Research On Two Stage Insulator Fault Detection Method Based On Collaborative Deep Learning

As an important insulation device in power system,insulators play an important role in mechanical support and preventing current from returning to ground in overhead transmission line,Affected by natural factors such as disaster,temperature and humidity,the insulator is prone to detonation cylinder,metal gear fall off and other physical faults,once the insulator fails,it will directly threaten the transmission stability of the whole power system,and cause serious security hidden danger and economic losses,therefore,in the power system of insulator state monitoring has been closely concerned.With the rapid development of UAV technology,the fault inspection of key electrical equipment in transmission lines based on aerial image has become a research hotspot.Focusing on the deep learning algorithm,this paper studies insulator location and fault detection in transmission lines of aerial images.Aiming at the problem that target detection algorithm is vulnerable to complex background interference,leading to low accuracy,a two-stage insulator fault detection method based on collaborative deep learning is proposed,the specific process is as follows:(1)The first stage,the FCN algorithm is used to preprocess the aerial image.The jump structure is designed to fuse the shallow image features and deep semantic features,and an 8-fold up-sampling insulator segmentation model is constructed,the insulator region image is preliminarily segmented,then the segmented insulator region image and the original image are logically processed to get the insulator image with the background region removed;(2)The second stage,the insulator image with the background region removed is used as the training set data,construct the insulator fault YOLOv3 object detection model,the improved Darknet-53 based on Darknet-19 network is used as the feature extraction network,and combining with the idea of feature pyramid,the insulator fault regions can be marked and classified on three scales of the output tensors.For the mismatch problem of default anchor frame parameters of YOLOv3 network to insulator data set,K-means++ clustering algorithm is used to optimize the anchor boxes parameters of YOLOv3 to further improve the detection accuracy.The experimental results show that the two-stage method based on collaborative deep learning can effectively overcome the interference of complex background,the mean average precision of insulator fault detection is as high as 96.88%,which is 4.65%higher than the original YOLOv3 algorithm.