数据中心制冷系统非线性模型预测控制

数据中心制冷系统具有非线性、强耦合和大滞后特性,目前常用的PID方法无法实现系统整体能效提升,而现有非线性优化算法计算量大,不易工程实现.鉴于此,提出一种数据中心制冷系统模型预测控制策略,上层优化层设计预测控制器,其目标为在满足制冷要求的前提下降低系统能耗,优化层采用神经网络作为反馈控制器,将系统整体优化目标函数作为神经网络控制器优化性能指标,结合变分法与随机梯度下降法,通过滚动优化求取下层各回路被控变量最优设定值,算法占用存储区适中、计算量小;下层现场控制层通过实时控制使各回路被控变量跟踪最优设定值,可以在不破坏原有现场控制系统的情况下实现性能优化.构建Trnsys-Matlab联合仿真平台,针对系统夏季、过渡季和冬季的控制策略进行仿真实验.结果表明,所提出控制策略能够在满足数据中心安全运行的前提下,实现系统整体能效提升,且具有良好的鲁棒性.

Nonlinear model predictive control for data center cooling systems

Data center cooling system has nonlinear, strong coupling and large hysteresis characteristics. The commonly used PID method cannot achieve overall system energy efficiency improvement, and the existing nonlinear system optimization algorithms are computationally intensive and not easy to implement. This paper proposes a model predictive control strategy for the data center cooling system. The upper optimization layer designs a predictive control strategy to reduce energy consumption on the premise of meeting the cooling load of the IT servers. A neural network is used as the feedback controller, and the optimization cost function of the system is used as the performance index of the neural network controller, and combining the variational method and stochastic gradient descent method to perform online receding horizon optimization to obtain the optimal set values of the controlled variables for each loop in the lower layer. The optimization algorithm occupies moderate storage space and small computation. The lower field control layer makes the controlled variables track the optimal set value through real-time control, which can realize optimization without destroying the original field control system. A Trnsys-Matlab simulation platform is constructed and simulation experiments are conducted for summer, transition season and winter conditions. Experimental results show that the proposed control strategy can achieve energy efficiency improvement with good robustness while meeting the premise of safe operation of the data center.