform and fractal theory. Wavelet transform provides an effective way to decompose a signal on unlimited scales. During this process, the wavelet coefficients corresponding to each scale are obtained, which could be considered as an accurate expression of the decomposed signal. However, the wavelet coefficients as signal features are inavailable for UHF signal recognition because of massive data generated by UHF detection. Fortunately, based on fractal theory, fractal dimensions of the wavelet coefficients, as the compressed features of UHF signal, are calculated by differential boxing-count estimation (DBC) to recognize different PD activities. To testify the effectiveness of our method, an experiment was carried out in laboratory. In experiment, four types of artificial defects models are constructed to generate UHF PD sample data. And A 3rd Hilbert fractal antenna with compact size, which performs well in the properties of radiating pattern and VSWR, is designed to detect signals. The features extracted from the PD data are classified by a radial basis function neural network (RBFNN). The recognition results convince that the recognition method, combining the knowledge of wavelet transform and fractal dimension estimation, is qualified to apply in the field of UHF detection pattern recognition.

Wavelet-based denoising for PD online measurement of transformers

The method based on thresholding and shrinking empirical wavelet coefficients is introduced in this paper for denoising PD signals. Because the wavelet-based denoising can not give a satisfied results while PD pulse amplitude is the same as or less than that of discrete spectral interferences (DSI), the wavelet combined with 2nd order lattice infinite impulse response (IIR) notch filter is proposed to enhance the signal-to-noise ratio (SNR) of signal detected by PD online measurement system of transformers. We present the results from simulations and real data examples.

A GA-based grey prediction model for predicting the gas-in-oil concentrations in oil-filled transformer

Dissolved-gas-analysis (DGA) techniques are widely used to diagnose oil-filled transformer insulation, but the conventional procedure acquiring the gas-in-oil concentrations is not timely. To make up the disadvantage, a new method based on a genetic algorithm and the grey theory to predict the gas-in-oil concentrations is proposed in This work. The grey model (GM(1,1)) has been improved and a new optimized grey model (GM(1,1,/spl beta/)) has been constructed. The genetic algorithm has been applied to search the optimal parameters of the GM(1,1,/spl beta/) model. The validity of the GA-based GM(1,1,/spl beta/) model was verified with two prediction examples.

Extraction of Partial Discharges from Noises by Use of Wavelet and Pulse-Sequence Analysis

This paper presents a de-noising method for partial discharge (PD) online monitoring of transformers. The method is based on an improved wavelet de-noising approach and a pulse-sequence analysis method. Dual couplers are used for detecting the PD pulse current flowing through the high voltage bushing of each phase and the grounding wire connected with the measurement terminal of the high voltage bushing. The white noise can be suppressed by the improved wavelet approach while the extracted pulses have small distortion. The extracted pulse sequences of each coupler from white noise contain the PD pulses and pulse-shaped noises. To separate the PD pulses from noisy pulses, the pulse-sequence analysis on the relationship between two pulse-sequences from the dual couplers is used. The experimental signal processing results show that the measured partial discharges inside the transformers can be separated from white noise and partial discharges occurring on the conductors connected with the high voltage bushings.

PD pattern recognition using combined features

For the purpose of identifying the defects within the insulation, a suitable set of combined features is used as input of back-propagation neural network (BPNN). In this procedure, fractal dimensions and the 2nd generalized dimensions of original PD images and fractal dimensions of high gray intensity PD images are proposed and computed by modified differential box-counting (MDBC) method, and thereafter moments and correlative statistical parameters are studied for recognition of PD images. Therefore feature vector consists of altogether 17 parameters. Meanwhile quadtree partitioning fractal image compression (QPFIC) is used for PD data compression in purpose of improving rate of PD image communication. With PD data gathered in artificial defect experiments, the final analysis results shows the method by means of combined features and BPNN performs effectively in recognition after QPFIC compression of PD images.

Belief network classifier for evaluation of DGA data of transformers

A method to improve the assessment capability of power transformers by using belief network classifier is proposed. Two different belief network classifiers, the Naive Bayes classifier and the tree augmented Naive Bayes classifier, are compared using utilities DGA data analysis. Their respective advantages and shortcomings are also shown by the detailed comparison. More than hundreds of historical DGA data has been used to demonstrate the capability of the method. Classification results show that the two classifiers are suitable for interpretation of DGA data and for diagnosis of incipient faults in transformers.

Recognizing different aging stages of oil-paper based on Fisher discriminant

In this paper, the discriminant analysis method is introduced to identify the aging stages of oil-paper. An identification method based on Fisher discriminant function and multiple PD characteristic parameters is proposed. The 30 vectors distracted from 6 samples at 5 different aging stages are used as reference samples to obtain Fisher discriminant functions. The characteristic vector with eleven-dimension is mapped to two-dimension and the other 30 vectors are analyzed as samples need to be identified using discriminant functions. It is concluded that a reasonable result can be achieved using the Fisher discriminant functions.