Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine
Само за регистроване кориснике
2018
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental model tests in laboratory and on-site prototype tests at the hydropower plant. The results of those experimental researches are valuable insofar as they are detailed and comprehensive. In order to reduce the number of modes, in which the double-regulated turbine has to be tested with the aim of obtaining the off-cam energy characteristics in unknown operating modes, the application of contemporary artificial neural networks models is presented in the paper. The rationalization of the turbine test conditions may not be at the expense of the quality of the obtained characteristics. Two types of neural networks, feedforward neural networks and adaptive network-based fuzzy inference system with different partitioning methods, were used. The reliability of applied method was considered by analyzing and validating the predicted turbin...e energy parameters with the results obtained in the highly sophisticated laboratory.
Кључне речи:
Off-cam characteristics / Neural network / Hydraulic turbine / ANFISИзвор:
Neural Computing & Applications, 2018, 30, 8, 2569-2579Издавач:
- Springer London Ltd, London
DOI: 10.1007/s00521-017-2843-9
ISSN: 0941-0643
WoS: 000445779400020
Scopus: 2-s2.0-85009756565
Колекције
Институција/група
Mašinski fakultetTY - JOUR AU - Jovanović, Radiša AU - Božić, Ivan PY - 2018 UR - https://machinery.mas.bg.ac.rs/handle/123456789/2869 AB - The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental model tests in laboratory and on-site prototype tests at the hydropower plant. The results of those experimental researches are valuable insofar as they are detailed and comprehensive. In order to reduce the number of modes, in which the double-regulated turbine has to be tested with the aim of obtaining the off-cam energy characteristics in unknown operating modes, the application of contemporary artificial neural networks models is presented in the paper. The rationalization of the turbine test conditions may not be at the expense of the quality of the obtained characteristics. Two types of neural networks, feedforward neural networks and adaptive network-based fuzzy inference system with different partitioning methods, were used. The reliability of applied method was considered by analyzing and validating the predicted turbine energy parameters with the results obtained in the highly sophisticated laboratory. PB - Springer London Ltd, London T2 - Neural Computing & Applications T1 - Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine EP - 2579 IS - 8 SP - 2569 VL - 30 DO - 10.1007/s00521-017-2843-9 ER -
@article{ author = "Jovanović, Radiša and Božić, Ivan", year = "2018", abstract = "The determination of the energy characteristics of a Kaplan hydraulic turbine is based on numerous measuring points during extensive and expensive experimental model tests in laboratory and on-site prototype tests at the hydropower plant. The results of those experimental researches are valuable insofar as they are detailed and comprehensive. In order to reduce the number of modes, in which the double-regulated turbine has to be tested with the aim of obtaining the off-cam energy characteristics in unknown operating modes, the application of contemporary artificial neural networks models is presented in the paper. The rationalization of the turbine test conditions may not be at the expense of the quality of the obtained characteristics. Two types of neural networks, feedforward neural networks and adaptive network-based fuzzy inference system with different partitioning methods, were used. The reliability of applied method was considered by analyzing and validating the predicted turbine energy parameters with the results obtained in the highly sophisticated laboratory.", publisher = "Springer London Ltd, London", journal = "Neural Computing & Applications", title = "Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine", pages = "2579-2569", number = "8", volume = "30", doi = "10.1007/s00521-017-2843-9" }
Jovanović, R.,& Božić, I.. (2018). Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine. in Neural Computing & Applications Springer London Ltd, London., 30(8), 2569-2579. https://doi.org/10.1007/s00521-017-2843-9
Jovanović R, Božić I. Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine. in Neural Computing & Applications. 2018;30(8):2569-2579. doi:10.1007/s00521-017-2843-9 .
Jovanović, Radiša, Božić, Ivan, "Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine" in Neural Computing & Applications, 30, no. 8 (2018):2569-2579, https://doi.org/10.1007/s00521-017-2843-9 . .