Research on intervention PID control of VAV terminal based on LabVIEW

被引:9
作者
Cao, Shuanghua [1 ]
Zhao, Weichao [1 ]
Zhu, Anxiong [1 ]
机构
[1] Univ Shanghai Sci & Technol, Sch Environm & Architecture, Shanghai 200093, Peoples R China
关键词
Terminal control; PID control; Intervention identification algorithm; LabVIEW; ENERGY-CONSUMPTION; SIMULATION; DESIGN;
D O I
10.1016/j.csite.2023.103002
中图分类号
O414.1 [热力学];
学科分类号
摘要
In recent years, the research on the control method of variable air volume (VAV) has been the focus of VAV air-conditioning research. Single terminal PID control is prone to overshoot in the system when the temperature difference is large. Aiming at the deficiency of single terminal PID control at the air conditioning terminal, a control method of coupling intervention control and PID control is proposed. This thesis used LabVIEW simulation and experiment to analyze the performance of terminal intervention PID control. According to the experiment, the change of room temperature after adding 1500W heat load in the room was recorded by thermocouple, and the step response of room temperature was obtained. The LabVIEW simulation program of PID and intervention PID control is established according to the characteristics of room step response. The reliability of LabVIEW simulation program was verified by experimental data, respectively. The intervention PID control and conventional PID control are simulated by LabVIEW simulation program under 1000W and 1500w load to compare their stability, anti-interference and robustness. The experiments and simulation results show that the control strategy using the intervention recognition algorithm nested with the PID algorithm has good dynamic perfor-mance, robustness, and adaptability.
引用
收藏
页数:14
相关论文
共 42 条
[41]   An incremental-PID-controlled particle swarm optimization algorithm for EEG-data-based estimation of operator functional state [J].
Zhang, Jianhua ;
Yang, Shaozeng .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2014, 14 :272-284
[42]   Vector field-based support vector regression for building energy consumption prediction [J].
Zhong, Hai ;
Wang, Jiajun ;
Jia, Hongjie ;
Mu, Yunfei ;
Lv, Shilei .
APPLIED ENERGY, 2019, 242 :403-414