通过扩展卡尔曼过来在线估计水分系统的状态
Matthew Bartos1, Meghna Thomas1, Min-Gyu Kim1
1Fariborz Maseeh Dept. of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, 301 E Dean Keeton St, Austin, 78712, TX, USA.
Water research
|August 13, 2024
概括
本研究引入了一种用于实时监控水分系统 (WDS) 的新方法. 通过将物理液压模型与扩展卡尔曼波器 (EKF) 结合起来,它可以使用有限的传感器数据准确地估计系统流量和头部.
科学领域:
- 液压工程 液压工程 液压工程
- 水资源管理 水资源管理
- 数据同化数据同化
背景情况:
- 供水系统 (WDS) 需要实时的运营数据以进行高效的管理.
- 现有的液压模型缺乏用于实时传感器集成的同步数据同化能力.
- 在WDS中的稀疏传感器测量限制了准确的实时状态估计.
研究的目的:
- 开发一个新的状态估计方法为WDSs.
- 将实时传感器数据与基于物理的液压模型集成.
- 提高WDS流量和头部估计的准确性.
主要方法:
- 使用1D圣维南方程,制定了WDS液压的状态空间模型.
- 将物理模型与扩展卡尔曼波器 (EKF) 结合起来进行状态估计.
- 通过EPANET模拟和使用稀疏的传感器数据进行持久分析来验证模型.
主要成果:
- 拟议的基于物理的模型与EPANET的稳定状态模拟非常相匹配.
- EKF-fused传感器数据准确估计了未被监控位置的流量和头部.
- 状态估计方法成功地从稀疏测量中推断出内部液压状态.
结论:
- 开发的方法可以使用有限的传感器数据在WDS中准确的实时状态估计.
- 这种方法有助于为WDSs创建实时操作模型.
- 结果支持在线检测和减轻WDS危险,如泄漏和爆发.
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