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This paper presents a feature extraction algorithm combining S transform (ST) and two-directional two-dimensional principal component analysis ((2D)2 PCA) for partial discharge (PD) pattern recognition. S transform (ST) is firstly employed to obtain a time-frequency representation of the recorded UHF signals. Then, (2D)2 PCA is applied to compress the ST amplitude(STA) matrices to extract various feature vectors with different (d1, d2) combinations, i.e. (5, 5), (5, 10), (10, 5) and (10, 10). The extracted features are examined by both PSO-SVM classifier and BPNN. Experimental results show that the classification accuracies by PSO-SVM are all higher than that by BPNN under four circumstances of (d1, d2) combinations. The success rates of the PSO-SVM with the four feature vectors are above 94% in all cases. It can be found that the proposed feature extraction and classification algorithm can be effectively applied to PD pattern recognition.