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首页> 外文期刊>Journal of Hydroinformatics >Modelling and optimized water management of artificial inland waterway systems
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Modelling and optimized water management of artificial inland waterway systems

机译：人工内河航道系统的建模和优化水管理

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摘要

A decision support system (DSS) for optimized operational water management of artificial inland waterways is presented. It will be deployed as part of a supervisory control and data acquisition (SCADA) system of the Mittellandkanal (MLK), a large canal structure in northern Germany, and relies on experience gained from a similar system. The DSS uses a model predictive controller with a 48 h prediction horizon to calculate optimal pump and discharge strategies that will ensure navigable water levels and at the same time minimize operational costs. The internal process model for the model predictive controller is obtained from a numerical integration of the Saint Venant equations using Godunov's method. The initial state needed for an accurate prediction is estimated using moving horizon state estimation (MHE) or unscented Kalman filtering. Additionally, the state estimation methods are used to estimate non-measurable disturbance inflows, which may have a strong impact on the control performance if not compensated for by the model predictive controller. The optimal control strategy is transformed into discrete-valued pump and discharge jobs that account for technical and operational input constraints. Closed-loop simulations with a high-resolution hydrodynamic numerical model of the MLK illustrate the ability of the control algorithm to adapt to model uncertainties and non-controllable inputs.
机译：提出了一种决策支持系统（DSS），用于优化人工内河航道的运营水管理。它将被部署为德国北部大型运河结构Mittellandkanal（MLK）的监督控制和数据采集（SCADA）系统的一部分，并将依赖于从类似系统中获得的经验。 DSS使用具有48小时预测范围的模型预测控制器来计算最佳的泵和排放策略，这些策略将确保可航行的水位，同时将运营成本降至最低。使用Godunov方法从Saint Venant方程的数值积分获得模型预测控制器的内部过程模型。使用移动视界状态估计（MHE）或无味卡尔曼滤波可以估计准确预测所需的初始状态。此外，状态估计方法用于估计不可测量的干扰流入，如果模型预测控制器未对其进行补偿，则可能会对控制性能产生重大影响。最佳控制策略被转换为离散值的泵和排放作业，这些作业考虑了技术和操作输入方面的限制。带有MLK高分辨率流体力学数值模型的闭环仿真说明了控制算法适应模型不确定性和不可控输入的能力。

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