Bayesian uncertainty quantification of modal parameters and RRF identification of transmission towers with limited measured vibration data

Abstract

The resonant response factor (RRF) sensitive to modal parameters, including the damping ratio and frequency, is a critical parameter for wind-resistant design of extra high voltage (EHV) transmission towers, because it directly affects the calculation of the wind vibration coefficient. However, the research on operational modal analysis (OMA) of in-service EHV transmission towers is limited due to the high risk of field monitoring. Therefore, it is critical to determine the size of sufficient data samples or minimum monitoring duration for the OMA of a transmission tower. In this study, an investigation of uncertainty quantification in Bayesian OMA of EHV transmission towers with small data samples from the ambient dynamic test is carried out. The uncertainty of the resonant component factor for wind-resistance design is proposed and quantified, which could provide engineering designers with a reference for confidence levels. Firstly, a field test system is installed on a multipurpose EHV transmission tower with a closed-loop cat head and a pair of cross-arms, to gather the acceleration responses. Then, the OMA for the transmission tower is carried out by the fast Bayesian FFT (FBFFT) method to determine the modal parameters as well as the associated posterior uncertainty. The small sample analysis of OMA for the transmission tower is further performed to obtain sufficient data samples and minimum monitoring duration. Finally, the distribution of the RRF is presented with the different confidential levels. It is found that a sampling length of 600 s could fulfill the basic needs of accuracy in Bayesian OMA and the determination of RRF. The outcomes of this study may provide a reference for the wind-resistance design parameter selection of the similar type of transmission towers.