Research On Capacitor Abnormal Condition Monitoring Technology Based On Edge Computing

Even though the fact that capacitor image samples are sent to the cloud for centralized processing in the power grid fault monitoring network,the energy consumption of each storage node will be greatly increased,which will cause a greater burden on the whole storage network.Therefore,it is very important to carry out the research of edge Intelligent Computing Technology in combination with the method of artificial intelligence image analysis and processing for the requirements of substation monitoring scene and intelligent monitoring recognition.To begin with,this paper analyzes and summarizes the research schemes and technical routes of target detection technology and edge computing technology at home and abroad.At the same time,based on the existing edge computing equipment,it discusses the possibility of model building and edge deployment,and plans two edge deployment routes to pursue the average accuracy of the model and the lightweight and speed of the model.Secondly,aiming at the problem that the number of samples is extremely limited at present,this paper studies the schemes and principles of various data enhancement methods,makes clear the advantages and disadvantages and characteristics of various schemes,selects and builds an appropriate deep learning network to complete the data enhancement process of small capacitor samples.Similarly important,several more suitable neural network models for target recognition and classification are studied,and the better ones are selected for construction and training with performance as the target.At the same time,the training results before and after data enhancement are analyzed to confirm the necessity of data enhancement process.At the same time,aiming at the limited resources of some edge computing devices,the lightweight neural network model is trained.At the same time,the compression algorithm is used to complete the compression process of the model.Last but not the least,the deployment scheme of target detection and classification model for edge computing platform is designed,the image acquisition process is activated,the edge deployment process of two models based on edge computing device is completed,and the deployment results and indicators are compared and analyzed.