The Dielectric Dreakdown Model Of Electrical Tree And Its Prediction

Electrical treeing is of interest to the electrical generation, transmission and distribution industries as it is one of the causes of insulation failure in electrical machines, switchgear and transformer bushings。The fractal geometry is a powerful tool to study the irregular figures in nature and project. On the study of discharge structures in dielectric, the breakdown phenomenon is very complicated and often occurs by means of narrow discharge channels that exhibit a strong tendency to branching into complicated stochastic patterns. Among these breakdown figures, many of them present a close structural similarity and stochastic characteristics, so they can be studied with fractal technology.This paper bases on the basic two-dimensional dielectrics breakdown fractal models (WZ model), and use Matlab7.0 tool to program and carry out the simulation for the development of discharge structures. We should both consider the deterministic and stochastic factors during the development of discharge. Comparing the simulation results, the effects of model parameters on the development of the electrical trees are discussed, such as the critical field and the inner structures field.Based on the WZ model, we simulate the development of electrical trees when different volume of metal-loaded materials whose resistance equals zeros is embedded in the pure dielectric, and which called lattice percolation model. In addition, we also analyze the influencing of the inner field to the breakdown field under different volume of metal-loaded, and compared the simulation breakdown field and the experiment breakdown field under different volume of metal-loaded materials. We also use the Weibull statistics and the first asymptote extreme to statistics of the static dielectric breakdown field.Further more, the deterministic chaos model was generated in the thermal breakdown mechanism. And the fractal dimension of the tree and that of the attractor v fand the Lyapunov exponent were found to be related. We can use the reconstructed attractor to analyze the dispersed of time sequence, and we also used the Lyapunov exponentλto predict the future time sequence and the breakdown time.