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System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system

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  • Kim, Ryunhee
  • Hong, Yejin
  • Choi, Youngwoong
  • Yoon, Sungmin

Abstract

The operational fouling of heat exchangers in a district heating substation (DHS) has a negative effect on the heating efficiency in the primary district heating side and on the heating charge and thermal comfort in the terminal building side. It is effective for early detection of heat exchanger fouling when using the existing building automation system installed in a building. Therefore, an automated fouling detection method is proposed for DHSs. Practical challenges, including sensor absences and limited operation datasets, are considered for the proposed method to be effective for application in a real DHS at the building side. The assistance of virtual sensors is suggested to address the issue of sensor absences. The virtual measurements are fed into the input layer of the fouling detection model to reinforce the physical relationship of the trained model. Based on the physical relationship in the model, the virtual-sensor-assisted fouling detection method can significantly improve fouling detection performance for light fouling conditions. The detection is also successful for new DHS operation patterns that are not included in the training datasets. Compared with the application without a virtual sensor, the virtual-sensor-assisted application showed an approximately 61% improvement in accurate detection with a 17-min fast alarm.

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  • Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s0360544221007647
    DOI: 10.1016/j.energy.2021.120515
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    Cited by:

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    3. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    4. Dino, Giuseppe Edoardo & Catrini, Pietro & Buscemi, Alessandro & Piacentino, Antonio & Palomba, Valeria & Frazzica, Andrea, 2023. "Modeling of a bidirectional substation in a district heating network: Validation, dynamic analysis, and application to a solar prosumer," Energy, Elsevier, vol. 284(C).
    5. Sun, Shaobo & Shan, Kui & Wang, Shengwei, 2022. "An online robust sequencing control strategy for identical chillers using a probabilistic approach concerning flow measurement uncertainties," Applied Energy, Elsevier, vol. 317(C).
    6. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    7. Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
    8. Jabeom Koo & Sungmin Yoon & Joowook Kim, 2022. "Virtual In Situ Calibration for Operational Backup Virtual Sensors in Building Energy Systems," Energies, MDPI, vol. 15(4), pages 1-12, February.
    9. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    10. Frafjord, Aksel Johan & Radicke, Jan-Philip & Keprate, Arvind & Komulainen, Tiina M., 2024. "Data-driven approaches for deriving a soft sensor in a district heating network," Energy, Elsevier, vol. 292(C).

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