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


Although the image of discharge in the nanosecond time scale will provide some details of a single discharge, the essence of gas discharge remains random underneath the identical macroscopic physical conditions. Thus, the statistical analysis of discharge images including a massive number of stochastic processes in an exceedingly long-time scale is still of nice significance. During this paper, a Digital Image Processing methodology presented in our previously paper is used to research the statistic indicators of AC corona discharge image in the time scale of seconds, and therefore the axial distribution of the common grey level and the grey level customary deviation concerning corona discharge image are determined. Then, these statistical indicators are utilised to review the long brush-like corona, and a clear "stem" caused by the point electrode and not by the ball head electrode was found, whether or not they all belong to the highly non uniform electric field. Considering its corresponding current pulse rise time, we have a tendency to believe that the leader discharge additionally exists within 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 info mining can seemingly be used to provide some new characteristic parameters.

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