Application of an In-Line Storage Strategy to Improve the Operational Performance of Main Irrigation Canals Using Model Predictive Control

Hashemy, S. M.; Monem, M J; Maestre, J. M.; Overloop, P. J. van

doi:10.1061/(asce)ir.1943-4774.0000603

Cited by 34 publications

(21 citation statements)

References 21 publications

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“…Presently, the synthesis of real‐time control strategies for separated stormwater systems remains largely unexplored. Guidance may however be taken from work on other water systems, including reservoir operations, open‐channel irrigation systems, water distribution systems, and sewer systems (Hashemy et al, ; Montestruque & Lemmon, ; van Overloop, ; van Overloop et al, ). While these prior approaches show great promise, they do not explicitly account for the types of time scales and feedbacks inherent in urban watersheds.…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Wong

Kerkez

2018

Water Resources Research

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The real‐time control of urban watersheds is now being enabled by a new generation of “smart” and connected technologies. By retrofitting stormwater systems with sensors and valves, it becomes possible to adapt entire watersheds dynamically to individual storms. A catchment‐scale control algorithm is introduced, which abstracts an urban watershed as a linear integrator delay dynamical system, parameterizes it using physical watershed characteristics, and then controls network flows using a Linear Quadratic Regulator. The approach is simulated on a 4‐km2 urban headwater catchment in Ann Arbor, Michigan, demonstrating the gains of a stormwater system that can adaptively balance between flood mitigation and flow reduction. We introduce an equivalence analysis and illustrate the performance of the controlled watershed across large events (30‐year storms) to show the uncontrolled passive watershed can only match it during smaller events (10‐year storm). For these smaller events, the storage volume of the controlled storage nodes (ponds, basins, and wetlands) could be reduced as much as 50% and still achieve the same performance of the controlled watershed. A controller placement analysis is also carried out, whereby all possible combinations of controlled sites are simulated across a wide spectrum of design storms. We show that the control of every storage node may not be needed in a watershed, but rather that in our case study a small subset (30%) of the overall watershed can be controlled in coordination to achieve outcomes that match a fully controlled system, even when tested across a long‐term rainfall record and under noisy sensor measurements.

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Section: Introductionmentioning

confidence: 99%

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Wong

Kerkez

2018

Water Resources Research

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“…These assumptions are common in control models of irrigation canals (see, for example, [10], [44], and [45]) and guarantee that the continuous part of the optimization problem is convex, which is a convenient feature given the efficient solvers available for this type of problem.…”

Section: Mobile Configuration Of Model Predictive Controlmentioning

confidence: 99%

“…As long as the operators are capable of following the instructions provided by the MoMPC, then the MPC is able to cope with these issues. Likewise, the flexibility of the MPC framework allows introducing additional constraints to limit, for example, the workload of the operators or to calculate the most appropriate terminal state for the canal during the rest periods of the operators [10]. Nevertheless, these topics are beyond the scope of this article due to the slow and stable dynamics of irrigation canals, which mitigates the uncertainty regarding the precise moment at which the operators apply the control actions or the effects derived from the lack of precision in the gate movements.…”

Section: Mobile Configuration Of Model Predictive Controlmentioning

confidence: 99%

“…A model of the Dez Main Canal in Iran has been developed using all exact dimensions of the cross sections and gates of the canal. The model, whose profile can be seen in Figure 2, has been calibrated and used in multiple studies [10], [12], [54]. The simulations run with a time step of 30 s, which is accurate enough to capture all relevant low-frequency behavior and the resonances that may occur.…”

Section: Process Model Of An Irrigation Canalmentioning

confidence: 99%

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Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]

2015

IEEE Control Syst.

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U ntil now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including indus-The difficulty arises when there are components in the controlled system that are not easily modeled in a standard setting. One important and broad class of such systems is when humans are involved in the actuation process, in measurements, or in system dynamics. How to integrate the important role played by human operators in the control problem formulation is still an open question. Control methods typically rely on a fully automatic control operation that, in the case of humans being involved in the control system, is unrealistic. Likewise, fully manual control methods might compromise the performance of the system because of factors such as limitations on the availability of the operators to implement control actions or the lack of coordination when several operators are involved. Therefore, there is an incentive in terms of performance to provide a link between stateof-the-art control methods and human operation.

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“…However, to achieve a wide range of objectives in modern operation, it is essential to introduce advanced operational strategies. These would include on‐demand agricultural water systems (van Overloop, ), inline water‐storing within the canals (Hashemy et al, ), conjunctive operational management of surface and groundwater resources, competitive operation based on local water markets and performing economical operation within the irrigation districts (Hashemy Shahdany et al, ). The operational strategies can be implemented by using the centralized control systems in an extensive manner on the main irrigation canals.…”

Section: Introductionmentioning

confidence: 99%

EFFECTS OF CANAL AUTOMATION ON REDUCING GROUNDWATER EXTRACTION WITHIN IRRIGATION DISTRICTS: CASE STUDY OF QAZVIN IRRIGATION DISTRICT^†

Jolfan

Shahdany

Javadi

et al. 2019

Irrigation and Drainage

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The impact of upgrading irrigation canal water distribution and delivery systems on reducing the pumping of water from aquifers was investigated. Consequently, energy consumption and carbon emission reduction were investigated. For this purpose, a decentralized integrated control system and a centralized control system were designed. The arrangements were tested on the main canal of the Qazvin Irrigation District under a water shortage scenario. In this research, the centralized automatic control system was satisfactorily implemented in water distribution under the water shortage scenario. The decentralized control system (PI) did not show significant operational performance. According to the results, obtained values of the index of adequacy by upgrading utilization to decentralized automated control systems (PI) were 70 and 77% for the centralized automated control system. Accordingly, the reduction in harvesting from the aquifer was 69.7 and 71.5%, respectively, the reduction of energy consumption was 54.5 and 63.7%, and greenhouse gas emission reduction was 52.4 and 62.5%. © 2019 John Wiley & Sons, Ltd.

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Application of an In-Line Storage Strategy to Improve the Operational Performance of Main Irrigation Canals Using Model Predictive Control

Cited by 34 publications

References 21 publications

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]

EFFECTS OF CANAL AUTOMATION ON REDUCING GROUNDWATER EXTRACTION WITHIN IRRIGATION DISTRICTS: CASE STUDY OF QAZVIN IRRIGATION DISTRICT^†

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Application of an In-Line Storage Strategy to Improve the Operational Performance of Main Irrigation Canals Using Model Predictive Control

Cited by 34 publications

References 21 publications

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Real‐Time Control of Urban Headwater Catchments Through Linear Feedback: Performance, Analysis, and Site Selection

Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]

EFFECTS OF CANAL AUTOMATION ON REDUCING GROUNDWATER EXTRACTION WITHIN IRRIGATION DISTRICTS: CASE STUDY OF QAZVIN IRRIGATION DISTRICT†

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EFFECTS OF CANAL AUTOMATION ON REDUCING GROUNDWATER EXTRACTION WITHIN IRRIGATION DISTRICTS: CASE STUDY OF QAZVIN IRRIGATION DISTRICT^†