A generalized model for partial discharge processes based on a stochastic process approach

A general framework for the physical description of partial discharge (PD) processes is presented that holds for different types of PD causing defects. A PD process is treated as a stochastic process consisting of short duration discharges (point-like in time) and charge carrier drift/recombination intervals between these discharges. It is determined by few basic physical parameters and, in a stochastic process framework, can be described in a closed form by a master equation. Since usually only the fast discharges can be measured as PD signals, a restricted possibility of observing a PD process results. The link between the stochastic process and observable quantities is derived.

A specific type of measurements is reported, the so-called phase-resolved partial discharge (PRPD) patterns. Here the total charge transferred during a discharge and the time or alternating current phase at which the discharge occurs are measured. Thus each discharge event is described by the two quantities, charge and phase angle. The modelling of the observation process is explicitly derived for this case. However, the used method can easily be generalized to other types of PD measurements.

The proposed approach yields new possibilities for the interpretation and analysis of PD patterns. Features of PD patterns can be derived analytically from the process parameters. Conversely, quantitative information about the discharge physics can be gained from measured patterns. Some limiting cases of model parameter values leading to typical pattern features are discussed explicitly.

Examples are presented that demonstrate the applicability of the model for three different discharge types (internal discharge in a gas-filled void, surface discharge in oil, corona in air).