基于关联规则的复杂系统稳态运行参数优化

Steady-state Optimization of Complex System Operating Parameters Based on Association Rules

针对系统运行过程中,工况切换可能导致系统运行参数目标值的跃变,进而引发系统运行不稳定的问题,本文提出一种基于关联规则的变工况运行参数稳态优化方法,改进了原有的关联规则挖掘策略,将历史工况作为变工况关联规则挖掘的输入条件之一,以系统运行状态稳定为约束,优化稳态运行参数目标值,并讨论了变工况下系统的准稳态运行条件。以某商场冷水机组为应用案例,在典型工况划分的基础上,采用Apriori算法挖掘冷水机组准稳态运行条件下的主要运行参数与性能指标之间的关联关系并进行优化。仿真结果表明:与原运行方式相比,冷水机组运行综合稳定性在过渡季和夏季分别提高了50.4%和62.7%,能耗分别降低了14.48%和16.5%。

To address the problem of unstable system operation caused by the jump of the target value of system operation parameters in the process of working condition switching,a steady-state optimization method of variable working condition operation parameters is proposed based on association rules.This method improves the original association rule mining strategy.The historical condition is regarded as one of the input conditions of association rule mining under variable working conditions and the steady-state operation parameters were optimized under the constraint of system stability.The quasi-steady-state operation conditions of the system under variable conditions were discussed.A chiller system of a shopping mall was chosen as the study object.Based on the division of typical working conditions,the Apriori algorithm was used to determine the relationship between the main operating parameters and performance indices under the quasi-steady operating conditions of chillers and the parameters were optimized.The simulation results showed that the comprehensive operation stability of the chiller increased by 50.4%and 62.7%in the transition season and summer,respectively.The energy consumption reduced by 14.48%and 16.5%in the transition season and summer,respectively,compared to the original operation mode.