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

Abstract

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.