Proceedings of the
The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK
Community Detection Algorithm for Natural Gas Pipeline Network Based on Transmission Characteristics
1School of Energy Resources, China University of Geosciences (Beijing), China.
2PipeChina Oil & Gas Control Center, Beijing, China.
3Department of Marketing, PipeChina, China.
4Department of Energy, Polytechnic University of Milan, Italy.
5School of Storage and Transportation Engineering, China University of Petroleum (Beijing), China.
ABSTRACT
The community detection is beneficial for natural gas pipeline network to optimize daily operation and divide regions in a reasonable way. However, traditional community detection methods only focus on the network topology structure and cannot reflect the transmission characteristics of natural gas pipeline network. To solve this problem, a gas flow tracing algorithm, based on the proportional sharing principle, is applied to determine the contribution of each source node to each download node. According to the gas flow tracing result, the gas transmission correlation strength is defined, the gas transmission modularity is constructed, and their physical meaning is elaborated. By replacing the traditional modularity with the gas transmission modularity, we improve the fast greedy community algorithm to form a new community detection algorithm for natural gas pipeline network, which can automatically obtain the optimal community division under different working conditions. The case study in this paper demonstrates how our method adaptively identify and divide communities for different conditions based on gas transmission characteristics.
Keywords: Natural gas pipeline network, Community detection, Gas flow tracing, Gas transmission modularity.
