Abstract
Faults in district heating systems (DHS) cause sub-optimal operating conditions, which increase energy losses. As DHSs are critical infrastructure for many households in Denmark, these faults should be detected and corrected quickly. A novel model-based fault detection and diagnosis framework has been applied to detect and prioritise faults. The framework uses a bound for normal operation based on the residuals between historical sensor data and simulated properties in a digital twin of the DHS. The faults detected are prioritised based on the fault probability calculated using the Chernoff bound method. A case study on a Danish DHS has proven that the framework can produce a prioritised list of faults that maintenance crews can use to target faults with the highest probability. Furthermore, the digital twin allowed for fault location investigation, which could correlate different faults in the DHS. The framework has the potential for real-time fault detection and diagnosis. However, more precise digital twins need to be developed.
This work is supported by the “Proactive and Predictive Maintenance of District Heating Systems” and “IEA DHC TS4”, funded by the Danish Energy Agency under the Energy Technology Development and Demonstration Program, ID number 64020-2102 and 134-22011, respectively.
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Acknowledgements
We thank Peer Andersen, Lasse Elmelund Pedersen, and the rest of their team at Fjernvarme Fyn A/S for their assistance with the data and the model. Also, thanks to Johan Peter Alsing from Danfoss A/S for assisting us with Leanheat Network.
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Madsen, F.W., Bank, T., Søndergaard, H.A.N., Mortensen, L.K., Shaker, H.R. (2024). Digital Twin-Based Fault Detection and Prioritisation in District Heating Systems: A Case Study in Denmark. In: Jørgensen, B.N., da Silva, L.C.P., Ma, Z. (eds) Energy Informatics. EI.A 2023. Lecture Notes in Computer Science, vol 14468. Springer, Cham. https://doi.org/10.1007/978-3-031-48652-4_18
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