Abstract: To reduce the running energy consumption of the central air conditioner and stabilize and control the return temperature of chilled water effectively, an optimal control method of return water temperature was proposed, and the actual load demand was judged according to the deviation between the measured value of return water temperature and the set value. Firstly, the inertia weight of Particle Swarm Optimization (PSO) algorithm was made decline exponentially which made updating speed of particles match each stage of optimization process. Then, aiming at uncertain disturbance of parameters of the model, the thoughts of Artificial Immune (AI) algorithm were introduced in Particle Swarm Optimization (PSO) algorithm to form AI-PSO algorithm which could expand the diversity of particles and enforce their ability to get rid of local optimum. Finally, three parameters of Proportional Integral Differential (PID) controller were optimized with AI-PSO algorithm, and through this controller, the frequency of chilled water pump was adjusted to make return water temperature steady near set value. The experimental results show that the proposed strategy can reduce operating frequency of chilled water pump more effectively while meeting indoor load demand, in addition, energy saving effect and control quality are much better.

Key words: return water temperature, Proportional Integral Differential (PID) controller, Particle Swarm Optimization (PSO) algorithm, Artificial Immune (AI) algorithm, energy conservation

摘要： 为了降低中央空调的运行能耗并对冷冻水回水温度进行稳定而有效的控制，提出了一种冷冻水回水温度优化控制方法，根据回水温度测量值与设定值间的偏差判断室内实际负荷需求。首先，对粒子群优化（PSO）算法中的惯性权重采用呈指数形式下降的策略，使微粒更新速率能够适配寻优过程的各个阶段；然后，针对模型参数存在不确定摄动的问题，引入人工免疫（AI）思想形成免疫粒子群算法，从而拓展微粒的多样性，增强其摆脱局部最优值的能力；最后，据此优化比例积分微分（PID）控制器的3个参数，并通过该控制器调节冷冻水泵的频率，将实际回水温度保持在设定值附近。实验结果表明，采用该控制策略在满足室内负荷需求的前提下能够更有效地降低冷冻水泵的运行频率，节能效果与控制质量更佳。

关键词: 回水温度, 比例积分微分控制器, 粒子群优化算法, 人工免疫算法, 节能