小波能量谱在超高压输电线暂态识别中的应用
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摘要
超高压输电线路是长距离输电中的重要部分。为了保证高质量的输送电能,对超高压输电线路稳定可靠运行的要求就很高,如系统产生扰动信号,必须及时的判别是否由故障产生,若不能及时准确的做出判断,切除故障,将会影响正常供电,引起巨大的生命财产损失。为了避免事故的进一步扩大,提高输电网络运行的可靠性和稳定性,需要及时正确的识别不同类型、不同位置的暂态信号,以便尽早的排除故障,避免进一步的损失。随着电力输电网络的扩大和电压等级的升高,输电线路暂态信号识别就变得日益重要。
本论文在详细、深入的分析了超高压输电线路特点的基础上,研究和综述了目前国内外学者对于超高压输电线路暂态信号识别的研究现状,并对小波能谱在暂态信号识别中应用的现状进行了分析。
在阐述小波多分辨分析的基础上,介绍了小波能谱的基本原理,并提出了基于小波能谱的矩阵相似度和小波相对熵的概念,为仿真和算法的建立奠定了理论基础。为了解决不同电流幅值引起的判断困难问题,提出将小波能谱矩阵进行归一化处理的思路,将同一尺度上不同值域的数据都映射到同样的值域内。
探讨了利用小波能谱识别电力暂态的可行性。首先建立了基于PSCAD/EMTDC的500kV超高压输电线路模型,仿真了单相接地短路故障、开关操作、故障性雷击和雷电扰动四种超高压输电线路中比较常见的暂态信号,并对比分析了它们的小波能谱。借鉴数学空间距离的计算方法,定量分析了不同暂态信号之间小波能谱的差别。比较分析结果表明,不同暂态信号在相同的高频和低频段能量分布存在差别,因此,利用小波能谱识别不同暂态信号是可行的。
提出了基于小波能谱矩阵相似度的暂态识别方法。针对不同类型,不同位置的暂态信号的能量分布不同的特点,利用归—化小波能谱矩阵作为判断依据,计算待识别信号与标准能谱矩阵之间的矩阵相似度,根据相似度极大原则区分不同信号。仿真分析了四种常见的不同类型暂态信号的识别,以及不同位置开关操作产生暂态信号识别。仿真分析表明,基于小波能谱矩阵相似度的识别方法分类效果较好,能达到实际暂态信号识别的要求。
深入研究了小波相对熵应用于暂态信号识别中的潜力,对比分析了不同工况下暂态信号与其标准能谱矩阵之间、不同类型暂态信号的标准能谱矩阵之间、不同工况下同类信号自身高低频段之间的三种小波相对熵的变化趋势和值域。根据信号各自的小波相对熵特点,给出了基于小波相对熵的输电线路暂态信号识别的方案。该方案同时利用信号与其标准矩阵之间和信号自身高低频能谱之间的两种小波相对熵的值域对不同暂态信号进行区分,仿真分析结果表明,该方案识别效果好,计算简便,可望在输电线路暂态识别中有一定的应用前景。
Extra High Voltage (EHV) transmission line is a very important part of long distance transmission. For high quality of power transmission, the stability and reliability of transmission line are so crucial. For example, a disturbance signal, which could not be recognized and cleared soon, may lead to large fault. It will affect the normal operation and great loss. Correct recognition of different type and location of transients is very important in improvement of the stability and reliability of power net and can avoid greater fault. With the development of power net and rise of voltage, recognition of transmission line transients becomes more and more important.
On the base of analysis of EHV transmission line, the paper do some work on the resent research of its transient signal recognition and wavelet energy spectrum application in this filed.
Based on multi-resolution, the wavelet energy spectrum is proposed. The matrix similarity and relative entropy, which are deduced by wavelet energy, are analyzed. This analysis provides a good foundation of further simulation and algorithm. In order to weaken the effect of different current magnitude, a normalized wavelet energy matrix is given. This normalized matrix could make all different data range mapped in a same range.
A 500kV EHV transmission line is modeled in PSCAD/EMTDC. Four common kinds of transients, single phase to ground, breaker operation, faulted lightning and lightning disturbance, are modeled. Their wavelet energy spectrum is analyzed based on the method of math space distance, contrastively. From the contrast, it is easy to find that there are differences between different transient signals in both high and low frequency band. Thus, wavelet energy spectrum is a potential tool for transient signal recognition.
With the different features of signals of different types and locations, the normalized wavelet energy matrix which can sharpen those differences is used in this paper to classify signals. Matrix similarities between signals and standard matrixes are calculated. When the similarity gets its max value, the signal is considered to be that standard type. In the simulation, transient signals of different types and different breaker locations are recognized. The simulation results shows the method based on wavelet energy matrix similarity is effective in classifying different signals and meets the needs of practical use.
Finally, the application of wavelet relative entropy in EHV is studied. The relative entropy of three kinds--the entropy between signal and its standard matrix, the entropy between signals of different types, the entropy between high and low frequency band of certain signal, are analyzed, contractively. Based on this analysis, a recognition method based on wavelet relative entropy is proposed. Simulation is done in the same model above. Two kinds of entropy are adopted in this simulation. Shown by the simulation results, the wavelet relative entropy could distinguish different kinds of transient signals. This method is easy to done and needs little calculation. It is a potential tool for transmission line signal recognition.
本论文在详细、深入的分析了超高压输电线路特点的基础上,研究和综述了目前国内外学者对于超高压输电线路暂态信号识别的研究现状,并对小波能谱在暂态信号识别中应用的现状进行了分析。
在阐述小波多分辨分析的基础上,介绍了小波能谱的基本原理,并提出了基于小波能谱的矩阵相似度和小波相对熵的概念,为仿真和算法的建立奠定了理论基础。为了解决不同电流幅值引起的判断困难问题,提出将小波能谱矩阵进行归一化处理的思路,将同一尺度上不同值域的数据都映射到同样的值域内。
探讨了利用小波能谱识别电力暂态的可行性。首先建立了基于PSCAD/EMTDC的500kV超高压输电线路模型,仿真了单相接地短路故障、开关操作、故障性雷击和雷电扰动四种超高压输电线路中比较常见的暂态信号,并对比分析了它们的小波能谱。借鉴数学空间距离的计算方法,定量分析了不同暂态信号之间小波能谱的差别。比较分析结果表明,不同暂态信号在相同的高频和低频段能量分布存在差别,因此,利用小波能谱识别不同暂态信号是可行的。
提出了基于小波能谱矩阵相似度的暂态识别方法。针对不同类型,不同位置的暂态信号的能量分布不同的特点,利用归—化小波能谱矩阵作为判断依据,计算待识别信号与标准能谱矩阵之间的矩阵相似度,根据相似度极大原则区分不同信号。仿真分析了四种常见的不同类型暂态信号的识别,以及不同位置开关操作产生暂态信号识别。仿真分析表明,基于小波能谱矩阵相似度的识别方法分类效果较好,能达到实际暂态信号识别的要求。
深入研究了小波相对熵应用于暂态信号识别中的潜力,对比分析了不同工况下暂态信号与其标准能谱矩阵之间、不同类型暂态信号的标准能谱矩阵之间、不同工况下同类信号自身高低频段之间的三种小波相对熵的变化趋势和值域。根据信号各自的小波相对熵特点,给出了基于小波相对熵的输电线路暂态信号识别的方案。该方案同时利用信号与其标准矩阵之间和信号自身高低频能谱之间的两种小波相对熵的值域对不同暂态信号进行区分,仿真分析结果表明,该方案识别效果好,计算简便,可望在输电线路暂态识别中有一定的应用前景。
Extra High Voltage (EHV) transmission line is a very important part of long distance transmission. For high quality of power transmission, the stability and reliability of transmission line are so crucial. For example, a disturbance signal, which could not be recognized and cleared soon, may lead to large fault. It will affect the normal operation and great loss. Correct recognition of different type and location of transients is very important in improvement of the stability and reliability of power net and can avoid greater fault. With the development of power net and rise of voltage, recognition of transmission line transients becomes more and more important.
On the base of analysis of EHV transmission line, the paper do some work on the resent research of its transient signal recognition and wavelet energy spectrum application in this filed.
Based on multi-resolution, the wavelet energy spectrum is proposed. The matrix similarity and relative entropy, which are deduced by wavelet energy, are analyzed. This analysis provides a good foundation of further simulation and algorithm. In order to weaken the effect of different current magnitude, a normalized wavelet energy matrix is given. This normalized matrix could make all different data range mapped in a same range.
A 500kV EHV transmission line is modeled in PSCAD/EMTDC. Four common kinds of transients, single phase to ground, breaker operation, faulted lightning and lightning disturbance, are modeled. Their wavelet energy spectrum is analyzed based on the method of math space distance, contrastively. From the contrast, it is easy to find that there are differences between different transient signals in both high and low frequency band. Thus, wavelet energy spectrum is a potential tool for transient signal recognition.
With the different features of signals of different types and locations, the normalized wavelet energy matrix which can sharpen those differences is used in this paper to classify signals. Matrix similarities between signals and standard matrixes are calculated. When the similarity gets its max value, the signal is considered to be that standard type. In the simulation, transient signals of different types and different breaker locations are recognized. The simulation results shows the method based on wavelet energy matrix similarity is effective in classifying different signals and meets the needs of practical use.
Finally, the application of wavelet relative entropy in EHV is studied. The relative entropy of three kinds--the entropy between signal and its standard matrix, the entropy between signals of different types, the entropy between high and low frequency band of certain signal, are analyzed, contractively. Based on this analysis, a recognition method based on wavelet relative entropy is proposed. Simulation is done in the same model above. Two kinds of entropy are adopted in this simulation. Shown by the simulation results, the wavelet relative entropy could distinguish different kinds of transient signals. This method is easy to done and needs little calculation. It is a potential tool for transmission line signal recognition.
引文
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