变压器局部放电超高频监测分形天线与最优小波去噪及信号识别研究
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摘要
局部放电与电力变压器内部绝缘缺陷具有紧密联系,通过局部放电在线监测能够及时判断变压器内部绝缘状态,对防止电力变压器事故发生,保障电力系统安全稳定运行具有重要意义。
由于局部放电超高频监测能够有效避开低频电磁干扰,从而避开变压器外部电晕等脉冲干扰的影响,近年来得到了广泛重视。然而,将电力变压器局部放电超高频监测系统应用于工程实际,仍然需要在传感技术、抗干扰技术以及局部放电模式识别技术等方面开展深入研究。本文在对变压器局部放电在线监测研究现状总结分析的基础上,对用于局部放电超高频监测的Hilbert分形天线、自适应最优小波去噪及多尺度能量参数和网格维数的提取与识别等问题进行了系统深入的研究。
①在深入研究分形天线基本原理的基础上,针对电力变压器的实际情况,通过仿真分析几何尺寸、导线宽度、天线阶数等参数对Hilbert分形天线性能的影响,首次提出了一种用于变压器局部放电超高频在线监测的Hilbert分形天线设计方法,制作出三阶Hilbert分形天线,该天线几何尺寸小、频带宽,可通过放油阀置于变压器箱体内壁,其检测频带、驻波比、输出阻抗和方向性等参数能够满足变压器局部放电超高频监测要求。
②设计了三种典型人工油纸绝缘局部放电缺陷,在实验室模拟变压器内部局部放电,测试了三阶Hilbert分形天线的局部放电测量性能,并分析了油纸屏障对局部放电超高频测量信号幅值及频谱的影响;另外,设计了四种典型变压器内部绝缘缺陷的人工模型,采用三阶Hilbert分形天线测量并获得了大量人工绝缘缺陷局部放电超高频信号样本数据,对四种类型局部放电超高频信号频谱进行了对比分析,为变压器局部放电超高频信号识别奠定了实验基础。
③在对小波阈值去噪方法进行深入研究的基础上,首次提出一种适用于局部放电监测的自适应最优小波去噪方法,该方法根据各层信号分解的尺度系数能量最大原则进行逐层最优基小波自适应选择,采用具有一阶连续导数阈值化函数及遗传算法进行尺度相关的最优阈值自适应估计。局部放电高频仿真信号与人工绝缘缺陷模型实验实测的局部放电超高频信号的去噪仿真实验结果表明,采用本文提出的自适应最优小波去噪方法的去噪效果明显优于标准小波阈值去噪方法,且计算速度快。
④首次提出一种局部放电超高频信号多尺度特征提取方法,该方法通过对局部放电超高频信号的小波或小波包多尺度变换,提取局部放电超高频信号多尺度分解信号的能量参数及网格维数;为有效计算局部放电超高频信号的网格维数,提出了一种二维曲线网格维数估计方法,比原差盒计数法具有更准确的估计结果。采用本文提出的多尺度特征提取方法和反向传播神经网络对四种人工绝缘缺陷放电样本的识别正确率最低达到84.17%。
上述理论分析与实验研究结果表明,本文提出的三阶Hilbert分形天线、自适应最优小波去噪以及信号的多尺度特征参数的提取与识别等方法,能有效实现变压器局部放电超高频监测与信号识别,具有良好的应用前景。
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.
①Thorough 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.
②Firstly, 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.
③On 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.
④A 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.
由于局部放电超高频监测能够有效避开低频电磁干扰,从而避开变压器外部电晕等脉冲干扰的影响,近年来得到了广泛重视。然而,将电力变压器局部放电超高频监测系统应用于工程实际,仍然需要在传感技术、抗干扰技术以及局部放电模式识别技术等方面开展深入研究。本文在对变压器局部放电在线监测研究现状总结分析的基础上,对用于局部放电超高频监测的Hilbert分形天线、自适应最优小波去噪及多尺度能量参数和网格维数的提取与识别等问题进行了系统深入的研究。
①在深入研究分形天线基本原理的基础上,针对电力变压器的实际情况,通过仿真分析几何尺寸、导线宽度、天线阶数等参数对Hilbert分形天线性能的影响,首次提出了一种用于变压器局部放电超高频在线监测的Hilbert分形天线设计方法,制作出三阶Hilbert分形天线,该天线几何尺寸小、频带宽,可通过放油阀置于变压器箱体内壁,其检测频带、驻波比、输出阻抗和方向性等参数能够满足变压器局部放电超高频监测要求。
②设计了三种典型人工油纸绝缘局部放电缺陷,在实验室模拟变压器内部局部放电,测试了三阶Hilbert分形天线的局部放电测量性能,并分析了油纸屏障对局部放电超高频测量信号幅值及频谱的影响;另外,设计了四种典型变压器内部绝缘缺陷的人工模型,采用三阶Hilbert分形天线测量并获得了大量人工绝缘缺陷局部放电超高频信号样本数据,对四种类型局部放电超高频信号频谱进行了对比分析,为变压器局部放电超高频信号识别奠定了实验基础。
③在对小波阈值去噪方法进行深入研究的基础上,首次提出一种适用于局部放电监测的自适应最优小波去噪方法,该方法根据各层信号分解的尺度系数能量最大原则进行逐层最优基小波自适应选择,采用具有一阶连续导数阈值化函数及遗传算法进行尺度相关的最优阈值自适应估计。局部放电高频仿真信号与人工绝缘缺陷模型实验实测的局部放电超高频信号的去噪仿真实验结果表明,采用本文提出的自适应最优小波去噪方法的去噪效果明显优于标准小波阈值去噪方法,且计算速度快。
④首次提出一种局部放电超高频信号多尺度特征提取方法,该方法通过对局部放电超高频信号的小波或小波包多尺度变换,提取局部放电超高频信号多尺度分解信号的能量参数及网格维数;为有效计算局部放电超高频信号的网格维数,提出了一种二维曲线网格维数估计方法,比原差盒计数法具有更准确的估计结果。采用本文提出的多尺度特征提取方法和反向传播神经网络对四种人工绝缘缺陷放电样本的识别正确率最低达到84.17%。
上述理论分析与实验研究结果表明,本文提出的三阶Hilbert分形天线、自适应最优小波去噪以及信号的多尺度特征参数的提取与识别等方法,能有效实现变压器局部放电超高频监测与信号识别,具有良好的应用前景。
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.
①Thorough 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.
②Firstly, 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.
③On 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.
④A 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.
引文
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