Research On Three-phase Power Self-balancing Technology Of Multi-agent Electric Heating Control System

With the development of national power,the implementation of new energy and low-carbon environmental protection policies,intelligent electric heating gradually replaces traditional heating methods.In recent years,the application of electric heating in schools,communities,and public places has become more and more popular.The intelligent electric heating system will be accompanied by three-phase power imbalance,large power-on startup current,large losses,and requirements for operation and maintenance during the heating process.High and automatic control problems.In order to solve these problems and improve the power supply quality and system stability,this paper studies the electric heating three-phase balance control system based on predictive expert control.The main research work completed and the results obtained are as follows:According to the working attributes and heating demand of the room,this paper firstly proposes a method for the thermal load classification of the electric heating system;since the temperature change of electric heating is affected by various factors,it shows the characteristics of non-linearity and time lag.Therefore,the electric heating system based on the load classification The heating temperature control method is very important.For this reason,a Smith-fuzzy PID temperature control algorithm is proposed to realize the stable temperature change within a certain range.The MATLAB simulation analysis shows that the Smith-fuzzy PID temperature control effect is better than PID,fuzzy PID control has good stability,fast response,and small steady-state error.In order to improve the reliability of electric heating system operation,this paper uses convolutional neural network algorithm to diagnose faults of heating equipment,identify fault types,and then adopt coordinated control methods to ensure The basic heating requirements under fault conditions have been solved.Establish a temperature linear regression prediction model based on the collected data,predict the switching time of the electric heating equipment and sort the time from large to small,so as to optimize the position of the electric heater that can be switched on and off;then use the expert control strategy to establish a database,Formulate expert control rules,design the electric heating inference engine,and realize that the electric heating system solves the three-phase power self-balance problem on the load side.This method is different from the traditional compensation method and commutation balance method.It relies on the characteristics of a multi-agent Internet of Things system of the electric heating control system itself.There is no need to add a detection device.Because it balances itself on the load side,it does not need to be An additional balancing device is added on the power supply side,thereby reducing losses and costs,and improving the quality of power supply.When the electric heating equipment fails to provide heating,the temperature of the heating room will decrease and the load level will decrease.In order to increase the temperature of the faulty unheated room without exceeding the set comfortable temperature,a heat balance temperature model of the faulty unheated room is established based on the heat transfer principle,the main factors affecting the temperature of the faulty unheated room are analyzed,and coordinated regulation is issued through the control layer For non-heated room temperature instructions,the on-site level adjusts the temperature to solve the basic heating demand in the fault state.Make heating more intelligent and more energy-efficient.