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In this study, the effect of noise elimination and voltage phase lag on the partial discharge pattern recognition was studied to improve the accuracy of the partial discharge diagnosis of the XLPE transmission cable system. Recognition rates were compared by applying Neural Network and SVM techniques using statistical feature values extracted from physical quantities in PRPDA data such as discharge numbers and discharge amounts according to the voltage phase, which were measured through the commercial partial discharge diagnostic system for four different types of partial discharge models. As a result, it was found that the minimum noise elimination method showed high pattern recognition rate because it relatively preserved the partial discharge information, even though the background noise would not be clearly eliminated. In addition, for the effect of the voltage phase lag, the neural network did not show any meaningful effect, whereas SVM showed significantly lowered recognition rate.
