PD pattern recognition using S transform and two-directional two-dimensional principal component analysis

Abstract

This paper presents a feature extraction algorithm combining S transform (ST) and two-directional two-dimensional principal component analysis ((2D) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA is applied to compress the ST amplitude(STA) matrices to extract various feature vectors with different (d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) 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 (d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) 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.