Hybrid intelligent monironing systems for thermal power plant trips

Nader Barsoum, Firas Basim Ismail

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Steam boiler is one of the main equipment in thermal power plants. If the steam boiler trips it may lead to entire shutdown of the plant, which is economically burdensome. Early boiler trips monitoring is crucial to maintain normal and safe operational conditions. In the present work two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MATLAB environment. The training and validation of the two systems has been performed using real operational data captured from the plant control system of selected power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables has been proposed for IMSs data analysis. The first IMS represents the use of pure Artificial Neural Network system for boiler trip detection. All seven boiler trips under consideration have been detected by IMSs before or at the same time of the plant control system. The second IMS represents the use of Genetic Algorithms and Artificial Neural Networks as a hybrid intelligent system. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables performed successfully by the hybrid intelligent system.

Original languageEnglish
Title of host publicationProceedings of the Sixth Global Conference on Power Control and Optimization
Pages428-433
Number of pages6
DOIs
Publication statusPublished - 01 Dec 2012
Event6th Global Conference on Power Control and Optimization, PCO 2012 - Las Vegas, NV, United States
Duration: 06 Aug 201208 Aug 2012

Publication series

NameAIP Conference Proceedings
Volume1499
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other6th Global Conference on Power Control and Optimization, PCO 2012
CountryUnited States
CityLas Vegas, NV
Period06/08/1208/08/12

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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  • Cite this

    Barsoum, N., & Ismail, F. B. (2012). Hybrid intelligent monironing systems for thermal power plant trips. In Proceedings of the Sixth Global Conference on Power Control and Optimization (pp. 428-433). (AIP Conference Proceedings; Vol. 1499). https://doi.org/10.1063/1.4769025