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|Title:||Application of neural networks as an auxiliary technique in the modelling of power station|
|Citation:||AUPEC 2004: Australasian Universities Power Engineering Conference Brisbane, Australia, 2004: www1-6|
|Publisher:||University of Queensland, School of Information Technology & Electrical Engineering|
|Publisher Place:||Brisbane, Qld. Australia|
|Conference Name:||Australasian Universities Power Engineering Conference (14th : 2004 : Brisbane, Australia)|
|Khayam Ghamami and Eric Hu|
|Abstract:||Artificial neural network (NN) is an alternative way (to conventional physical or chemical based modeling technique) to solve complex ill-defined problems. Neural networks trained from historical data are able to handle nonlinear problems and to find the relationship between input data and output data when there is no obvious one between them. Neural Networks has been successfully used in control, robotic, pattern recognition, forecasting areas. This paper presents an application of neural networks in finding some key factors eg. heat loss factor in power station modeling process. In the conventional modeling of power station, these factors such as heat loss are normally determined by experience or “rule of thumb”. To get an accurate estimation of these factors special experiment needs to be carried out and is a very time consuming process. In this paper the neural networks (technique) is used to assist this difficult conventional modeling process. The historical data from a real running brown coal power station in Victoria has been used to train the neural network model and the outcomes of the trained NN model will be used to determine the factors in the conventional energy modeling of the power stations that is under the development as a part of an on-going ARC Linkage project aiming to detail modeling the internal energy flows in the power station.|
|Appears in Collections:||Mechanical Engineering conference papers|
Environment Institute publications
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