Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254587" target="_blank" >RIV/61989100:27240/24:10254587 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2210537924000039" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210537924000039</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.suscom.2024.100958" target="_blank" >10.1016/j.suscom.2024.100958</a>
Alternative languages
Result language
angličtina
Original language name
Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach
Original language description
Silt is the leading cause of the erosion of the turbine's underwater components during hydropower generation. This erosion subsequently decreases the machine's efficiency. The present study aims to develop statistical correlations for predicting the efficiency of a hydropower plant based on the Kaplan turbine. Historical data from a Kaplan turbine-based hydropower plant was employed to create the model. Curve fitting, multilinear regression (MLR), and artificial neural network (ANN) techniques were used to develop models for predicting the machine's efficiency. The results show that the ANN method is better at predicting the machine's efficiency than the MLR and curve fitting methods. It got an R2-value of 0.99966, a MAPE of 0.0239%, and an RMSPE of 0.1785%. Equipment manufacturers, plant owners, and researchers can use the established correlation to evaluate the machine's condition in real-time. Additionally, it offers utility in formulating effective operations and maintenance (O&M) strategies. (C) 2024 Elsevier Inc.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
—
Continuities
O - Projekt operacniho programu
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sustainable Computing-Informatics & Systems
ISSN
2210-5379
e-ISSN
—
Volume of the periodical
42
Issue of the periodical within the volume
2024
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
—
UT code for WoS article
001172403300001
EID of the result in the Scopus database
2-s2.0-85185188820