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Partial discharge pattern recognition of insulation models of power transformers

To analyze the statistical characteristics of partial discharges (PDs) in power transformers, 7 types of experimental models simulating PD in transformers and 3 types of models simulating interfering PD in air are designed and model experiments are conducted in a screened room. Using a digital measuring device with the sampling rate of 50 kHz, the quantity phase information of PD pulse current in models is obtained after the signal is processed by a peak-holding hardware. The PD features are extracted using the 3D pattern chart and then a group of three-layer back-propagation ANNs (artificial neural networks) is used to recognize the PD patterns. The investigation shows that ANN has enough ability to recognize different PD in oil-paper insulation of power transformers and PD interference from air can be recognized and eliminated.