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Application of artificial neural network to the condition monitoring and diagnosis of a CHP plant

Fast, Magnus LU ; Assadi, Mohsen LU and De, Sudipta (2008) ECOS 2008. 21st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems In [Host publication title missing] p.981-988
Abstract
The objective of this study has been to create an online system for condition monitoring and diagnosis for a combined heat- and power plant in Sweden. This system consists of artificial neural network models, representing each main component of the combined heat- and power plant, accompanied with a graphical user interface. The artificial neural network models are integrated on a power generation information manager server in the combined heat- and power plant computer system and the graphical user interface is made available on workstations connected to this server. The artificial neural network models have been constructed with the multi-layer feed-forward network type and trained with operational data from the combined

heat-... (More)
The objective of this study has been to create an online system for condition monitoring and diagnosis for a combined heat- and power plant in Sweden. This system consists of artificial neural network models, representing each main component of the combined heat- and power plant, accompanied with a graphical user interface. The artificial neural network models are integrated on a power generation information manager server in the combined heat- and power plant computer system and the graphical user interface is made available on workstations connected to this server. The artificial neural network models have been constructed with the multi-layer feed-forward network type and trained with operational data from the combined

heat- and power plant using back-propagation. The plant consists of a Siemens gas turbine with a heat recovery steam generator and a bio fuelled boiler and its steam cycle. Steam from the heat recovery steam generator and the bio fuelled boiler expands together in one common steam turbine, producing both electricity and heat. Each component, i.e. gas turbine, heat recovery steam generator, bio fuelled boiler and steam turbine, are modelled separately with artificial neural network. To ensure accurate predictions from the models a baseline is established after which all training data is collected. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
artificial neural network, combined heat- and power plant, condition monitoring
in
[Host publication title missing]
editor
Ziębik, Andrzej; Kolenda, Zygmunt and Stanek, Wojciech Stanek
pages
8 pages
publisher
ECOS
conference name
ECOS 2008. 21st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
external identifiers
  • Scopus:84924301659
ISBN
978-83-922381-4-0
language
English
LU publication?
yes
id
b29a07db-629d-473b-a4ae-f14da65df20c (old id 1171729)
date added to LUP
2008-08-04 10:16:49
date last changed
2016-10-13 04:38:30
@misc{b29a07db-629d-473b-a4ae-f14da65df20c,
  abstract     = {The objective of this study has been to create an online system for condition monitoring and diagnosis for a combined heat- and power plant in Sweden. This system consists of artificial neural network models, representing each main component of the combined heat- and power plant, accompanied with a graphical user interface. The artificial neural network models are integrated on a power generation information manager server in the combined heat- and power plant computer system and the graphical user interface is made available on workstations connected to this server. The artificial neural network models have been constructed with the multi-layer feed-forward network type and trained with operational data from the combined<br/><br>
heat- and power plant using back-propagation. The plant consists of a Siemens gas turbine with a heat recovery steam generator and a bio fuelled boiler and its steam cycle. Steam from the heat recovery steam generator and the bio fuelled boiler expands together in one common steam turbine, producing both electricity and heat. Each component, i.e. gas turbine, heat recovery steam generator, bio fuelled boiler and steam turbine, are modelled separately with artificial neural network. To ensure accurate predictions from the models a baseline is established after which all training data is collected.},
  author       = {Fast, Magnus and Assadi, Mohsen and De, Sudipta},
  editor       = {Ziębik, Andrzej and Kolenda, Zygmunt and Stanek, Wojciech Stanek},
  isbn         = {978-83-922381-4-0},
  keyword      = {artificial neural network,combined heat- and power plant,condition monitoring},
  language     = {eng},
  pages        = {981--988},
  publisher    = {ARRAY(0x91eff50)},
  series       = {[Host publication title missing]},
  title        = {Application of artificial neural network to the condition monitoring and diagnosis of a CHP plant},
  year         = {2008},
}