Model based control for run-of-river system. Part 1: Model implementation and tuning
Optimal operation and control of a run-of-river hydro power plant depends on good knowledge of the elements of the plant in the form of models. River reaches are often considered shallow channels with free surfaces. A typical model for such reaches use the Saint Venant model, which is a 1D distribut...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Norwegian Society of Automatic Control
2015-10-01
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| Series: | Modeling, Identification and Control |
| Subjects: | |
| Online Access: | http://www.mic-journal.no/PDF/2015/MIC-2015-4-4.pdf |
| _version_ | 1811294249152413696 |
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| author | Liubomyr Vytvytskyi Roshan Sharma Bernt Lie |
| author_facet | Liubomyr Vytvytskyi Roshan Sharma Bernt Lie |
| author_sort | Liubomyr Vytvytskyi |
| collection | DOAJ |
| description | Optimal operation and control of a run-of-river hydro power plant depends on good knowledge of the elements of the plant in the form of models. River reaches are often considered shallow channels with free surfaces. A typical model for such reaches use the Saint Venant model, which is a 1D distributed model based on the mass and momentum balances. This combination of free surface and momentum balance makes the problem numerically challenging to solve. The finite volume method with staggered grid was compared with the Kurganov-Petrova central upwind scheme, and was used to illustrate the dynamics of the river upstream from the Grønvollfoss run-of-river power plant in Telemark, Norway, operated by Skagerak Energi AS. In an experiment on the Grønvollfoss run-of-river power plant, a step was injected in the upstream inlet flow at Årlifoss, and the resulting change in level in front of the dam at the Grønvollfoss plant was logged. The results from the theoretical Saint Venant model was then compared to the experimental results. Because of uncertainties in the geometry of the river reach (river bed slope, etc.), the slope and length of the varying slope parts were tuned manually to improve the fit. Then, friction factor, river width and height drop of the river was tuned by minimizing a least squares criterion. The results of the improved model (numerically, tuned to experiments), is a model that can be further used for control synthesis and analysis. |
| first_indexed | 2024-04-13T05:13:39Z |
| format | Article |
| id | doaj.art-4565176ff4804337b25d10700996f266 |
| institution | Directory Open Access Journal |
| issn | 0332-7353 1890-1328 |
| language | English |
| last_indexed | 2024-04-13T05:13:39Z |
| publishDate | 2015-10-01 |
| publisher | Norwegian Society of Automatic Control |
| record_format | Article |
| series | Modeling, Identification and Control |
| spelling | doaj.art-4565176ff4804337b25d10700996f2662022-12-22T03:00:57ZengNorwegian Society of Automatic ControlModeling, Identification and Control0332-73531890-13282015-10-0136423724910.4173/mic.2015.4.4Model based control for run-of-river system. Part 1: Model implementation and tuningLiubomyr VytvytskyiRoshan SharmaBernt LieOptimal operation and control of a run-of-river hydro power plant depends on good knowledge of the elements of the plant in the form of models. River reaches are often considered shallow channels with free surfaces. A typical model for such reaches use the Saint Venant model, which is a 1D distributed model based on the mass and momentum balances. This combination of free surface and momentum balance makes the problem numerically challenging to solve. The finite volume method with staggered grid was compared with the Kurganov-Petrova central upwind scheme, and was used to illustrate the dynamics of the river upstream from the Grønvollfoss run-of-river power plant in Telemark, Norway, operated by Skagerak Energi AS. In an experiment on the Grønvollfoss run-of-river power plant, a step was injected in the upstream inlet flow at Årlifoss, and the resulting change in level in front of the dam at the Grønvollfoss plant was logged. The results from the theoretical Saint Venant model was then compared to the experimental results. Because of uncertainties in the geometry of the river reach (river bed slope, etc.), the slope and length of the varying slope parts were tuned manually to improve the fit. Then, friction factor, river width and height drop of the river was tuned by minimizing a least squares criterion. The results of the improved model (numerically, tuned to experiments), is a model that can be further used for control synthesis and analysis.http://www.mic-journal.no/PDF/2015/MIC-2015-4-4.pdfRun-of-river hydropowerSaint Venant EquationsModelingSimulation |
| spellingShingle | Liubomyr Vytvytskyi Roshan Sharma Bernt Lie Model based control for run-of-river system. Part 1: Model implementation and tuning Modeling, Identification and Control Run-of-river hydropower Saint Venant Equations Modeling Simulation |
| title | Model based control for run-of-river system. Part 1: Model implementation and tuning |
| title_full | Model based control for run-of-river system. Part 1: Model implementation and tuning |
| title_fullStr | Model based control for run-of-river system. Part 1: Model implementation and tuning |
| title_full_unstemmed | Model based control for run-of-river system. Part 1: Model implementation and tuning |
| title_short | Model based control for run-of-river system. Part 1: Model implementation and tuning |
| title_sort | model based control for run of river system part 1 model implementation and tuning |
| topic | Run-of-river hydropower Saint Venant Equations Modeling Simulation |
| url | http://www.mic-journal.no/PDF/2015/MIC-2015-4-4.pdf |
| work_keys_str_mv | AT liubomyrvytvytskyi modelbasedcontrolforrunofriversystempart1modelimplementationandtuning AT roshansharma modelbasedcontrolforrunofriversystempart1modelimplementationandtuning AT berntlie modelbasedcontrolforrunofriversystempart1modelimplementationandtuning |