Research On Transformer Operation State Monitoring System Based On Infrared Temperature Measurement Technology

The real-time monitoring of the state of electrical equipment is the key to ensure the safe operation of the power grid.The temperature of the transformer is the basic characteristic quantity to monitor whether the transformer is in normal operation,that is,whether the equipment is in good condition can be judged by monitoring the temperature.At present,the traditional transformer temperature measurement method is mainly based on regular temperature measurement and supplemented by preset temperature measurement elements,which can not meet the real-time temperature measurement function.In order to solve the above problems,this thesis realizes on-line temperature measurement of transformer based on infrared temperature measurement technology.On this basis,the idea of cloud platform is introduced to deploy the online monitoring system of transformer temperature to cloud platform,and then the real-time temperature state is realized monitor.The main contents of this thesis are as follows:Firstly,the hardware terminal uses IRTP-300 L infrared sensor and STM32 chip to collect the transformer temperature in real time,and the XBEE PRO S2 C wireless data transmission module constructs a ZigBee network,which includes several terminal nodes connected to the hardware collection and a coordinator node connected to the upper computer.Use the coordinator to collect and transmit terminal data to the host computer.The host computer analyzes and processes the collected data,converts the data frame into a temperature that is easy to identify,and submits it to the cloud server layer.Secondly,for the server layer of the Web cloud platform,the B/S architecture is used for data interaction design,My SQL stores data,and combines PHP,Apache,Think PHP and MVC frameworks to develop the system to achieve user management,real-time temperature display,historical temperature query and Temperature status supervision and other functions.Perform software and hardware tests on the overall system to verify whether the system meets performance requirements.Finally,in view of the problem that the transformer temperature is easily interfered by many factors,which may lead to inaccurate temperature measurement results,in this thesis,the Bayesian network model based on pair copula is used to analyze the influence factors of transformer temperature measurement and determine the weight.Through multiple linear regression models,the transformer temperature is predicted by the influence factor,and the result proves that the predicted result of the model fits the actual measured value and meets the accuracy requirements of the system.