Multiscale subspace identification of nuclear reactor using wavelet basis function

This paper introduces a methodology of multiscale system identification using wavelet basis functions. Being specific, it deals with subspace identification, for approximation of any multiscale process by employing a number of linear time invariant models at different scales. The idea is to estimate low dimensional state-space models in projection space at appropriate scales. The efficacy of proposed approach has been demonstrated by modeling a nuclear reactor coupled with thermal hydraulics in prediction as well as in simulation framework. Outcome of the multiscale subspace modeling approach is compared with that in single scale to bring out the advantage of the proposed method at different signal to noise ratios. In case of multiscale subspace process identification, the mean squared error in output prediction is found to be small, which suggests improvement in modeling.