Data-driven sparse identification of galloping model of iced quad bundle conductors

Abstract

The galloping of iced conductors is a serious threat to the safe operation of power systems. Establishing an accurate galloping model of iced conductors has always been a difficult point in galloping research. Therefore, the sparse identification of nonlinear dynamics (SINDy) algorithm is used to identify the galloping model from noise measurement data. A theoretical model of galloping of iced quad bundle conductors is established. Meanwhile, the algorithm is used to identify the simulated data of the theoretical model. The parameter identification ability of the algorithm under noisy velocity measurement is analyzed. An excellent denoising method was selected for data preprocessing, and then the model identification effect of the algorithm after data preprocessing is studied. Besides, the accuracy of the prediction model based on this algorithm and the support vector regression (SVR) prediction model under different training data lengths are compared. The results show that the model identified by the SINDy algorithm in the noise measurement data after data preprocessing has high accuracy and robustness. Moreover, the amount of data used is small. The model identified by this algorithm plays an important role in the rapid investigation, prediction and early warning of galloping phenomena.