Central water resources management in a cascade of hydropower plants

Theobald, Ute; Theobald, Stephan

doi:10.1109/icnsc.2011.5874907

Cited by 6 publications

(5 citation statements)

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“…Mainly driven by the progress in this field, the techniques became also popular for general flow forecasting [31] or even water quality applications [17]. We follow this path in our discussion before highlighting more general, supporting techniques such as data assimilation and uncertainty analysis, as well as the evolution of technical systems.…”

Section: Predictions In Water Systemsmentioning

confidence: 97%

“…This lead time can be effectively used to implement measures either to reduce the consequence of flooding through for example evacuation, or to reduce flooding itself through controlling dedicated hydraulic structures [9,29,31] or adhoc interventions, such as placing sandbags.…”

Section: Predictions In Water Systemsmentioning

confidence: 99%

“…Non-structural measures in water systems, such as adjustable structures like gates, pumps, and sluices, are operated in order to keep water levels [9,11,29,31], flows [5,14,24,31], or water quality variables [1,17,18] in a desired state. There are many ways in which these adjustable structures can be controlled.…”

Section: Control Of Water Systemsmentioning

confidence: 99%

“…Several successful implementations of control systems on water systems are described in [21]. In increasing order of information usage, three possible types of control for water systems are feedback control [5,11,18], feed-forward control [17,31], and model predictive control [1,8,9,14,24,29].…”

Section: Control Of Water Systemsmentioning

confidence: 99%

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Towards integrating water prediction and control technology

Overloop

Negenborn

Schwanenberg

et al. 2011

2011 International Conference on Networking, Sensing and Control

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Abstract-In most countries, especially in deltas, there is a long tradition in the management of water resource systems, in particular related to structural measures such as the construction of dikes and riverine/coastal hydraulic structures. We discuss how this infrastructure can be supported and managed in order to serve as non-structural measures. Therefore, we present a review about technology for the prediction and control of water resources. This covers techniques for flow and water quality forecasting including modeling and data assimilation as well as its combination with predictive controllers for managing hydraulic structures. These techniques are discussed both from a conceptual and technical perspective. Furthermore, we give an overview about ongoing and future work in this field, which is aimed at integrating the techniques from both the water prediction and the water control community.

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Section: Predictions In Water Systemsmentioning

confidence: 97%

Section: Predictions In Water Systemsmentioning

confidence: 99%

Section: Control Of Water Systemsmentioning

confidence: 99%

Section: Control Of Water Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Towards integrating water prediction and control technology

Overloop

Negenborn

Schwanenberg

et al. 2011

2011 International Conference on Networking, Sensing and Control

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“…The current control solution is a local water level and discharge control as described e.g. in [1,2,3]. The water level at the downstream weir is controlled by a PI control loop with added feed forward compensation of upstream discharge variations.…”

Section: Figure 1 Locations Of the 25 Barrages At Moselle Rivermentioning

confidence: 99%

On Migrating to Advanced Model Predictive Control Strategies at the Moselle River

Simons¹,

Kasper²,

Amann³

et al.

EPiC Series in Engineering

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Along the German and Luxembourgian part of the Moselle River, eleven distributed local water level and discharge controllers ensure safe navigation by guaranteeing a water level within a specified tolerance and by reducing variations in the river discharge. The current control scheme is based on gain scheduled PI control with a feed forward disturbance compensation element. Both were parametrized using a 1D Saint-Venant model. Due to advancements in control strategies and processing power, the current scheme will be upgraded by adding a model predictive feed forward component (MPFFC) which improves local control and links the isolated local controllers to coordinate their efforts. The authors want to report on this process from an operator's point of view and share their insights from the ongoing testing procedure prior to actual service. A prototype implementation was deployed on the target hardware and linked to the data acquisition system to verify real time operation. The logged results are then verified using a simulation model.

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