超声波法检测电力变压器局部放电的研究
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摘要
本文系统地研究了超声波法检测变压器局部放电的基础理论以及实际应用的关键技术,得到了典型放电模型的真实放电量估计方法;建立了局部放电产生超声波的等值电路;研究了超声波在变压器内部的传播规律;提出了一种基于广义互相关函数的变压器局部放电定位方法;采用了非线性方法对超声波信号进行了特征提取,利用人工神经网络实现了基于超声波信号的局部放电模式识别;提出一种利用超声波法估计变压器局部放电真实放电量的方法;研制了一套基于超声波法的变压器局部放电测量系统。
在三电容模型的基础上,本文建立了纳秒级的局部放电测量等值电路模型,得到实验室内典型放电模型的真实放电量估计方法和估算公式,在实验室条件下实现了典型放电模型的真实放电量估计。仿真和实验结果验证了模型和估算方法的正确和有效性。
利用电-力-声类比的方法建立了基于气隙模型的局部放电产生超声波的力学等值电路,首次提出超声波特征量和局部放电的定量及定性关系,解释了超声波检测局部放电的三个关键问题。研究了超声波的传播规律,对超声波在变压器内部传播时的声衰减、声传播时间以及波形变化进行了定量分析,并得到了根据超声波特征来确定其传播路径的方法。
分析了影响局部放电定位效果的三个主要问题:传感器之间的相对时间差估计、定位算法以及超声波传播路径的影响。提出了一种基于广义互相关函数的声-声定位方法。避免了直接计算超声波信号时间差的问题,提高了定位精度及成功率,研究了多声源与单声源的区分方法,实验室内成功地进行了单声源与多声源的区分。
利用非线性和非平稳的方法对超声波信号进行了分析处理,对于局部放电模型得到的信号,计算了超声波信号的Lyapunov指数、分维数D、空缺率、小波包分解的8个频带能量,得到了11个特征数据,归一化处理后形成特征向量,由人工神经网络进行识别,实现了对局部放电典型模型的模式识别,显著提高了模式识别的正确率,并分析了传播途径对识别的影响。
根据局部放电的发声机理和超声波的传播规律,本文采用原始超声波脉冲进行标定,根据超声波信号的幅值、频率以及衰减时间三个特征,两者对照得到超声波信号的标定基础信息。再采用波形反演法实现超声波信号的波形反演,得到内部实际放电点上超声波信号波形,提取反演后的超声波信号特征与原始标定信号进行比较,实现真实放电量的估计。实验室结果表明该方法的定量精度满足工程实际需要。
研制了一套变压器局部放电测量系统,该系统由传感器、数据采集板和工控机组成,基于LABVIEW软件平台,设计了系统程序。该系统可以方便的给出局部放电的位置、模式以及放电量信息,该系统在变压器上进行的试验表明,测量结果准确。
In the dissertation, the main points of the author’s study are ultrasonic detection of transformer partial discharge. It finds the way of real discharging magnitude estimation from partial discharging model. The equivalent circuit for partial discharge-generated ultrasonic waves is found. The rule of the ultrasonic wave propagating from its source to the sensor is gained. An ultrasonic-ultrasonic locating method with great practical value is put forward in the dissertation. The dissertation resorts to such nonlinear and non-stationary signal processing technologies as chaotic, fractal, and wavelets analysis to come into being characteristic vectors, then, the characteristic vectors are sorted by means of artificial neural network for pattern recognition. It bring forward the way of real discharging magnitude estimation from transformer. An ultrasonic measuring system of transformer partial discharge has been developed.
A nanosecond-level equivalent circuit for partial discharge measuring is built according to actual cases. The formula for real discharging magnitude estimation is gained, and the validity of the model and formula are ratified by digital simulation and relevant experiments.
Resorting to the method of electrical-mechanical-ultrasonical analogy, the dissertation offers the relevant model-of-gap-based equivalent circuit for partial discharge-generated ultrasonic waves. It makes clear three key problems of ultrasonic detection. The dissertation obtains the rules of the ultrasonic wave propagating from its source to the sensor. Also, through laboratory testing has been made clear damping and propagating time constants of ultrasonic wave and wave form changes as well. At the same time the relationship between the waveform and the propagating distance has been recognized.
In the dissertation, three aspects affecting partial discharge location are addressed, relative time differences among the sensors, algorithm of location, and propagation path of ultrasonic wave. From a comprehensive view, an ultrasonic-ultrasonic locating method with great practical value is put forward in the dissertation. The method mentioned in the dissertation improves the precision of the location and effectively constrains divergence of discharge detection. In the laboratory, for the mutual correlation function of multi-source waveform is denser than that of the individual one.
The dissertation resorts to such nonlinear and non-stationary signal processing technologies as chaotic, fractal, and wavelets analysis to come into being characteristic vectors. 11 characteristic data have been worked out, including the maximum Lyapunov index λ, fractional dimension D, lacunarity Λ and eight energies of various frequency bands. After being normalized, they can function as the characteristic vectors. Then, they are transmitted into an artificial neural network to perform pattern recognition.
Starting from the mechanism of ultrasonic generating, initial ultrasonic pulses are calibrated. A stylish discharge source is directly coupled with the ultrasonic sensors, and work out actual discharge magnitude. The ultrasonic signal waveforms reaching the sensors are supposed to be recorded for calculation of magnitude, frequency and attenuation time, etc. Thus the basic calibrated information of the ultrasonic signals is obtained. Then, according to the Green function, the waveform from the sensors can be inversed to estimate the ultrasonic waveform at the actual discharge source. Finally, the characteristics of the resultant waveform should be compared with the initial calibrated information to calculate the actual discharge magnitude. The laboratory data has demonstrated the validity of this quantity method.
An ultrasonic measuring system of transformer partial discharge, on Labview platform, has been developed. It consists of sensors, data acquisition board and computer. The system achieved welcoming results in terms of locationing, pattern recognizing and quantify magnitude. The experiments has positively the system can use on transform
在三电容模型的基础上,本文建立了纳秒级的局部放电测量等值电路模型,得到实验室内典型放电模型的真实放电量估计方法和估算公式,在实验室条件下实现了典型放电模型的真实放电量估计。仿真和实验结果验证了模型和估算方法的正确和有效性。
利用电-力-声类比的方法建立了基于气隙模型的局部放电产生超声波的力学等值电路,首次提出超声波特征量和局部放电的定量及定性关系,解释了超声波检测局部放电的三个关键问题。研究了超声波的传播规律,对超声波在变压器内部传播时的声衰减、声传播时间以及波形变化进行了定量分析,并得到了根据超声波特征来确定其传播路径的方法。
分析了影响局部放电定位效果的三个主要问题:传感器之间的相对时间差估计、定位算法以及超声波传播路径的影响。提出了一种基于广义互相关函数的声-声定位方法。避免了直接计算超声波信号时间差的问题,提高了定位精度及成功率,研究了多声源与单声源的区分方法,实验室内成功地进行了单声源与多声源的区分。
利用非线性和非平稳的方法对超声波信号进行了分析处理,对于局部放电模型得到的信号,计算了超声波信号的Lyapunov指数、分维数D、空缺率、小波包分解的8个频带能量,得到了11个特征数据,归一化处理后形成特征向量,由人工神经网络进行识别,实现了对局部放电典型模型的模式识别,显著提高了模式识别的正确率,并分析了传播途径对识别的影响。
根据局部放电的发声机理和超声波的传播规律,本文采用原始超声波脉冲进行标定,根据超声波信号的幅值、频率以及衰减时间三个特征,两者对照得到超声波信号的标定基础信息。再采用波形反演法实现超声波信号的波形反演,得到内部实际放电点上超声波信号波形,提取反演后的超声波信号特征与原始标定信号进行比较,实现真实放电量的估计。实验室结果表明该方法的定量精度满足工程实际需要。
研制了一套变压器局部放电测量系统,该系统由传感器、数据采集板和工控机组成,基于LABVIEW软件平台,设计了系统程序。该系统可以方便的给出局部放电的位置、模式以及放电量信息,该系统在变压器上进行的试验表明,测量结果准确。
In the dissertation, the main points of the author’s study are ultrasonic detection of transformer partial discharge. It finds the way of real discharging magnitude estimation from partial discharging model. The equivalent circuit for partial discharge-generated ultrasonic waves is found. The rule of the ultrasonic wave propagating from its source to the sensor is gained. An ultrasonic-ultrasonic locating method with great practical value is put forward in the dissertation. The dissertation resorts to such nonlinear and non-stationary signal processing technologies as chaotic, fractal, and wavelets analysis to come into being characteristic vectors, then, the characteristic vectors are sorted by means of artificial neural network for pattern recognition. It bring forward the way of real discharging magnitude estimation from transformer. An ultrasonic measuring system of transformer partial discharge has been developed.
A nanosecond-level equivalent circuit for partial discharge measuring is built according to actual cases. The formula for real discharging magnitude estimation is gained, and the validity of the model and formula are ratified by digital simulation and relevant experiments.
Resorting to the method of electrical-mechanical-ultrasonical analogy, the dissertation offers the relevant model-of-gap-based equivalent circuit for partial discharge-generated ultrasonic waves. It makes clear three key problems of ultrasonic detection. The dissertation obtains the rules of the ultrasonic wave propagating from its source to the sensor. Also, through laboratory testing has been made clear damping and propagating time constants of ultrasonic wave and wave form changes as well. At the same time the relationship between the waveform and the propagating distance has been recognized.
In the dissertation, three aspects affecting partial discharge location are addressed, relative time differences among the sensors, algorithm of location, and propagation path of ultrasonic wave. From a comprehensive view, an ultrasonic-ultrasonic locating method with great practical value is put forward in the dissertation. The method mentioned in the dissertation improves the precision of the location and effectively constrains divergence of discharge detection. In the laboratory, for the mutual correlation function of multi-source waveform is denser than that of the individual one.
The dissertation resorts to such nonlinear and non-stationary signal processing technologies as chaotic, fractal, and wavelets analysis to come into being characteristic vectors. 11 characteristic data have been worked out, including the maximum Lyapunov index λ, fractional dimension D, lacunarity Λ and eight energies of various frequency bands. After being normalized, they can function as the characteristic vectors. Then, they are transmitted into an artificial neural network to perform pattern recognition.
Starting from the mechanism of ultrasonic generating, initial ultrasonic pulses are calibrated. A stylish discharge source is directly coupled with the ultrasonic sensors, and work out actual discharge magnitude. The ultrasonic signal waveforms reaching the sensors are supposed to be recorded for calculation of magnitude, frequency and attenuation time, etc. Thus the basic calibrated information of the ultrasonic signals is obtained. Then, according to the Green function, the waveform from the sensors can be inversed to estimate the ultrasonic waveform at the actual discharge source. Finally, the characteristics of the resultant waveform should be compared with the initial calibrated information to calculate the actual discharge magnitude. The laboratory data has demonstrated the validity of this quantity method.
An ultrasonic measuring system of transformer partial discharge, on Labview platform, has been developed. It consists of sensors, data acquisition board and computer. The system achieved welcoming results in terms of locationing, pattern recognizing and quantify magnitude. The experiments has positively the system can use on transform
引文
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