Data driven analysis of district cooling substations for performance diagnosis
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Examensarbete för masterexamen
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Sammanfattning
District cooling (DC) is a crucial technology for providing cooling for purposes such
as space cooling for indoor environment, industrial processes, food storage industry. To improve
the efficiency of the district cooling system (DCS) periodic maintenance of the components in the
system is required. This requires detection of faults in the system and addressing these faults.
Previously, fault detection has been done in district heating (DH) system but not as much on
district cooling (DC). In this work, a large scale data driven approach is implemented to identify
the performance of district cooling substations and detect faults.
The scope of this study includes all 180 substations from the city of Gothenburg, Sweden and the
operational data from the primary side of the substation is analysed. Different signatures are
analysed namely, energy signature, delta signature and re- turn temperature signature to identify
faulty behaviour in the substations. From previous research and domain knowledge, the most suitable
methods for the data analysis are chosen and applied. Out of 180 substations, customer categories
for 41 buildings are identified and their delta T signatures are studied for performance diagnosis.
The results show that the method implemented in this study can be used in the fault detection
domain for DC substations on a large scale. A mix of domain knowl- edge and data driven tools are
required to detect faults and analyse performance.
Beskrivning
Ämne/nyckelord
District cooling, substations, performance diagnosis, fault detection, signatures