Response Characteristics Prediction of Surge Protective Device Based on NARX Neural Network

Abstract

The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic neural architecture, which is commonly used for input–output modeling of nonlinear dynamical systems. Due to the lack of accurate mathematical model for the performance analysis on surge protective devices (SPDs), with the input of fast rising time electromagnetic pulse (FREMP), the NARX neural network is employed to predict the response characteristics of SPD in this paper. In order to verify the feasibility of this method, SPD test system is set up according to IEC 61000-4-24. The results show that the response curve estimated by the proposed model is in good agreement with experimental results, especially the waveform parameters such as response time, pulse peak, and residual voltage. The good forecasting performance of the network suggests that the NARX model used in this paper has good generalization ability. Moreover, with less measured data it can predict the response of the SPD under different voltage levels that were not yet measured.