A new stochastic model for partial discharge processes

In new stochastic model for Partial Discharge (PD) processes is presented. It is shown that a PD process is a special stochastic process, namely a piecewise deterministic Markov process consisting of point-like discharges and drift/recombination phases between the discharges. It is determined by few basic physical parameters and, in a stochastic process framework, can be formulated in a master equation. Only the fast point-like discharges can be measured as PD signals, leading to a restricted possibility of observing the PD process. The link between the process and observable quantities like Phase Resolved PD (PRPD) patterns is derived. With this new approach a much easier interpretation and analysis of PD patterns is possible. Features of the patterns can be derived analytically from the process parameters. Thus the present investigation forms a further step towards a better understanding of PD processes.