Research On Energy Efficiency Optimization Of Refrigeration System Based On Seven-order Model

Among all kinds of energy consumption brought by life and production,building energy consumption accounts for a considerable proportion.And in building energy consumption,the proportion of air conditioning energy consumption is about 60%.Therefore,the study on the stable operation and energy efficiency optimization of refrigeration system is of great significance for reducing building power consumption as well as construction of green buildings.At present,most air conditioning systems on the market adopt the principle of compressed refrigeration.In actual operation,air conditioning often works under partial load,where the actual operating efficiency of the system cannot reach the expected value.In order to optimize the energy efficiency of compressed refrigeration system,this thesis studies the mechanism model of compressed refrigeration system,determines a nonlinear PID control algorithm to control the refrigeration system,and uses an improved particle swarm optimization algorithm to optimize the working point of the refrigeration system.Firstly,this thesis analyzes the structure and basic working principle of the compressed refrigeration unit.And the influence of each component in the system and the coupling relationship between them are analyzed.For compressors and expansion valves with fast response,the input-output model is established,and for evaporators and condensers with slow dynamic response,a phase separation zone dynamic model of is established based on the lumped parameter method.At the same time,the seventhorder dynamic model of refrigeration system including refrigeration unit and chilled water cycle is obtained by simplifying system parameters with reasonable assumptions.For the phase separation zone model,the undetermined coefficients of the model are identified by experimental data,and the accuracy of the dynamic model is verified by comparing the experimental data with the simulation data.Secondly,the function and adjustment trend of each parameter of PID control are analyzed.In order to achieve better performance in stability,accuracy and rapidity of refrigeration system control,a nonlinear PID control algorithm is presented,which includes error adjustment,error nonlinearization,gain nonlinearization,control signal calculation,saturator and so on.The simulation results show that the control performance of nonlinear PID is better than that of traditional PID in dynamic model control of refrigeration system.Then,this thesis analyzes the principle,basic structure,advantages and disadvantages of particle swarm optimization(PSO),and the improved methods of PSO are studied from three aspects: parameter adjustment,topology adjustment and mixing with other intelligent algorithms.To improve the ability of PSO for refrigeration system,an improved cooperative particle swarm optimization(CPSO)algorithm was designed.According to the fitness,the particles are divided into two populations: good group and bad group.The two populations adopt different evolutionary strategies of nonlinear adjustment weight so that that can evolve and cooperate with each other.The load change of refrigeration system has the characteristics of long-term cycle and short-term fluctuation.According to this characteristic,the load requirement curve of refrigeration system is simulated.Finally,the energy efficiency optimization structure of refrigeration system is constructed: according to the refrigeration load requirement of the system,PSO is used to optimize the working point,so that the energy consumption index of the system will be optimized.Then PSO is used to adjust the nonlinear PID parameters to optimize the dynamic index of the system at the optimized working point.Experiments were conducted to compare the energy consumption advantages of particle swarm optimization algorithm with that of conventional control algorithm.