Analysis Of Arcing Fault Of Transmission Line Based On Fractal And Wavelet Transform

In power system, the kinds of faults often occur on HV transmission line, and the single-phase-to earth arcing faults are the main forms of the faults. Moreover, arcing faults often exist in the form of high impedance at fault point and can lead to serious danger to power system and human body. Therefore, it is important to identify arcing faults very effectively. Fractal dimension is one of main factors of Fractal and characterizes the complicated degree and filling spatial degree of researched object. Wavelet transform has excellent localization nature in time and frequency domain, and it can change the time and frequency windows according to variation of signals' frequency.In this paper, a new method based on wavelet transform and fractal is presented, in order to distinguish among normal signal, arcing fault and switching operations. When a fault takes place in power system, the transient waves of voltage and current are different from the wave of normal signal. Fractal is efficient to characterize most of the transient type found in power system signals. The variable trend of fractal dimension of a signal is related with its composition and energy, which takes on shape-V. So it can clearly show whether there is fault in transmission line.When a power network is normally operating, fractal dimension of voltage signal is 1, but when faults take place, its fractal dimension may be larger than 1 or smaller than 1 because of all kinds of factors such as the location of fault or sampling rate. However, there are other transient signals (switching operations) in power system. These transient processes have many common characteristics with that of arcing fault. So, arcing fault cannot effectively be identified only by that result of fractal dimension. A signal transformed by wavelet has two different results. One is frequency analysis in different channels. The other is energy reduction with the increasing of scale. These are related to two factors of fractal dimension (the complicated degree and filling spatial degree). The fractal dimension of arcing fault signal transformed by wavelet is different from that of switching operations. The fractal dimension of arcing fault wave will decrease with increasing of scale, but the fractal dimension of switching operations will increase or decrease with increasing of scale. If noise is thought of as a factor in identifying arcing fault, it is a good way to eliminate noise with wavelet. According to analyzing the signal of eliminating noise, it can conclude that the fractal dimension of voltage signal of arcing fault is different from that of normal signal and will decrease with increasing of scale; the fractal dimension of voltage signal of switching operations is also different form that of normal signal and will increase or decrease with increasing of scale.