Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization
Autori
Milašinović, MilošProdanović, Dušan
Stanić, Miloš
Zindović, Budo
Stojanović, Boban
Milivojević, Nikola
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assi...milate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window.
Ključne reči:
data assimilation / NSGA-II / PID controllers / tuning controllersIzvor:
Journal of Hydroinformatics, 2022, 24, 4Izdavač:
- IWA Publishing
Finansiranje / projekti:
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200092 (Univerzitet u Beogradu, Građevinski fakultet) (RS-MESTD-inst-2020-200092)
Kolekcije
Institucija/grupa
GraFarTY - JOUR AU - Milašinović, Miloš AU - Prodanović, Dušan AU - Stanić, Miloš AU - Zindović, Budo AU - Stojanović, Boban AU - Milivojević, Nikola PY - 2022 UR - https://grafar.grf.bg.ac.rs/handle/123456789/2693 AB - Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assimilate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window. PB - IWA Publishing T2 - Journal of Hydroinformatics T1 - Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization IS - 4 VL - 24 DO - 10.2166/hydro.2022.034 ER -
@article{ author = "Milašinović, Miloš and Prodanović, Dušan and Stanić, Miloš and Zindović, Budo and Stojanović, Boban and Milivojević, Nikola", year = "2022", abstract = "Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assimilate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window.", publisher = "IWA Publishing", journal = "Journal of Hydroinformatics", title = "Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization", number = "4", volume = "24", doi = "10.2166/hydro.2022.034" }
Milašinović, M., Prodanović, D., Stanić, M., Zindović, B., Stojanović, B.,& Milivojević, N.. (2022). Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization. in Journal of Hydroinformatics IWA Publishing., 24(4). https://doi.org/10.2166/hydro.2022.034
Milašinović M, Prodanović D, Stanić M, Zindović B, Stojanović B, Milivojević N. Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization. in Journal of Hydroinformatics. 2022;24(4). doi:10.2166/hydro.2022.034 .
Milašinović, Miloš, Prodanović, Dušan, Stanić, Miloš, Zindović, Budo, Stojanović, Boban, Milivojević, Nikola, "Control theory-based data assimilation for open channel hydraulic models: tuning PID controllers using multi-objective optimization" in Journal of Hydroinformatics, 24, no. 4 (2022), https://doi.org/10.2166/hydro.2022.034 . .