Neuro-fuzzy modeling of superheating system of a steam power plant

In this paper superheating system of a 325MW steam power plant is modeled based on the recurrent neuro-fuzzy 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.