Statistical evaluation of AC corona images in long-time scale and characterization of short-gap leader

2016 
Although the image of discharge in the nanosecond time scale can provide some details of a single discharge, the essence of gas discharge remains random under the same macroscopic physical conditions. Therefore, the statistical evaluation of discharge images including a large number of stochastic processes in a long-time scale is still of great significance. In this paper, a digital image processing method presented in our previously paper is used to research the statistic indicators of AC corona discharge image in the time scale of seconds, and the axial distribution of the average gray level and the gray level standard deviation about corona discharge image are determined. Then, these statistical indicators are utilized to study the long brush-like corona, and a clear �stem� caused by the point electrode and not by the ball head electrode was found, even if they all belong to the highly non uniform electric field. Considering its corresponding current pulse rise time, we believe that the leader discharge also exists in the cm-level short gap. These results indicate that the statistical analysis on the longtime scales can be used in discharge research, and further image information mining will likely be used to provide some new characteristic parameters.
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