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Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach

Kumar, Krishna LU orcid ; Kumar, Aman ; Saini, Gaurav ; Mohammed, Mazin abed ; Shah, Rachna ; Nedoma, Jan ; Martinek, Radek and Kadry, Seifedine (2024) In Sustainable Computing: Informatics and Systems 42.
Abstract

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%,... (More)

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.

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author
; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
ANN, Curve Fitting, Hydro Turbine, Machine Learning, Operation and Maintenance
in
Sustainable Computing: Informatics and Systems
volume
42
article number
100958
publisher
Elsevier
external identifiers
  • scopus:85185188820
ISSN
2210-5379
DOI
10.1016/j.suscom.2024.100958
language
English
LU publication?
no
id
a8b5fadb-335b-4618-832b-af64dbf97a34
date added to LUP
2024-04-15 13:42:53
date last changed
2024-05-16 14:46:49
@article{a8b5fadb-335b-4618-832b-af64dbf97a34,
  abstract     = {{<p>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&amp;M) strategies.</p>}},
  author       = {{Kumar, Krishna and Kumar, Aman and Saini, Gaurav and Mohammed, Mazin abed and Shah, Rachna and Nedoma, Jan and Martinek, Radek and Kadry, Seifedine}},
  issn         = {{2210-5379}},
  keywords     = {{ANN; Curve Fitting; Hydro Turbine; Machine Learning; Operation and Maintenance}},
  language     = {{eng}},
  month        = {{04}},
  publisher    = {{Elsevier}},
  series       = {{Sustainable Computing: Informatics and Systems}},
  title        = {{Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach}},
  url          = {{http://dx.doi.org/10.1016/j.suscom.2024.100958}},
  doi          = {{10.1016/j.suscom.2024.100958}},
  volume       = {{42}},
  year         = {{2024}},
}