The use of PEANO for on-line monitoring of fossil power plants

This paper describes the results of the use of a combined approach of artificial neural network and fuzzy logic, implemented in the computer code PEANO, to the on-line monitoring of the steam-water cycle of a 320 MW fossil plant in Italy. First, a short review of the underlying theory is reported. Then some results are illustrated of data pre-processing, aimed at selecting the appropriate data and to address the neural networks architecture. Finally the simulation of continuous monitoring is documented and data reconciliation capability of the code is discussed in some detail. These results demonstrate that the approach provided by PEANO is very effective to validate measured signals and to track a process on-line, giving the plant operator an immediate insight of the evolution of a possible fault in sensors or system components. (Author)