Clustering- and statistic-based approach for detection and impact evaluation of faults in end-user substations of thermal energy systems.

ABSTRACT: In response to climate change mitigation efforts, improving the efficiency of heat networks is becoming increasingly important. An efficient operation of energy systems depends on faultless performance. Following the need for effective fault detection and elimination methods, this study suggests a three-step workflow for increasing automation in managing defective substations on the user level within heat networks. The work focuses on a model region in northern Germany. The local heat network provides data in roughly hourly intervals, including the supply and return temperatures and the volume flow of the substations. Firstly, this study identifies common indicators of faults using k-means clustering analysis of the temperature data and expert knowledge: an exceeded return temperature leve

SUBMITTER: Weber SA 

PROVIDER: S-EPMC11688455 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature