Application of artificial neural network to the condition monitoring and diagnosis of a CHP plant
(2008) ECOS 2008. 21st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems 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:
https://lup.lub.lu.se/record/1171729
- author
- Fast, Magnus LU ; Assadi, Mohsen LU and De, Sudipta
- organization
- publishing date
- 2008
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- artificial neural network, combined heat- and power plant, condition monitoring
- host publication
- [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
- conference location
- Krakow, Poland
- conference dates
- 0001-01-02
- 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
- 2016-04-04 10:10:40
- date last changed
- 2022-01-29 19:53:44
@inproceedings{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}},
booktitle = {{[Host publication title missing]}},
editor = {{Ziębik, Andrzej and Kolenda, Zygmunt and Stanek, Wojciech Stanek}},
isbn = {{978-83-922381-4-0}},
keywords = {{artificial neural network; combined heat- and power plant; condition monitoring}},
language = {{eng}},
pages = {{981--988}},
publisher = {{ECOS}},
title = {{Application of artificial neural network to the condition monitoring and diagnosis of a CHP plant}},
year = {{2008}},
}