Research On Online Condition Monitoring And Overhaul Of Substation Equipment In Nuclear Power Plants

As the capacity and scale of power systems increase,substations serve as hubs for energy distribution,and their equipment failures will have a huge impact on power distribution,resulting in huge economic losses.How to better adapt to the current operational requirements of the power system requires a more intelligent online monitoring and overhaul system.With the control of production costs by power companies and the reform of national power equipment.The more intelligent online maintenance of substation equipment has gradually become the focus of the relationship,and it has increasingly attracted the attention of the field of electricity.At present,there are two problems in the traditional online monitoring of substation equipment: First,the early warning methods are mostly based on the threshold of monitoring equipment or traditional machine learning algorithms,which are not well applied to historical data,and are mostly based on single monitoring data.Modeling.Second,there is no application management system that integrates well-tested substation equipment.Starting from the actual situation of the electric power transportation system,this paper mainly realizes two effective methods to solve the above problems: First,combine the monitoring and early warning of the substation equipment with the maintenance,starting from the transformer of the substation equipment,according to the relevant monitoring technology of the transformer(transformer)The data generated by oil chromatography,near-infrared spectroscopy,and passive infinite temperature measurement technology has innovated a model input method combining three kinds of monitoring data.Based on the input method,an early warning model of LSTM+Attention mechanism is proposed.Effectively realize the monitoring and early warning of substation equipment,which can combine the historical data of substation equipment with different monitoring data,and obtain the advantages of traditional methods in actual testing.The second is to apply this algorithm to actual industrial production.Finally,an online monitoring and overhaul system based on substation equipment is designed and developed.The system mainly starts from the principle of online monitoring and state maintenance of substation equipment,and realizes the intelligent monitoring and overhaul early warning system combined with deep learning model.The mainfunctions of the system include monitoring device management,operation monitoring and comprehensive display.It can effectively perform online monitoring and status warning on substation equipment.It greatly improves the intelligent degree of operation and management of substation equipment,saves the cost of traditional manual monitoring,effectively prevents the economic loss caused by substation equipment failure,and has far-reaching significance for promoting the construction of smart grid.