Identification of the optimal control characteristics of a small hydropower plant using artificial neural networks and the support vector machines method
Authors:
- Dariusz Borkowski
Abstract
This study investigates a small hydropower plant at varying turbine speeds. The identification of the optimal turbine rotational speed as a function of a flow rate is usually based on the efficiency function of two variables. The existing procedures require a large number of operating points, which prolong the necessary field measurements. In this paper, a new identification procedure based either on the use of a traditional neural network (multilayer perceptron, radial basis function), or on a support vector machines method using dedicated assessment factors, is proposed. The procedure was tested and verified in an experimental 150 kW small hydropower plant that contained two propeller turbines. The tests highlighted the advantages of the support vector machines network over traditional neural networks owing to the precise approximation under a limited amount of training data. It is shown that even 15 measurement points provide relatively accurate results.
- Record ID
- CUTe62497d321d7459299c538f3741e21ff
- Publication categories
- ;
- Author
- Journal series
- Journal of Hydraulic Research, ISSN 0022-1686, e-ISSN 1814-2079
- Issue year
- 2019
- Vol
- 57
- No
- 5
- Pages
- 715-723
- Other elements of collation
- fot.; schem.; wykr.; Bibliografia (na s.) - 722-723; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2018-10-29; Numeracja w czasopiśmie - Vol. 57, Iss. 5
- Keywords in English
- control structures, field studies, flow control, hydraulics of renewable energy systems, velocity measurements
- DOI
- DOI:10.1080/00221686.2018.1522378 Opening in a new tab
- URL
- https://www.tandfonline.com/doi/full/10.1080/00221686.2018.1522378 Opening in a new tab
- Related project
- Metody i algorytmy generacji i przetwarzania energii. . Project leader at PK: , ,
Działalność statutowa - Language
- eng (en) English
- Score (nominal)
- 100
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe62497d321d7459299c538f3741e21ff/
- URN
urn:pkr-prod:CUTe62497d321d7459299c538f3741e21ff
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.