机器学习方法的CO_(2)并行压缩系统最优控制

Optimal Control of CO_(2) Parallel Compression System Based on Machine Learning

为研究一种高效的跨临界CO_(2)并行压缩系统的控制方法,本文借助GT-SUITE仿真软件,建立了跨临界CO_(2)并行压缩系统的动态仿真模型,基于仿真得到的系统性能数据集,建立并对比了二阶多项式模型和神经网络模型的系统性能预测模型,并基于神经网络模型开发了跨临界CO_(2)并行压缩系统的模型预测控制器,研究控制器对系统控制稳定性、高效性、实时控制的性能。结果表明:在模型预测控制器作用下,不同制冷工况在150 s内系统能达到稳定运行状态;对比定值控制,采用模型预测控制的系统性能最大提升13.3%;仿真验证了提出的模型预测控制策略对于CO_(2)并行压缩系统实时控制性能优化的可行性,在给定工况下相比对定值控制整体提升性能7.3%。

A dynamic simulation model of a transcritical CO_(2) parallel compression system was established using GT-SUITE simulation software to explore an efficient control method for the transcritical CO_(2) parallel compression system.Based on the system performance dataset obtained by the simulation,the second-order polynomial model and the neural network model were established and compared as the system performance prediction models.Based on the neural network model,a model predictive controller for the transcritical CO_(2) parallel compression system was developed,and the performance of the controller in terms of the stability,high efficiency,and real-time control of the system was studied.The results show that under the action of the model predictive controller,the system can reach a stable operating state within 150 s for different cooling conditions.The performance of the system using model predictive control is 13.3%higher than that using fixed value control.The simulation verifies that the proposed model predictive control strategy is feasible and optimizes the real-time control performance of the CO_(2) parallel compression system;the overall performance is improved by 7.3%compared with the fixed value control under the given working conditions.The control strategy proposed in this study is significant for the use of machine learning methods in designing system controllers to improve the performance of heat pump air-conditioning systems.