CHEN Libing, LIU Xiaohui, WU Chuan, YE Zhongfei, ZHANG Bo
The galloping of transmission lines seriously threatens the safety and stability of power system. It is particularly important to accurately establish a galloping model of iced conductor. Therefore, a data-driven sparse identification algorithm is adopted, which can effectively identify the control equations of the dynamic system from the data and reduce the calculation cost. In this paper, the galloping equation of the iced quad bundle conductors is identified based on the data-driven sparse recognition algorithm. Firstly, based on Hamilton's principle, the partial differential galloping equation of the iced quad bundle conductors is derived. Then, the partial differential equation is converted into an ordinary differential equation via the Galerkin method. Next, the aerodynamic load model is established, and the aerodynamic force is introduced into the galloping equation, obtaining the final ordinary differential galloping equation of the iced quad bundle conductors. Finally, the equation data are obtained by numerical simulation and the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is used to conduct the sparse identification. The results show that the SINDy algorithm can accurately identify galloping equation of the iced quad bundle conductors from the noise-free data. When the noise amplitude of the data derivative is below 0.2, the number of terms of the galloping equation can be correctly identified and the mean error of coefficients is within 0.22 %. Additional terms start to be identified when the noise amplitude of data derivative is above 0.3, which leads to a significant increase in the maximum relative error of the coefficients of the identified cubic nonlinear higher-order terms , reaching 58.78 % at a noise amplitude of 0.5. However, the maximum deviation of the final identified galloping amplitude is only 2.1 %. The identification accuracy of the model can be improved by updating the galloping model through adjusting the sparsity promotion parameter and the time step, which proves the strong adaptability of the SINDy algorithm to the data derivative noise. The results of this paper can provide a certain reference for the establishment of the transmission line galloping model.