No abstract

Low-temperature composite insulation is commonly applied in high-temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined variational mode decomposition with multi-scale entropy and image feature is proposed. Based on the simulated test platform, original and noisy signals of three typical PD defects were obtained and decomposed. Accordingly, relative moments and grayscale co-occurrence matrix were employed for feature extraction by Kmodal component diagram. Afterwards, new PD feature vectors were obtained by dimension reduction. Finally, effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour. Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.