Feedforward neural network and ANFIS-based approaches to forecasting the off-cam energy characteristics of Kaplan turbine

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 ins...

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Bibliographic Details
Published in:Neural computing & applications 2018-10, Vol.30 (8), p.2569-2579
Main Authors: Jovanović, Radiša Ž., Božić, Ivan O.
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial Intelligence
Artificial neural networks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Fuzzy systems
Hydraulic turbines
Hydroelectric power
Image Processing and Computer Vision
Laboratories
Model testing
Neural networks
Original Article
Probability and Statistics in Computer Science
Prototype tests
Reliability analysis
Turbines
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-2843-9