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This paper presents a novel partial-discharge (PD) recognition method based on the extension theory for high-voltage cast-resin current transformers (CRCTs). First, a commercial PD detector is used to measure the three-dimensional (3-D) PD patterns of the high-voltage CRCTs, then three data preprocessing schemes that extract relevant features from the raw 3-D-PD patterns are presented for the proposed PD recognition method. Second, the matter-element models of the PD defect types are built according to PD patterns derived from practical experimental results. Then, the PD defect in a CRCT can be directly identified by degrees of correlation between the tested pattern and the matter-element models which have been built up. To demonstrate the effectiveness of the proposed method, comparative studies using a multilayer neural network and k-means algorithm are conducted on 150 sets of field-test PD patterns of 23-kV CRCTs with rather encouraging results.