Partial discharge signals from a partial discharge detector (fig 1, 130) coupled to high voltage power machinery (fig 1, 100) are automatically identified using a neural network. The partial discharge identifying method includes: plotting a graph of successive discharges, D n: D n-1, that is phase of a detected discharge against phase of the preceding detected discharge; transforming the graph; extracting a phase dependant summation and a phase independent summation; and inputting both of these to a neural network engine. The phase independent summation may be compared with phase dependant summation after phase shifting the phase dependant summation by 120 degrees or 240 degrees or other phases generated by training of the neural network, and may involve multiplication, and integration of the result. The result of such phase shifting may further be cross correlated with reference phase dependent summation and reference phase independent summation and then reasoning that where the correlation is a maximum is a phase of power applied to a point where a discharge signal is generated. Partial discharges can be identified without detecting phase information of the applied power. Partial discharges may be detected in three-phase machinery, such as transformers, gas insulated switchgear and power transmission lines, and power cables, and may be measured with a portable device.
Input vector formation method of neural networks for auto-identification of partial discharge source