Performance monitoring of kaplan turbine based hydropower plant under variable operating conditions using machine learning approach
(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.
(Less)
- author
- Kumar, Krishna LU ; Kumar, Aman ; Saini, Gaurav ; Mohammed, Mazin abed ; Shah, Rachna ; Nedoma, Jan ; Martinek, Radek and Kadry, Seifedine
- publishing date
- 2024-04-01
- 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&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}}, }