Preliminary communication

https://doi.org/10.17559/TV-20190510052210

MLP ANN Condition Assessment Model of the Turbogenerator Shaft A6 HPP Đerdap 2

Dragoljub Ilić ; Aviation Academy, Bulevar vojvode Bojovića 2, 11000 Belgrade, Serbia
Dragan Milošević ; University Business Academy in Novi Sad, Faculity of Economics and Engineering Management in Novi Sad, Cvećarska 2, 21000 Novi Sad, Serbia
Zoran Jovanović ; College "Dositej", Bulevar vojvode Putnika 7, 11000 Belgrade, Serbia
Milena Cvjetković ; College "Dositej", Bulevar vojvode Putnika 7, 11000 Belgrade, Serbia
Miroslav Vulić* ; University Business Academy in Novi Sad, Faculity of Economics and Engineering Management in Novi Sad, Cvećarska 2, 21000 Novi Sad, Serbia

Abstract

This paper describes a model for estimating the condition of the shafts of turbines of the current generator in Hydropower plant Đerdap 2. For this purpose, an integral diagnostic approach was used. Based on the diagnostics of the condition of the shaft and the estimated lifetime, a multi-layer perceptron (MLP) based artificial neural network (ANN) is built, which is able to estimate the remaining lifespan of the turbine shaft. The MLP ANN model has not been made in this way on turbogenerators of hydroelectric power plant Đerdap 2 until now. The significance of this approach is that experiment brings about topology of ML ANN (number of neurons and layers) which is optimal for this model, training and testing. Results obtained from the neural network can be further used for decision-making about the moment of diagnosis or maintenance actions, as well as reducing stagnation and production losses.

Keywords

ANN (Artificial Neural Network); model; shaft; turbogenerator

Hrčak ID:

251773

URI

https://hrcak.srce.hr/251773

Publication date:

5.2.2021.

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