The Dangerous Feature Identification of Transmission Line Galloping Under Multi-channel Modal State

Abstract

A rough set combined with an ant colony algorithm is used to extract the non-forced vibration signal from the measured transmission line galloping displacement signal, which is used as the risk feature recognition index. In the real model test, the transmission line's six split line segment shift time history is measured across multiple channels to identify the galloping frequency, vibration mode, and damping ratio of the transmission line. At the same time, MATLAB simulation is employed to verify the algorithm's accuracy and computation time. The results show that the accuracy of the proposed algorithm in identifying the galloping frequency, vibration mode, and damping ratio is 98%, 97.6%, and 98.5%, respectively, with calculation times of 16s, 18s, and 17.5s. Therefore, in multi-channel modal analysis, a rough set combined with the ant colony algorithm can effectively address the problem of dangerous feature identification of transmission line galloping.