Refrigerant amount detection algorithm for a ground source heat pump unit
- Authors
- Choi, H.; Cho, H.; Choi, J.M.
- Issue Date
- 2012
- Keywords
- Ground source heat pump unit; Ground-loop heat exchanger; Refrigerant amount detection; Refrigerant charge; Subcooling; Superheat
- Citation
- Renewable Energy, v.42, pp.111 - 117
- Indexed
- SCIE
SCOPUS
- Journal Title
- Renewable Energy
- Volume
- 42
- Start Page
- 111
- End Page
- 117
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/110667
- DOI
- 10.1016/j.renene.2011.08.055
- ISSN
- 0960-1481
- Abstract
- To make an energy efficient GSHP system, the heat pump unit should have high efficiency and be optimized well with other parts such as ground heat exchangers and pumps. The amount of refrigerant charge in the heat pump unit is one of the primary parameters influencing system performance. In the present study, the effects of off-design charge on the performance of a ground source heat pump unit are investigated by varying refrigerant charge amount, and the algorithm to predict refrigerant charge amount in the heat pump unit was developed. Undercharge or overcharge of refrigerant into the ground source heat pump unit degraded performance and deteriorated system reliability. Refrigerant flow control by an EEV compensated for refrigerant undercharge or overcharge fault. However, refrigerant charge amount should be detected to optimize the heat pump unit performance. In the first step to develop the algorithm of refrigerant amount detection, the subcooling and superheat at the adjusted EEV conditions are determined as recommended parameters, because they are not sensitive to the EWT of the OD HX. Finally, the subcooling is selected as a fault detection and diagnosis parameter about the refrigerant charge amount in the second screening analysis, because it is properly sensitive to charge amount. The algorithm to predict the refrigerant amount detection in the ground source heat pump unit was developed based on the test data and analysis. The refrigerant amount detection algorithm gave relatively good predictions within a relative deviation of 8.0%. © 2011 Elsevier Ltd.
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Collections - College of Engineering > School of Civil, Environmental and Architectural Engineering > 1. Journal Articles
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