Prediction of double-regulated hydraulic turbine on-cam energy characteristics by artificial neural networks approach

Abstract

Određivanje energetskih kombinatorskih karakteristika dvojno regulisane hidraulične turbine se zasniva na rezultatima opsežnih i skupih eksperimentalnih ispitivanja na modelu u laboratoriji i terenskih merenja na prototipu u hidroelektranama. Eksploatacioni dijagram se dobija na osnovu prostornih interpolacija reprezentativnih mernih tačaka koje pripadaju kombinatorskim krivama formiranih za različite brzinske faktore. U radu je dat akcenat na primeni savremene metode veštačkih neuronskih mreža u određivanju kombintorskih karakteristika turbine posebno u radnim režimima koji nisu mereni. Deo postojećih podataka o energetskim parametrima Kaplan turbine koji su dobijeni eksperimentalnim putem iskorišćeni su za obučavanje tri razvijena modela veštačkih neuronskih mreža. Analizom, testiranjem i validacijom dobijenih energetskih parametara turbine međusobnim upoređivanjem sa ostalim eksperimentalnim podacima razmatrana je pouzdanost primenjene metode.

The determination of the energy characteristics of a double-regulated hydro turbine is based on numerous measuring points during extensive and expensive experimental model tests in the laboratory and on site prototype tests at the hydropower plant. By the spatial interpolation of representative measured points that belong to the so-called on-cam curves for different speed factors, the hill performance diagram is obtained. The focus of the paper is the contemporary method of artificial neural network models use for the prediction of turbine characteristics, especially in not measured operation modes. A part of the existing set of experimental data for the Kaplan turbine energy parameters is used to train three developed neural network models. The reliability of applied method is considered by analysing, testing and validating the predicted turbine energy parameters in comparison with the remaining data.