Realtime setpoint optimization with time-varying extremum seeking for vapor compression systems
Realtime setpoint optimization with time-varying extremum seeking for vapor compression systems
复制标题
通过时变极值寻找蒸汽压缩系统的实时设定点优化
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Guay
中科院分区：
文献类型：
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作者：
D. Burns;Walter K. Weiss;M. Guay
在许多变速蒸汽压缩机的控制器结构公式中，由于蒸发器过热度与循环效率的相关性，通常选择蒸发器过热度作为调节变量。此外，过热温度设定值在广泛的工作条件范围内方便地作为一个恒定值。然而，对过热度的直接测量并不总是可用的，而且对过热度的估计具有有限的鲁棒性。因此，需要在蒸汽压缩机的控制中识别与效率相关的交替信号。在本文中，我们考虑了一种无模型极值搜索算法，该算法通过调整压缩机排气温度设定值来优化能效。虽然基于微扰的极值搜索方法已经被发现了一段时间，但它们的收敛速度很慢，这是一个在应用中与热系统相关的长时间常数所强调的问题。我们的方法采用了一种新的算法（时变极值搜索），具有显著的更快和更可靠的收敛性。特别是，我们使用从无模型时变极值搜索算法中选择的设定值来调节压缩机排气温度。我们表明，压缩机排气温度和功耗之间的关系是凸的（这类实时优化的要求），并使用时变极值寻求将这些设定值驱动到最小功率的值。结果与传统的基于微扰的极值搜索方法进行了比较。实验表明，在一组特定的实验条件下，放电温度从72°C优化到62°C，功耗从525 W降低到450 W，导致观察到的性能系数（COP）增加14%。
In many formulations of controller architectures for variable-speed vapor compression machines, evaporator superheat temperature is commonly selected as a regulated variable due to its correlation with cycle efficiency. Further, the superheat temperature setpoint is conveniently taken as a constant value over the wide range of operating conditions. However, direct measurement of superheat is not always available, and estimates of superheat have limited robustness. Therefore identifying alternate signals in the control of vapor compression machines that correlate to efficiency is desired. In this paper, we consider a model-free extremum seeking algorithm that adjusts compressor discharge temperature setpoints in order to optimize energy efficiency. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates-a problem emphasized in application by the long time constants associated with thermal systems. Our method uses a new algorithm (time-varying extremum seeking), which has dramatically faster and more reliable convergence properties. In particular, we regulate the compressor discharge temperature using setpoints selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of realtime optimization), and use time-varying extremum seeking to drive these setpoints to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Experiments are performed demonstrating discharge temperature optimization from 72°C to 62°C for a particular set of experimental conditions where the power consumption is decreased from 525 W to 450 W, resulting in an increase in observed coefficient of performance (COP) of 14%.