| The insulation defects in power transformer contact with partial discharge (PD) closely. The PD on-line monitoring can be used to judge the insulation situation of power transformer, and it is valuable to prevent accident faults in power transformer and insure stable performance of power system.Ultra-high-frequency (UHF) monitoring approach is focused on recent years because of its effectiveness to avoid low-frequency noises and pulse interference from corona. However, there are still unsolved problems to obstacle the on-site application of UHF on-line monitoring system for PD in transformers, such as: sensing technology, anti-interference technique, PD pattern recognition and so on. Based on summarize and analyze the research status of PD on-line monitoring in transformers, this paper made a systematic and thorough study on the Hilbert fractal antenna for PD UHF on-line monitoring, the adaptive optimal wavelet de-nosing and extraction and identification of multi-scale energy feature and gridding dimension feature etc.â‘ Thorough research on basic principle of fractal antenna and based on the practical situation of power transformer, the influence of the geometry, linewidth, antenna order and other parameters to the performance of Hilbert fractal antenna have been analysised in this paper. A type of Hilbert fractal antenna was first presented to be applied for UHF on-line monitoring for PD in transformer. The third-order Hilbert fractal antenna is designed, which can be placed in inner wall of transformer through oil drain valve, and the Bandwidth, voltage standing wave ratio (VSWR), output impedance, directivity and other parameters of fractal antenna can achieve the requirement of PD UHF on-line monitoring.â‘¡Firstly, three typical PD artificial insulation defect models was designed, then PD in transformer was simulated and the PD detection performance of the third-order Hilbert fractal antenna is tested in the laboratory, and the oil-paper barriers to UHF signal amplitude and spectrum for PD UHF measurement were analysised in effect. In addition, four typical artificial insulation defect models in transformer are designed, the third-order Hilbert fractal antenna was used to detected the PD UHF signal, and a great deal of data were obtained. The spectrums of four types PD UHF signals were analyzed, and it was laid the experimental foundation for PD UHF signal recognition.â‘¢On the basis of detailed analysis of wavelet threshold de-noising method, an adaptive optimal wavelet de-nosing method is first presented for PD on-line monitoring. According to the principle of maximum energy for scale coefficients, the optimal wavelet can be selected adaptively in each scale. A new threshold function with one order continuous derivative and the genetic algorithm (GA) are used for scaled optimal threshold adaptive estimation. The de-nosing results of simulation PD high-frequency signals and PD UHF signals show that the adaptive optimal wavelet de-nosing method presented in this paper is superior to the standard wavelet threshold method and the computational speed is quick.â‘£A method of extraction multi-scale feature form PD UHF signal is first presented in this paper. The method based on the wavelet multi-resolution transform and wavelet packet multi-resolution transform, extracts fractal feature and wavelet energy feature from multi-scale decomposition signal of PD UHF signal. To calculate the gridding dimension of PD UHF signal, a gridding dimension estimate method is presented, which has a more accurate estimate results than the original difference box-counting method. The lowest recognition rate of four type PD UHF signals reached 84.17% by using the multi-scale feature extraction method and back propagation neural network (BPNN).The above-mentioned theory analyses and experimental results show that the proposed third-order Hilbert fractal antenna, the adaptive optimal wavelet de-nosing, the multi-scale feature extraction and recognition method etc, has good application prospects.
|
PDF Full Text Request