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Artificial Neural Networks for Gas Turbine Modeling and Sensor Validation

Fast, Magnus (2006)
Department of Energy Sciences
Abstract
The aim of this collaboration, between the division of Thermal Power Engineering

and Lunds Energi AB, is to investigate the possibilities of training artificial neural

networks (ANNs) with power plant operational data. For this purpose operational

data from Lunds Energi's gas turbine GT10B with heat recovery unit (HRU) will be

used. Furthermore a model with user interface is created to demonstrate the

possibilities of using ANN. The results are evaluated through feedback from Lunds

Energi and many different areas of implementation are considered.

ANN differs from conventional mathematical models in the sense that they are

trained rather than programmed. During training, data is presented in an iterative

manner in order to find the... (More)
The aim of this collaboration, between the division of Thermal Power Engineering

and Lunds Energi AB, is to investigate the possibilities of training artificial neural

networks (ANNs) with power plant operational data. For this purpose operational

data from Lunds Energi's gas turbine GT10B with heat recovery unit (HRU) will be

used. Furthermore a model with user interface is created to demonstrate the

possibilities of using ANN. The results are evaluated through feedback from Lunds

Energi and many different areas of implementation are considered.

ANN differs from conventional mathematical models in the sense that they are

trained rather than programmed. During training, data is presented in an iterative

manner in order to find the relation between selected inputs and outputs. After the

network is trained the weights, i.e. the parameters containing the network

information, are locked and if the network is presented with new, before unseen, data

it is able to predict new outputs.

The software used for modeling ANNs is called NeuroSolutions. The resulting

networks have been processed in Visual Basic for final use in Excel.

Thru these studies several ANN models have been produced, both models of the gas

turbine (GT) and models for sensor validation (SV). The results have been

promising, e.g. with networks demonstrating high performance predictability. (Less)
Please use this url to cite or link to this publication:
author
Fast, Magnus
supervisor
organization
year
type
H1 - Master's Degree (One Year)
subject
keywords
ANN, Gas turbine, Sensor validation, Energy research, Energiforskning
language
English
id
1329348
date added to LUP
2006-09-27
date last changed
2006-09-27
@misc{1329348,
  abstract     = {The aim of this collaboration, between the division of Thermal Power Engineering

and Lunds Energi AB, is to investigate the possibilities of training artificial neural

networks (ANNs) with power plant operational data. For this purpose operational

data from Lunds Energi's gas turbine GT10B with heat recovery unit (HRU) will be

used. Furthermore a model with user interface is created to demonstrate the

possibilities of using ANN. The results are evaluated through feedback from Lunds

Energi and many different areas of implementation are considered.

ANN differs from conventional mathematical models in the sense that they are

trained rather than programmed. During training, data is presented in an iterative

manner in order to find the relation between selected inputs and outputs. After the

network is trained the weights, i.e. the parameters containing the network

information, are locked and if the network is presented with new, before unseen, data

it is able to predict new outputs.

The software used for modeling ANNs is called NeuroSolutions. The resulting

networks have been processed in Visual Basic for final use in Excel.

Thru these studies several ANN models have been produced, both models of the gas

turbine (GT) and models for sensor validation (SV). The results have been

promising, e.g. with networks demonstrating high performance predictability.},
  author       = {Fast, Magnus},
  keyword      = {ANN,Gas turbine,Sensor validation,Energy research,Energiforskning},
  language     = {eng},
  note         = {Student Paper},
  title        = {Artificial Neural Networks for Gas Turbine Modeling and Sensor Validation},
  year         = {2006},
}