Identification and characterization of galloping of Tsuruga test line based on multi-channel modal analysis of field data

Abstract

In spite of several signal processing and system identification techniques, discussion on field-observed galloping of overhead transmission lines is still based on primitive form of field data such as time series, Lissajous diagrams and power spectra. Any form of large amplitude vibration in ice storms is defined as galloping and an attempt has seldom made in identifying whether such vibrations are self-excited modal responses. In doing so, there are always possibilities of misinterpreting gust response as galloping. In this study, a method of multi-channel modal analysis consisting of random decrement method (RDM) and eigensystem realization algorithm (ERA) is proposed to identify galloping, which is self-excited modal response based on a typical field-monitored data of wind-induced vibration of the Tsuruga Test line. RDM was used to transform the field data into non-forced response component, which is similar to free vibration response, and ERA was used to extract modal parameters from the non-forced components. Based on these modal parameters, galloping events were identified, and characteristics of galloping such as coupled translational and rotational motions, and nature of full span vibration, oscillation envelopes and influence of geometry of the line section to its occurrence are discussed. Result of analysis has confirmed well-known mechanism of bundle conductor galloping, which is galloping of bundle transmission lines involves significant coupling of vertical and torsional motions. As for the characteristics of bundle conductor galloping, the most likely galloping mode in deadend span is found as first asymmetric mode and large amplitude of galloping occurs when torsion is in-phase with vertical velocity. Furthermore, it is found that deadend span line section is more prone to galloping than semi-suspension span line section. Finally, performance of proposed method was tested by introducing usual buffeting analysis, and it is confirmed that it has immense potential to identify and characterize galloping based on field data.