Research On Control Strategy Of Solar-phase Change Heat Storage Fresh Air Heating System Based On System Identification

With the reduction of non-renewable energy, substitution of low-carbon and green energy for traditional energy has become the worldwide concern. Moreover, with the progress of science and development of civilization, higher requirements are put forward for the comfortable living environment. How to balance the relationship between low energy consumption and high living standard becomes the focus of researchers. Nowadays, fresh air heating system with scholar energy and phase change thermal storage provides an effective solution for the above contradiction in indoor air temperature control field.In such a fresh air heating system, issues of building energy conservation, new energy utilization, energy storage, and ventilation requirements are all considered. In addition, combination of techniques such as solar energy, LHTS, and modern control theory is proposed to solve the indoor heating and ventilation problems. In the fresh air heating system, the following functions are realized:real-time monitoring of various condition changes; automatic shifts among air heating, energy storage and energy release according to occupants requirements; automatic shifts among different modes to achieve an ideal living environment with high air quality and comfortable temperature. From the review of relevant domestic and international literatures, studies based on solar energy and LHTS fresh air heating system characteristics, such as energy change, operation mechanism, system modeling, and intelligent control, are rare, which are of great research value but with weak study basis.The main achievements of this dissertation are as follows:1. From the view point of thermodynamics, thermal conductivity process and work principle of the system are analyzed. The system is divided into two subsystems from the view point of system function:thermal conductivity system and test and control system. Then the following contents are introduced:design of thermal conductivity system; determination of parameters for the test and control system hardware platform, selection of the test and control devices, design of the test and control network; realization of the data collection, data processing, data monitoring and output control functions.2. Physical characteristics of solar collector, heat storage tank, fan coil units are studied. Through mathematical model analysis of the physical characteristics, thermal conductivity laws of different parts of the system are obtained. Meanwhile, the simulation objectives for control strategies are also studied, among which, fan coil unit is the main controlled objective. Thus, the mathematical model of fan coil unit is simulated for study and experimental data are used to validate the model.3. Control requirements of the solar collector part are analyzed. Then two control modes are proposed according to different outdoor light conditions. Then control requirements of heat storage and release and fresh air heating parts are analyzed. Five control modes are introduced for different heat conducting amount, which meet the requirements of fresh air heating and energy conservation. Subsequently, conditions for mode shifts are discussed, and control mode shift strategies for the whole system are designed.4. Methods of nonlinear system identification are briefly introduced. Through comparison of identification methods, BP neural network is selected as the system identification method. After analysis of BP neural network structure, mathematical algorithm and training process, BP network identification process is determined. During the system identification, neural network structure is chosen and the different layers of BP network are set. After determining the network training algorithm and improved algorithm, BP network identification models under different control modes are obtained. Then simulation and validation of the models are carried out.5. Control theories based on models are analyzed and compared. Then the MPC (model predictive control) is introduced whose control properties are better. MPC theories based on both linear and nonlinear models are analyzed, and MPC mathematical description and algorithm characteristics are studied. In view of the system features, a neural network MPC controller is developed. Invariable temperature tests and follow-up tests are simulated by various control modes. Simulation results are analyzed and show that the control strategies are feasible.