Grey Box Modelling for River Control
Document identifier: oai:dalea.du.se:2658
Keyword: Extended Kalman Filter,
Grey box model,
Model Predictive Control,
Parameter estimation,
RiverPublication year: 2002Relevant Sustainable Development Goals (SDGs):
The SDG label(s) above have been assigned by OSDG.aiAbstract: This paper deals with modelling and identification of a river system using physical insights about the process, experience of operating the system and information about the system dynamics shown by measured data. These components, together, form a linear model structure in the state space form. The inputs of the prospective model are physical variables, which are not directly measured. However, the model inputs can be found by a nonlinear transformation of measured variables. Unknown parameters of the model are estimated from measured data. The modelling work focuses on the principle of parsimony, which means the best model approach is the simplest one that fit the purpose of the application.
The goal of the model is to control the water level of the river where the water flow is mainly determined by the demand for energy generation produced by the hydropower stations along the river. The energy requirement increases in the morning and decreases in the evening. These flow variations, caused by the energy demand, have to be compensated by controlling the power plants downstream, in such a way that the water level between the power stations is guaranteed. Simulation of the control system by using an adaptive model predictive controller shows that the water levels vary less and can be maintained at a higher level than during manual control. This means that more electric power can be produced with the same amount of water flow.
Authors
Björn Sohlberg
Högskolan Dalarna; Elektroteknik
Other publications
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Mats Sernfält
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header:
identifier: oai:dalea.du.se:2658
datestamp: 2021-04-15T12:22:19Z
setSpec: SwePub-du
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recordCreationDate: 2007-04-04
identifier: http://urn.kb.se/resolve?urn=urn:nbn:se:du-2658
titleInfo:
@attributes:
lang: eng
title: Grey Box Modelling for River Control
abstract: This paper deals with modelling and identification of a river system using physical insights about the process experience of operating the system and information about the system dynamics shown by measured data. These components together form a linear model structure in the state space form. The inputs of the prospective model are physical variables which are not directly measured. However the model inputs can be found by a nonlinear transformation of measured variables. Unknown parameters of the model are estimated from measured data. The modelling work focuses on the principle of parsimony which means the best model approach is the simplest one that fit the purpose of the application.\nThe goal of the model is to control the water level of the river where the water flow is mainly determined by the demand for energy generation produced by the hydropower stations along the river. The energy requirement increases in the morning and decreases in the evening. These flow variations caused by the energy demand have to be compensated by controlling the power plants downstream in such a way that the water level between the power stations is guaranteed. Simulation of the control system by using an adaptive model predictive controller shows that the water levels vary less and can be maintained at a higher level than during manual control. This means that more electric power can be produced with the same amount of water flow.
subject:
@attributes:
lang: eng
topic: Extended Kalman Filter
@attributes:
lang: eng
topic: Grey box model
@attributes:
lang: eng
topic: Model Predictive Control
@attributes:
lang: eng
topic: Parameter estimation
@attributes:
lang: eng
topic: River
language:
languageTerm: eng
genre:
publication/journal-article
ref
note:
Published
2
name:
@attributes:
type: personal
authority: du
namePart:
Sohlberg
Björn
role:
roleTerm: aut
affiliation:
Högskolan Dalarna
Elektroteknik
nameIdentifier: bso
@attributes:
type: personal
namePart:
Sernfält
Mats
role:
roleTerm: aut
originInfo:
dateIssued: 2002
publisher: IWA Publishing
place:
placeTerm: London
relatedItem:
@attributes:
type: host
titleInfo:
title: Journal of Hydroinformatics
identifier: 1465-1734
part:
detail:
@attributes:
type: volume
number: 4
@attributes:
type: issue
number: 4
extent:
start: 265
end: 280
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form: print
typeOfResource: text