Closed loop identification of nuclear steam generator water level using esn network tuned by genetic algorithm

Abstract

The behavior of Steam Generators Water Level from nuclear power plants is highly nonlinear. Its parameters change when facing different operational conditions. Simulating this system can be very useful to train people involved in the real plant operation. However, in order to simulate this system the identification process must be performed. Echo State Networks are a special type of Recurrent Neural Networks that are well suited to nonlinear dynamic systems identification, with the advantage of having a simpler and faster training algorithm than conventional Recurrent Neural Networks. Echo State Networks have an additional advantage over other conventional methods of dynamic systems identification, since it is not necessary to specify the model’s structure. However, some other parameters of the Echo State Network must be tuned in order to attain its best performance. Therefore, this study proposes the use of an Echo State Network, automatically tuned by Genetic Algorithms, to a closed loop identification of a nuclear steam generator water level process. The results obtained demonstrate that the proposed Echo State Networks can correctly model dynamical nonlinear system in a large range of operation.