Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20200 - Electrical engineering, Electronic engineering, Information engineering
Návaznosti výsledku
Projekt
—
Návaznosti
O - Projekt operacniho programu
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Sustainable Computing-Informatics & Systems
ISSN
2210-5379
e-ISSN
—
Svazek periodika
42
Číslo periodika v rámci svazku
2024
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
13
Strana od-do
—
Kód UT WoS článku
001172403300001
EID výsledku v databázi Scopus
2-s2.0-85185188820