Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers

Increasing renewable energy usage puts extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Model-driven forecasting used for river state estimation often provides bad results due to numerous uncertainties....

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Autores principales:	Miloš Milašinović, Dušan Prodanović, Budo Zindović, Boban Stojanović, Nikola Milivojević
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	hydraulic model update model-driven forecasting near future forecasting pid controller pid controllers’ tuning Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/28857e6f1db6478598f6714c192b0116
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spelling	oai:doaj.org-article:28857e6f1db6478598f6714c192b01162021-11-05T17:46:41ZControl theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers1464-71411465-173410.2166/hydro.2021.078https://doaj.org/article/28857e6f1db6478598f6714c192b01162021-05-01T00:00:00Zhttp://jh.iwaponline.com/content/23/3/500https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Increasing renewable energy usage puts extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Model-driven forecasting used for river state estimation often provides bad results due to numerous uncertainties. False inflows and poor initialization are some of the uncertainty sources. To overcome this, standard data assimilation (DA) techniques (e.g., ensemble Kalman filter) are used, which are not always applicable in real systems. This paper presents further insight into the novel, tailor-made model update algorithm based on control theory. According to water-level measurements over the system, the model is controlled and continuously updated using proportional–integrative–derivative (PID) controller(s). Implementation of the PID controllers requires the controllers’ parameters estimation (tuning). This research deals with this task by presenting sequential, multi-metric procedure, applicable for controllers’ initial tuning. The proposed tuning method is tested on the Iron Gate hydropower system in Serbia, showing satisfying results. HIGHLIGHTS Uncertainty of the boundary and initial conditions affects model-driven forecasting.; Data Assimilation is used to overcome these problems.; Research presents potential of using novel, tailor-made, PID controllers-based data assimilation method for river hydraulic models update.; Method could be used as a decision-support tool for hydropower systems control.; Sequential, multi-metric tuning procedure has been introduced.;Miloš MilašinovićDušan ProdanovićBudo ZindovićBoban StojanovićNikola MilivojevićIWA Publishingarticlehydraulic model updatemodel-driven forecastingnear future forecastingpid controllerpid controllers’ tuningInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 3, Pp 500-516 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hydraulic model update model-driven forecasting near future forecasting pid controller pid controllers’ tuning Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	hydraulic model update model-driven forecasting near future forecasting pid controller pid controllers’ tuning Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Miloš Milašinović Dušan Prodanović Budo Zindović Boban Stojanović Nikola Milivojević Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
description	Increasing renewable energy usage puts extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Model-driven forecasting used for river state estimation often provides bad results due to numerous uncertainties. False inflows and poor initialization are some of the uncertainty sources. To overcome this, standard data assimilation (DA) techniques (e.g., ensemble Kalman filter) are used, which are not always applicable in real systems. This paper presents further insight into the novel, tailor-made model update algorithm based on control theory. According to water-level measurements over the system, the model is controlled and continuously updated using proportional–integrative–derivative (PID) controller(s). Implementation of the PID controllers requires the controllers’ parameters estimation (tuning). This research deals with this task by presenting sequential, multi-metric procedure, applicable for controllers’ initial tuning. The proposed tuning method is tested on the Iron Gate hydropower system in Serbia, showing satisfying results. HIGHLIGHTS Uncertainty of the boundary and initial conditions affects model-driven forecasting.; Data Assimilation is used to overcome these problems.; Research presents potential of using novel, tailor-made, PID controllers-based data assimilation method for river hydraulic models update.; Method could be used as a decision-support tool for hydropower systems control.; Sequential, multi-metric tuning procedure has been introduced.;
format	article
author	Miloš Milašinović Dušan Prodanović Budo Zindović Boban Stojanović Nikola Milivojević
author_facet	Miloš Milašinović Dušan Prodanović Budo Zindović Boban Stojanović Nikola Milivojević
author_sort	Miloš Milašinović
title	Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
title_short	Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
title_full	Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
title_fullStr	Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
title_full_unstemmed	Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
title_sort	control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/28857e6f1db6478598f6714c192b0116
work_keys_str_mv	AT milosmilasinovic controltheorybaseddataassimilationforhydraulicmodelsasadecisionsupporttoolforhydropowersystemssequentialmultimetrictuningofthecontrollers AT dusanprodanovic controltheorybaseddataassimilationforhydraulicmodelsasadecisionsupporttoolforhydropowersystemssequentialmultimetrictuningofthecontrollers AT budozindovic controltheorybaseddataassimilationforhydraulicmodelsasadecisionsupporttoolforhydropowersystemssequentialmultimetrictuningofthecontrollers AT bobanstojanovic controltheorybaseddataassimilationforhydraulicmodelsasadecisionsupporttoolforhydropowersystemssequentialmultimetrictuningofthecontrollers AT nikolamilivojevic controltheorybaseddataassimilationforhydraulicmodelsasadecisionsupporttoolforhydropowersystemssequentialmultimetrictuningofthecontrollers
_version_	1718444090249445376

Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers

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