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|Title:||Multi-objective optimal sensor placement for low-pressure gas distribution networks||Authors:||Zan, Thaw Tar Thein
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Zan, T. T. T., Gupta, P., Wang, M., Dauwels, J., & Ukil, A. (2018). Multi-objective optimal sensor placement for low-pressure gas distribution networks. IEEE Sensors Journal, 18(16), 6660-6668. doi:10.1109/jsen.2018.2850847||Journal:||IEEE Sensors Journal||Abstract:||Natural gas distribution systems are inherently vulnerable to accidental or intentional intrusion. Such events lead to financial losses and endanger the environmental and public safety. Therefore, it is crucial to adequately monitor the gas distribution systems. An important step toward this goal is to optimize the placement of sensors in the network. In this paper, we propose three design objectives including time-to-detection (TTD), sensitivity, and impact propagation (IP) and implement five multi-objective optimization algorithms (greedy, greedy randomized adaptive search procedure, non-dominated sorting genetic algorithm II, FrameSense, and particle swarm optimization (PSO)) to strategically place the sensors. From the results on an artificial network with 37 nodes and 50 branches and a real network in Singapore with 148 nodes and 150 branches, we find that Greedy and PSO algorithms are almost 10 times faster than the other algorithms in computational time. We also investigate the tradeoff between the design objectives and the number of sensors. Since TTD, sensitivity, and IP have different measurement units, we normalize their values within 0 to 1 (0%-100%) and consider the average of those three normalized values as the design cost. For 10% design cost, the number of required sensors is 5 and 8 for the artificial network and the real network, respectively. The results indicate that PSO yields the sensor configuration with the lowest design cost and the computational time.||URI:||https://hdl.handle.net/10356/140084||ISSN:||1530-437X||DOI:||10.1109/JSEN.2018.2850847||Rights:||© 2018 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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