Insulator Recognition And Fault Detection Based On Fully Convolutional Neural Network

The insulator is exposed to the external environment for a long time,so it is easy to be damaged,missing piece and other faults.If it is not repaired or replaced in time,it is very unfavorable to the stable operation of the power system.However,the traditional manual inspection is not only inefficient,vulnerable to subjective impact,but also waste of manpower.So at present,UAV cruise and image processing technology are mostly used to identify insulator fault.In order to improve the accuracy of insulator fault detection,this article mainly did the following work:In order to improve image contrast and image quality,this paper proposes a segmentation algorithm of full convolution neural network based on watershed correction.First,the full convolution neural network is used to deal with the feature information to find the corresponding category of each pixel,to achieve the classification effect of the pixel set,and to preliminarily segment the image.Secondly,the watershed algorithm is used to locate the edge effectively,and the edge of the segmentation result of full convolution neural network is modified.Finally,the watershed segmentation results and the full convolution neural network segmentation results are superposed to obtain more accurate image segmentation results.Through the experimental comparison and the quantitative analysis of the objective evaluation index,the results show that the algorithm proposed in this paper can accurately segment the insulator region.At the same time,it solves the problem of the traditional segmentation algorithm's wrong segmentation,and provides reliable support for the subsequent fault identification and classification.In order to prevent the single speed of optimization due to the influence of the step size of Ba algorithm,which is easy to fall into the local optimal solution prematurely,the BA algorithm based on cloud theory is proposed.Firstly,the normal cloud model is used to generate the adaptive weight,which is applied to the speed update formula of Ba to meet the needs of local search and global search.Then the improved Ba optimization algorithm is used to optimize the penalty factor and kernelparameters of SVM,and the fault identification model of insulator based on SVM algorithm of improved Ba optimization algorithm is established.Compared with traditional SVM,BP neural network and convolution neural network,the model has higher accuracy for insulator fault identification with small sample data,which verifies the feasibility and effectiveness of the model.The image processing software is designed by MATLAB simulation platform.In this software,the collected image can be denoised,filtered,enhanced and other basic functions.At the same time,it also has the function of insulator fault identification.