Application of Enhanced Whale Adaptive Threshold Noise Reduction Method in Transformer Ultrasonic 3D Imaging Detection. Issue 5 (October 2020)

• Record Type: Journal Article
• Authors: Du, Haoyang; Li, Yimu; Liu, Wanting
• Is Part Of: Journal of physics. Volume 1624:Issue 5(2020)
• Publication Date: 2020-10
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1624/5/052001
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Most of the detection methods for transformer winding faults are off-line detection, which have to know the internal parameters of the transformer including the winding radius and the position of the winding center point It is only suitable for radial deformation, which has great limitations. Therefore, based on the principle of the ultrasonic wave propagation character, this paper designs a vertical 3D imaging detection system, which can visually display the 3D pattern of the winding deformation. This system can quantitatively and qualitatively detect the transformer and is suitable for various shapes of transformer winding deformation. An improved whale adaptive threshold optimization noisee reduction algorithm has been improved in this paper based on wavelet decomposition for the problem that echo is easily interfered by noise in ultrasonic detection. After testing, most of the interference noise can be filtered, so that the transformer can be more intuitive around the three-dimensional state diagram, which can better distinguish the fault location.