Automatic modeling of a gas turbine using genetic programming: An experimental study
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{EnriquezZarate:2017:ASC,
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author = "Josue Enriquez-Zarate and Leonardo Trujillo and
Salvador {de Lara} and Mauro Castelli and
Emigdio Z-Flores and Luis Munoz and Ales Popovic",
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title = "Automatic modeling of a gas turbine using genetic
programming: An experimental study",
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journal = "Applied Soft Computing",
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year = "2017",
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volume = "50",
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month = jan,
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pages = "212--222",
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keywords = "genetic algorithms, genetic programming, Gas turbine,
Data-driven modeling, Local search",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2016.11.019",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494616305889",
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sizze = "11 pages",
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abstract = "This work deals with the analysis and prediction of
the behavior of a gas turbine (GT), the Mitsubishi
single shaft Turbo-Generator Model MS6001, which has a
30 MW generation capacity. GTs such as this are of
great importance in industry, as drivers of gas
compressors for power generation. Because of their
complexity and their execution environment, the failure
rate of GTs can be high with severe consequences. These
units are subjected to transient operations due to
starts, load changes and sudden stops that degrade the
system over time. To better understand the dynamic
behavior of the turbine and to mitigate the
aforementioned problems, these transient conditions
need to be analyzed and predicted. In the absence of a
thermodynamic mathematical model, other approaches
should be considered to construct representative models
that can be used for condition monitoring of the GT, to
predict its behavior and detect possible system
malfunctions. One way to derive such models is to use
data-driven approaches based on machine learning and
artificial intelligence. This work studies the use of
state-of-the-art genetic programming (GP) methods to
model the Mitsubishi single shaft Turbo-Generator. In
particular, we evaluate and compare variants of GP and
geometric semantic GP (GSGP) to build models that
predict the fuel flow of the unit and the exhaust gas
temperature. Results show that an algorithm, proposed
by the authors, that integrates a local search
mechanism with GP (GP-LS) outperforms all other
state-of-the-art variants studied here on both
problems, using real-world and representative data
recorded during normal system operation. Moreover,
results show that GP-LS outperforms seven other
modeling techniques, including neural networks and
isotonic regression, confirming the importance of
GP-based algorithms in this domain.",
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notes = "Cites \cite{Martinez-Arellano:2014:UKSim}",
- }
Genetic Programming entries for
Josue Enriquez-Zarate
Leonardo Trujillo
Salvador de Lara
Mauro Castelli
Emigdio Z-Flores
Luis Munoz Delgado
Ales Popovic
Citations