AbstractIntroductionComponent ModelsMixing BoxVAV Fan SystemModeling Using Measured Data From An Experimental FacilityModel Calibration ProceduresResultsModeling Using Measured Data From A Real BuildingDescription Of The Building And Performance DataFunctional TestsCalibration ProcedureNo Fan Catalog Data. The lack of manufacturer’s Functional Tests And Modeling ResultsMixing Box. The results of the functional tests and modeling of the mixing box are shown in Figure 6. In order to avoid measurement errors due to incomplete mixing, the measured supply air temperature, corrected for the rise across the fan, is used asSupply And Return Fans. Figure 7 shows the test results for the supply and return fans. Because the building was occupied at the time, the tests cover only a limited part of the operating range.ConclusionsAcknowledgementsReferencesPeng Xu and Philip Haves Ernest Orlando Lawrence Berkeley National Laboratory HPCBS California Energy Commission Public Interest Energy Research Program LBNL No. 50678 (HPCBS No. E5P2.3T3d) Field Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems Element 5 Project 2.3 Task 2.3.3 High Performance Commercial Building SystemsLBNL-50678 CD-452 Presented at the ACEEE 2002 Summer Study on Energy Efficiency in Buildings, August 18-23, 2002, Asilomar Conference Center, Pacific Grove, California, and published in the proceedings. Field Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems Peng Xu and Philip Haves Building Technologies Department Environmental Energy Technologies Division Ernest Orlando Lawrence Berkeley National Laboratory University of California 1 Cyclotron Road Berkeley, California 94720-8134 USA May 2002 This work was supported by the California Energy Commission Public Interest Energy Research Program and by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State and Community Programs, Office of Building Research and Standards of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098.Field Testing of Component-Level Model-Based Fault Detection Methods for Mixing Boxes and VAV Fan Systems Peng Xu and Philip Haves Building Technologies Department, Environmental Energy Technologies Division Lawrence Berkeley National Laboratory Abstract An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, life-cycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers’ data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation. Introduction There is a growing consensus that most buildings do not perform as well as intended and that faults in HVAC systems are widespread in commercial buildings. There is a lack of skilled people to commission buildings and commissioning is widely seen as too expensive and/or unnecessary. There is also a lack of skilled people, and procedures, to ensure that buildings continue to operate efficiently after commissioning. One approach to these problems is to wholly or partly automate both commissioning and performance monitoring, using computer-based methods of fault detection and diagnosis (FDD). Component-level FDD, which is the subject of the work presented here, uses a bottom up methodology to detect individual faults by analyzing the performance of each component in the HVAC system (Hyvarinen 1997, LBNL 1999, Haves & Khalsa 2000). Model-based approaches to fault detection for different HVAC system or sub system have been proposed by various researchers. Benouarets et al. (1994) describe two model-based schemes for detecting and diagnosing faults in air-conditioning systems. They examined their ability to detect water-side fouling and valve leakage in the cooling coil subsystem of an air handling unit. McIntosh et al. (2000) developed a mechanical model for fault detection and diagnosis in chillers. The model was calibrated using data from an operating system and was used in identifying operating faults. Ahn et al. (2001) present a model-based method for the detection and diagnosis of faults in the cooling tower circuit of a central chilled water facility. Faults are detected from deviations in the values of the characteristic quantities from the corresponding values for fault-free operation. The patterns of the deviations are different for each fault, allowing rules to be developed that can be used to diagnose of the source of the fault. For commissioning, a baseline model of correct operations is normally first configured and adjusted using design information and manufacturers’ data. Next, the behavior of the equipment measured during functional testing is compared to the predictions of the model; significant differences indicate the presence of one or more faults. Once the faults have been fixed, the model is fined-tuned to match the actual performance observed during the functional tests performed to confirm correct operation. The model is then used as part of a diagnostic tool to monitor performance monitoring diagnostic tool during routine operation. In each case, the reference model is used to predict the performance that would be expected in the absence of faults. A comparator is used to determine the significance of any differences between the predicted and measured performance and hence the level of confidence that a fault has been detected. The performance of a model-based fault detection tool is critically dependent on the ability of the model to represent the performance of correctly operating equipment in the field. The paper presents the results of tests to assess how well simple models can represent the performance of HVAC secondary systems (air handling units and distribution systems). The tests were performed at the Iowa Energy Center’s Energy
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