Double Q-PI architecture for smart model-free control of canals

Shahverdi, Kazem; Alamiyan-Harandi, Farinaz; Maestre, José María Maestre

doi:10.1016/j.compag.2022.106940

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…One of the extensively employed models, which simulates water conveyance and distribution systems by solving the complete form of the Saint‐Venant equations (Tavares et al, 2013), is the irrigation conveyance system simulation (ICSS) hydrodynamic model that simulates steady and unsteady flows passing under water structures (Fatemeh et al, 2020; Shahverdi et al, 2016, 2022; Shahverdi & Maestre, 2022). It was also used in this research to simulate the studied canal under base and future climate change conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the impact of climate change on irrigation canal performance

Mohebbi,

Ghodousi,

Shahverdi

2024

Irrigation and Drainage

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Irrigation canals are the main systems that convey water from sources to demand nodes. Their performance is affected by climate change. In this research, the effect of climate change on temperature and precipitation was investigated in the Aghili irrigation network over the base period 1994–2017, and the Aghili east canal performance was consequently assessed. To this end, the climate data were first assessed by the Mann–Kendall test to determine trends. Then, changes in temperature and precipitation were simulated using HadGEM2‐ES in the Long Ashton Research Station Weather Generator (LARS‐WG) under two representative concentration pathways (RCPs) of 2.6 and 8.5 over the periods 2021–2040 and 2041–2060. CROPWAT8 was used to calculate the crop water requirement and irrigation hydromodule, and the turnout flow in the considered canal was calculated next. Finally, the canal was simulated and assessed. The results showed that the maximum temperature, evapotranspiration and turnout flow increases are 3.7°C, 1.45 mm/day and 39 L/s, respectively, related to the base timescale. Additionally, the adequacy performance decreased to 0.768 from 0.986, leading to a maximum extra water requirement of 15.1 million m3/year in the Aghili east canal under a pessimistic scenario.

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Section: Introductionmentioning

confidence: 99%

Evaluating the impact of climate change on irrigation canal performance

Mohebbi,

Ghodousi,

Shahverdi

2024

Irrigation and Drainage

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“…The double Q‐learning PID architecture of Carlucho et al (2019) was implemented to control ASCE's Canal 2 (Clemmens et al, 1998) by providing the canal's state‐space model as a linear model in the MATLAB environment. The canal's linear model was used to simulate the canal during the learning process embedded within the controller, written in Python (Shahverdi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

AICSS: Automatic simulator–controller/optimizer model of open channels

Shahverdi

2023

Irrigation and Drainage

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The coupling between simulators and controllers in control systems of irrigation canals has always been a challenging issue. Consequently, simplified or approximated models of Saint‐Venant equations (SVEs) have unavoidably been used thus far. In this research, a coupled simulator–controller/optimizer model named Automatic Irrigation Canal Simulation System (AICSS) was developed to control irrigation conveyance system simulation (ICSS) using MATLAB, which allows setting input data, retrieving input or output data and opening, running and closing the ICSS model, enabling any real‐time modelling of canals with mathematical and statistical experiments as well as calibration of the discharge coefficient and Manning's roughness. To this end, several submodels were written in FORTRAN to automatically guide orders in ICSS received from MATLAB. The developed model is started by pressing the F5 key in MATLAB. To introduce the AICSS application and evaluate it, the proportional‐integral‐derivative (PID) controller was used and tested in the E1R1 Dez and Aghili East canals located in Khuzestan province (Iran), showing successful results when simulating and controlling the canals and leading to maximum and average water depth errors of 2.3% and 0.07% in the E1R1 canal and 5.3% and 0.36% in the Aghili East canal, respectively.

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Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient

Shahverdi

Talebmorad

2023

Water Resour Manage

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Double Q-PI architecture for smart model-free control of canals

Cited by 9 publications

References 28 publications

Evaluating the impact of climate change on irrigation canal performance

Evaluating the impact of climate change on irrigation canal performance

AICSS: Automatic simulator–controller/optimizer model of open channels

Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient

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