A hammerstein-type fourier neural network-based identification with application to transformer vibration system modelling

This paper aims to construct an appropriate model to represent the nonlinear transformer vibration system, with electric applies as the system inputs and the vibration response on the transformer tank as the outputs. A single-input and single-output (SISO) Hammerstein-type model is developed for identifying the nonlinear transformer vibration system, when the observed vibration on the transformer tank is derived into two components contributed by the individual vibration sources. The nonlinear system is identified by the Fourier neural network, which consists of a nonlinear element and a linear dynamic block. The order determination method based on the Lipschitz criterion as well as the back-propagation algorithm for weights update are both presented. The Hammerstein-type Fourier neural networkbased model is tested on a transformer, giving promising results for prediction of the transformer vibration.