配水网络中检测污染事件的传感器定位——基于NSGA-II的多目标方法

C. H. Antunes, D. Margarida
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引用次数: 9

摘要

由于衍生节点和接入点数量众多且地理分布分散,配电网是供水系统中最易受影响的部分。因此,需要一个可靠的基于传感器网络的监测和监控系统来及时发现污染事件。通过优化方法解决了供水网络中检测(意外或故意)污染事件的传感器位置问题,旨在确定一组传感器的最佳位置,从而允许管理实体在短时间内检测这些事件,并能够将其对服务人群的影响降至最低。本文提出了一种多目标进化方法来确定配水网络中传感器的位置,以检测污染事件并根据每个解决方案优点的多个,不相称和相互冲突的评估方面最小化其潜在后果。目标函数为期望检测时间、期望检测前受影响的人群、期望检测前受污染水的消费量和检测可能性。得到了代表Pareto前沿的一组非支配解,并用已知解对其进行了验证。此外,这些信息使我们能够根据决策者的偏好进行权衡并确定良好的折衷解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor location in water distribution networks to detect contamination events - A multiobjective approach based on NSGA-II
The distribution network is the most exposed part of water supply systems due to the large number and geographical dispersion of derivation nodes and access points. Therefore, a reliable monitoring and surveillance system based on a sensor network is necessary to timely detect contamination events. The sensor location problem in water distribution networks to detect (accidental or intentional) contamination events has been tackled by optimization approaches aimed to determine the best location for a set of sensors, thus allowing the management entity to detect those events in a short period of time and be able to minimize their impact on the population served. This paper presents a multiobjective evolutionary approach to determine the location of sensors in a water distribution network to detect a contamination event and minimize its potential consequences according to multiple, incommensurate and conflicting evaluation aspects of the merits of each solution. The objective functions are the expected time of detection, the expected population affected prior to detection, the expected consumption of contaminated water prior to detection, and the detection likelihood. A set of nondominated solutions representing the Pareto front is obtained, which have been validated with known solutions for the case studies. Further, this information enables to exploit tradeoffs and identify good compromise solutions according to the decision maker's preferences.
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