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Virtual sensor-assisted in situ sensor calibration in operational HVAC systems

机译:虚拟传感器 - 在操作HVAC系统中辅助原位传感器校准

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摘要

A sensing environment is important for the reliable control and performance of operational heating, ventilation, and air-conditioning systems. Virtual in situ sensor calibration (VIC) has been developed to estimate various sensor errors, especially systematic errors caused by working environments, to correct erroneous measurements in the field. However, it is difficult to apply VIC to operational systems, because key sensors for the VIC formulation are not usually installed in real systems. This makes VIC impossible or the problem difficult to determine accurately. To improve its applicability and performance by handling this practical challenge, an advanced in situ sensor calibration method that uses virtual sensors for replacement and backup purposes is proposed. The virtual sensors for replacement make it possible to construct the calibration formulation, even with a limited number of physical sensors. The backup virtual sensors can reinforce the formulation mathematically, thus improving calibration accuracy. In a case study for a typical air-handling unit, the suggested virtual sensor-assisted VIC was constructed based on the model-driven virtual sensors, and it showed excellent calibration accuracies for multiple simultaneous sensor errors by adding data-driven virtual sensors. In addition, with the various virtual sensors developed, the use of multiple operational datasets was effective in calibrating simultaneous errors. The calibration error decreased from 72.7% to 5.2% using the virtual sensors.
机译:传感环境对于操作加热,通风和空调系统的可靠控制和性能非常重要。已经开发了虚拟地原位传感器校准(VIC)以估计各种传感器误差,特别是由工作环境引起的系统错误,以纠正现场错误的测量。但是,难以将VIC应用于操作系统,因为VIC配方的键传感器通常不安装在实际系统中。这使得VIC不可能或者难以准确确定的问题。通过处理这种实际挑战,提出了一种使用虚拟传感器进行更换和备份目的的先进原位传感器校准方法的适用性和性能。用于替换的虚拟传感器使得即使具有有限数量的物理传感器,也可以构建校准制剂。备用虚拟传感器可以数学上加强制剂,从而提高校准精度。在对典型空气处理单元的案例研究中,基于模型驱动的虚拟传感器构建建议的虚拟传感器辅助VIC,并且通过添加数据驱动的虚拟传感器,它显示出多个同时传感器误差的出色校准精度。此外,通过开发各种虚拟传感器,使用多个操作数据集的使用对于校准同时误差有效。使用虚拟传感器,校准误差从72.7%降至5.2%。

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