Intelligent fault diagnosis of transformer insulating bushings based on improved Mask R-CNN

Abstract

As a major insulation device outside the transformer box， insulating bushing is of great significance to the safe and stable operation of the transformer. In order to improve fault diagnosis efficiency of insulating bushing， an intelligent diagnosis method of transformer insulating bushing fault based on improved Mask R-CNN is proposed in this paper. Firstly， the infrared images including those of insulating bushings are collected and labeled， and a preliminary data set is established in the process of operation and maintenance. In order to remedy the imbalance between positive and negative samples， negative samples are increased by rotating， scaling， clipping and translation， and the GHM loss function is introduced. MobileNetv3 is used as the backbone network of mask R-CNN to meet the requirements of real-time detection. The comparative experiments show that the improved Mask R-CNN acquires a detection speed as high as 216 ms per frame， a fault diagnosis rate of 89.72% and a misreport rate of 6.78%； it can accurately realize the intelligent fault diagnosis of transformer insulating bushings and can be used for automatic inspection and construction of intelligent power stations.

Keywords

• transformer insulating bushing
• infrared image
• improved mask r-cnn
• fault diagnosis