Research And Development Of Voltage Monitoring And Low Voltage Early Warning System Based On Internet Of Things

Low voltage of power grid is not only the main reason for customer complaints,but also an important factor restricting customers’ electricity demand.In order to control low voltage,power grid companies have taken measures such as optimizing power grid structure and regularly checking equipment,but it is still difficult to avoid low voltage phenomenon in the time period of concentrated electricity consumption.Among them,the key problem is that the traditional means are in a passive position in the governance of low voltage,and we need to wait for the occurrence of low voltage phenomenon or user complaints,before the power supply company can govern according to the situation.Therefore,it is urgent to need for a voltage monitoring and low voltage prediction system to break the traditional barriers,the main function is to monitor the voltage and low voltage prediction and early warning,in order to achieve the purpose of preventing low voltage in advance and taking measures to prevent the occurrence of low voltage.The paper developed a set of voltage monitoring and low voltage early warning system based on the Internet of Things,which mainly includes hardware circuit design,modeling and algorithm and software design.Hardware circuit design includes acquisition circuit design,communication circuit design,power supply circuit design and memory module design.The acquisition circuit obtains real-time data from mainand processes it to the system to ensure its accuracy;the communication module includes Wi Fi module and GPRS module for different monitoring environments;the power module designs 3.3V and5 V power modules to ensure the stable operation of the circuit;save data to lose data due to subsequent work.Software includes back-end function design,front-end module design,and cloud platform function design.The back-end function design is mainly for each logic control part of the back-end,including database connection,data encryption logic,prediction algorithm module,data display module,log function,alarm module;the front-end part is mainly interface display and data reception,including main interface,equipment management,alarm management,log query,permission management,etc.The cloud platform function mainly realizes the device data uploading to the cloud platform,connects the data with the background client through the cloud platform,and stores the data into the database for the use of the system.The algorithm design is mainly the prediction model and the algorithm design.Based on the characteristics of the data used,the intelligent algorithm combining the triple exponential smoothing model and the model of genetic algorithm is designed,and the advantages and disadvantages of traditional algorithms and the characteristics of other algorithms are compared and analyzed.The genetic algorithm is mainly used to optimize the main parameters of the cubic exponential smoothing model,analyze the correlation of the prediction curves and the actual data curves,and discuss the prediction results of several different cubic exponential models.The results show that the fusion algorithm performs well in both the prediction accuracy and stability.After the completion of the system development,the system was tested.and the misreport rate and misreport rate of 0 since 2 months were 0,which is expected to have good application value and application promotion prospect.