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, River
Publication year: 2002
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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. 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 >>

Mats Sernfält

Other publications >>

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        identifier: oai:dalea.du.se:2658
        datestamp: 2021-04-15T12:22:19Z
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                version: 3.7
            recordInfo: 
                recordContentSource: du
                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