小波分析在变压器励磁涌流识别中的研究
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摘要
20世纪90年代中后期人工智能以及网络技术的飞速发展,出现了以微机和光传输技术为特征的全数字控制智能保护系统,以此为标志微机继电保护技术呈现出网络化,智能化,以及保护、控制、测量和数据通信一体化的发展趋势。变压器是电力系统中非常重要的能量传递元件,它的正常运行关系到整个电力系统的正常运行。特别是大型变压器本身造价昂贵,一旦发生故障,造成的经济损失是不可预想的。
     电气主设备内部故障的主保护方案是差动保护,差动保护在发电机和线路上的应用比较简单。但是对变压器内部故障的主保护,差动保护有许多特点和困难,这是由于差动保护用于变压器,一方面要反映各种因素产生较大的不平衡电流,另一方面又要求能反映具有流出电流性质的轻微内部短路。因此变压器保护的一些难题始终得不到完善彻底的解决,如果不改变继电保护的传统观念、原理和手段,则很难突破这些障碍。
     基于此,本文力图讨论小波分析在变压器励磁涌流中的应用,将变压器励磁涌流或内部故障发生后的情况小波变换结果来考虑励磁涌流和内部故障的区别。
    
    广西大学硕士学位论文
     实际上,不是任何函数都可以作为小波函数,而小波函数也不是唯
    一的。选择不同的小波函数对变换结果有很大的影响,若选择的母小波
    不合适,则很难对变换后的结果进行识别。基于大量的仿真实验,本文
    提出利用coi fs小波函数对信号奇异性的检测能力,寻求小波变换结果
    的区别。
     最后利用psCAD/〔MToc软件仿真变压器励磁涌流和内部故障,并对
    其进行小波变换。实验结果表明coifs小波函数具有良好的信号检测能
    力,能够检测到励磁涌流的突变点,从而正确识别励磁涌流和内部故障,
    为变压器保护提供可靠的闭锁判据,同时由于励磁涌流识别的复杂性,
    该判据也需要实践的检验。
With the fast development of artificial intelligence and network technology since late 1990's, there appears the digit-controlled intelligence protection system. Based on this system, the micro-computer relay technology has come to present a new trend where network and intelligence constitute the main feature and protection, control and measurement as well as data communications are integrated as a whole. One of the most important equipments in the power system is transformer(an energy transfer element), the normal operation of which bears greatly upon the whole power system, especially costly large-size transformer. In the case of malfunctions, economic damage incurred might be unimaginable.
    The main protection scheme of power equipments against internal faults has been differential protection, the application of which is comparatively simple in the generator and transmission line. Nevertheless, there still exist many difficulties. On the one hand, differential protection is expected to
    
    
    reflect the unbalanced current caused by various factors; On the other hand, it should also reflect internal faults. Therefore, some difficulties in transformer protection still fail to final satisfactory solutions so far. Without changing the traditional conception, principle and measurement, these obstacles would be rather hard to break through.
    In view of these, the research on transformer inrush identification is presented in this paper. The results of wavelet transform caused by either inrush or internal faults will be available to display distinctions between the two.
    In fact, not many functions can be used as wavelet function, and wavelet function is not the only one, too. The choosing to wavelet functions matters a lot. With improper functions, the distinguishing of different transform results would pose as a difficult task. Based on many tests, coif5 wavelet function is to be chosen in this paper. Because it shows the outstanding property to detect signal singularity and seek the result of their wavelet transform.
    Finally, PSCAD/EMTDC the software is adopted to simulate the inrush and internal faults, and then transform them with the help of coif5 wavelet function. The test reveals that coif5 wavelet function possesses the ability to detect the signal and find the singularity of transformer inrush, thus identifying correctly the inrush and internal faults and providing a reliable blocking criterion for transformer protection. In the meanwhile, in view of
    
    the complexity of inrush identifying, this criterion also requires testing in practice.
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