Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm
Publication: Journal of Energy Engineering
Volume 139, Issue 4
Abstract
A parameter identification method based on genetic algorithm (GA) is presented to solve the multimodel parameters identification problem for the main steam temperature of ultra-supercritical (USC) units. Linear ranking selection, nonuniform linear crossover, and Gaussian mutation are employed in the algorithm design to enhance the convergence speed and the accuracy of the identification. Besides, the uniform design method is executed to initialize the population and the sigmoid function with adaptation is applied to adjust the probabilities of crossover and mutation. Simulations carried out with the field operation data from Haimen USC units, including two processes—parameters identification and model verification. The simulation results show that the improved genetic algorithm performs well in global parameters searching and the proposed identification methodology offers good results for the multimodel parameters identification.
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Information
Published In
Journal of Energy Engineering
Volume 139 • Issue 4 • December 2013
Pages: 290 - 298
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jul 20, 2011
Accepted: Nov 1, 2012
Published online: Nov 3, 2012
Discussion open until: Apr 3, 2013
Published in print: Dec 1, 2013
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