MATEC Web Conf.
Volume 131, 2017UTP-UMP Symposium on Energy Systems 2017 (SES 2017)
|Number of page(s)||6|
|Section||Energy management and conservation|
|Published online||25 October 2017|
Hybrid Intelligent Warning System for Boiler tube Leak Trips
1 Power Generation, Institute of Power Engineering (IPE), Universiti Tenaga Nasional, 43000, Kajang, Selangor, Malaysia
2 Mechanical Engineering Department, Universiti Teknologi Petronas, 32610, Tronoh, Malaysia
* Corresponding author: firstname.lastname@example.org
Repeated boiler tube leak trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. In this study two artificial intelligent monitoring systems specialized in boiler tube leak trips have been proposed. The first intelligent warning system (IWS-1) represents the use of pure artificial neural network system whereas the second intelligent warning system (IWS-2) represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. The Extreme Learning Machine (ELM) methodology was also adopted in IWS-1 and compared with traditional training algorithms. Genetic algorithm (GA) was adopted in IWS-2 to optimize the ANN topology and the boiler parameters. An integrated data preparation framework was established for 3 real cases of boiler tube leak trip based on a thermal power plant in Malaysia. Both the IWSs were developed using MATLAB coding for training and validation. The hybrid IWS-2 performed better than IWS-1.The developed system was validated to be able to predict trips before the plant monitoring system. The proposed artificial intelligent system could be adopted as a reliable monitoring system of the thermal power plant boilers.
© The authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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