A novel image‐orientation feature extraction method for partial discharges. Issue 6 (30th November 2021)

• Record Type: Journal Article
• Authors: Li, Ze; Qian, Yong; Wang, Hui; Zhou, Xiaoli; Sheng, Gehao; Jiang, Xiuchen
• Is Part Of: IET generation, transmission & distribution. Volume 16:Issue 6(2022)
• Page Start: 1139
• Page End: 1150
• Publication Date: 2021-11-30
• Subjects: Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/gtd2.12356
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract: Partial discharge identification is of great significance in monitoring power equipment. Although substantial work has been done in partial discharge pattern recognition, most studies have been performed in noise‐free environments. To improve the accuracy of partial discharge pattern recognition, this paper introduces a partial discharge image feature extraction method based on upright speeded‐up robust features. Each group of phase‐resolved pulse sequence data is converted into grayscale images to construct a sample library of partial discharge defects. Upright speeded‐up robust features is used to extract features in grayscale images. The improved support vector machine is used to achieve the best classification of partial discharge sources. The sensitivity of local features to different phase resolutions and different image resolutions of partial discharge phase‐resolved pulse sequence patterns was investigated, and the optimal phase resolution and image resolution were used to construct phase‐resolved pulse sequence patterns. In addition, the recognition effect of this feature extraction method under different noise conditions was also analysed. The results prove that upright speeded‐up robust features can effectively deal with noisy data. The results of this research can provide references for the storage setting and recognition of three‐dimensional images of on‐site partial discharge data.