Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant
(2010) 21st International Conference on Efficiency, Cost, Optimization , Simulation and Environmental Impact of Energy Systems 35(2). p.1114-1120- Abstract
- The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server. The plant comprised a Siemens SGT800 gas turbine with a heat recovery steam generator as well as a bio-fueled boiler and its steam cycle. Steam from the heat... (More)
- The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server. The plant comprised a Siemens SGT800 gas turbine with a heat recovery steam generator as well as a bio-fueled boiler and its steam cycle. Steam from the heat recovery steam generator and the bio-fueled boiler expanded together in a common steam turbine, producing both electricity and heat. The artificial neural network models were trained with operational data from the components of the combined heat and power plant. Accurate predictions from the ANN (Artificial neural network) models in combination with an undemanding integration in the power plant's computer system were some of the main conclusions from this study. (C) 2009 Elsevier Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1589633
- author
- Fast, Magnus LU and Palme, T.
- organization
- publishing date
- 2010
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Monitoring, Power plant, Artificial neural network, Diagnosis
- host publication
- Energy
- volume
- 35
- issue
- 2
- pages
- 1114 - 1120
- publisher
- Elsevier
- conference name
- 21st International Conference on Efficiency, Cost, Optimization , Simulation and Environmental Impact of Energy Systems
- conference location
- Cracow, Poland
- conference dates
- 2008-06-24 - 2008-06-27
- external identifiers
-
- wos:000275166900074
- scopus:76449115422
- ISSN
- 1873-6785
- 0360-5442
- DOI
- 10.1016/j.energy.2009.06.005
- language
- English
- LU publication?
- yes
- id
- 31a30bc5-c6c8-46e2-bc8e-db6d476c50ab (old id 1589633)
- date added to LUP
- 2016-04-01 10:36:29
- date last changed
- 2024-11-04 11:25:08
@inproceedings{31a30bc5-c6c8-46e2-bc8e-db6d476c50ab,
abstract = {{The objective of this study has been to create an online system for condition monitoring and diagnosis of a combined heat and power plant in Sweden. The system in question consisted of artificial neural network models, representing each main component of the combined heat and power plant, connected to a graphical user interface. The artificial neural network models were integrated on a power generation information manager server in the computer system of the combined heat and power plant, and the graphical user interface was made available on workstations connected to this server. The plant comprised a Siemens SGT800 gas turbine with a heat recovery steam generator as well as a bio-fueled boiler and its steam cycle. Steam from the heat recovery steam generator and the bio-fueled boiler expanded together in a common steam turbine, producing both electricity and heat. The artificial neural network models were trained with operational data from the components of the combined heat and power plant. Accurate predictions from the ANN (Artificial neural network) models in combination with an undemanding integration in the power plant's computer system were some of the main conclusions from this study. (C) 2009 Elsevier Ltd. All rights reserved.}},
author = {{Fast, Magnus and Palme, T.}},
booktitle = {{Energy}},
issn = {{1873-6785}},
keywords = {{Monitoring; Power plant; Artificial neural network; Diagnosis}},
language = {{eng}},
number = {{2}},
pages = {{1114--1120}},
publisher = {{Elsevier}},
title = {{Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant}},
url = {{http://dx.doi.org/10.1016/j.energy.2009.06.005}},
doi = {{10.1016/j.energy.2009.06.005}},
volume = {{35}},
year = {{2010}},
}