基于热泵热量传递系数预测的PV/T-SAHP系统建模  

PV/T-SAHP System Modeling Based on Heat Transfer Coefficient Prediction of Heat Pump

作　　者：李畸勇[1,2] 周兴操 王纪港 LI Ji-yong;ZHOU Xing-cao;WANG Ji-gang(College of Electrical Engineering,Guangxi University,Nanning Guangxi 530004,China;Guangxi Key Laboratory of Power System Optimization and Energy Technology,Nanning Guangxi 530004,China)

机构地区：[1]广西大学电气工程学院,广西南宁530004 [2]广西电力系统最优化与节能技术重点实验室,广西南宁010020

出　　处：《计算机仿真》2021年第11期424-428,468,共6页Computer Simulation

基　　金：广西自然科学基金项目(2014GXNSFAA118372);广西研究生教育创新计划资助项目(YCSW2017026)。

摘　　要：针对现有光伏光热一体化热泵(PV/T-SAHP)系统模型构建难度大且求解时间长,提出一种基于Elman神经网络和集总参数法的建模方法。根据历史数据建立热泵热量传递系数的Elman神经网络预测模型来对热泵进行简化处理,利用集总参数法构建系统各部件的热量平衡方程,从而实现了PV/T-SAHP系统的动态建模。实验结果表明,上述模型求解速度快,预测精度高,为PV/T-SAHP系统的优化控制奠定了基础。Aiming at the difficulty and long solving time in modeling the existing PV/T-SAHP system, a modeling method based on Elman neural network and lumped parameter method is proposed. Firstly, an Elman neural network prediction model of the heat transfer coefficient of the heat pump was established based on historical data to simplify the heat pump. Then, heat balance equations of all components of the system were established by lumped parameter method, thus realizing dynamic modeling of PV/T-SAHP system. The experimental results show that the above model has fast solution speed and high prediction accuracy, which lays a foundation for optimal control of the PV/T-SAHP system.

关 键 词：光伏光热 神经网络 集总参数法 动态建模 

分 类 号：TM391.9[电气工程—电机]