Nonlinear modeling of complex large-scale plants using neural networks and stochastic approximation

This paper deals with a general methodology for system greybox identification. As is well-known, the tuning of accurate models of real plants (obtained, for instance, by using the physical knowledge of the plants and the technicians´ expertise), on the basis of the measures provided by the available sensors, remains a challenge. In this paper, a tuning methodology for complex large-scale models, is presented. The proposed technique is based on the suitable use of neural networks and specific stochastic-approximation algorithms. It is therefore possible to design a simulator that can be connected in parallel with a real plant, thus providing the plant technician with information about inaccessible variables that are useful for supervision purposes. The proposed methodology is applied to a section of a real 320 MW power plant. Simulation results on the tuning algorithm show the effectiveness of the approach