Neuro-fuzzy modeling of superheating system of a steam power plant
Authors: 
A. R. Mehrabian, A. Yousefi-Koma, M. Mohammad-Zaheri, A. Ghaffari, D. MehrabiAuthors Info & Claims
Pages 347 - 352
Abstract
In this paper superheating system of a 325MW steam power plant is modeled based on the recurrent neurofuzzy networks and subtractive clustering. The experimental data are obtained from a complete set of field experiments under various operating conditions. Nine neuro-fuzzy models are constructed and trained for seven subsystems of the superheating unit. Then, these nine fuzzy models are put together merging series and parallel units according to the real power plant subsystems, to obtain the global model of the superheating process. Comparing the time response of the nonlinear neuro-fuzzy model of a subsystem with the time response of its linear model based on the Least Square Error (LSE) method, indicates that the nonlinear neurofuzzy model is more accurate and reliable than the linear model in the sense that its response is closer to the response of the actual superheating system.
References
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{10} A. Ghaffari, A. R. Mehrabian, M. Mohammad-Zaheri, Identification and Control of Power Plant De-Superheater Using Soft Computing Techniques, submitted to Engineering Applications of Artificial Intelligence, Special Issue on Artificial Intelligence Applications in Process Control.
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{11} A. R. Mehrabian, M. Mohammad-Zaheri, A. Yousefi-Koma, Design of a Genetic-Algorithm-Based Steam Temperature Controller in Thermal Power Plants, submitted to IEEE International Conference on Engineering of Intelligent Systems, ICEIS'2006.
Index Terms
- Neuro-fuzzy modeling of superheating system of a steam power plant
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ACTA Press
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Published: 13 February 2006
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