Distributed Acoustic Sensing of Wind Turbine Towers for Structural Health Monitoring

Researchers: Peter Hubbard, James Xu, Matthew Dejong, Kenichi Soga (UC Berkeley), Linqing Luo (LBNL), Shenghan Zhang (HKUST)

Client/Owner: Enel Green Power, Enel Foundation

Technologies: Distributed Strain and Acoustic Sensing


Hubbard PG, Xu J, Zhang S, DeJong MJ, Luo L, Soga K, Papa C, Zulberti C, Malara D, Fugazzotto F, Lopez FG, Minto C (2021) Dynamic Structural Health Monitoring of a Model Wind Turbine Tower using Distributed Acoustic Sensing (DAS), Journal of Civil Structural Health Monitoring. DOI: 10.1007/s13349-021-00483-y

UC Berkeley researchers have been working with Enel Green Power and the Enel Foundation to identify promising new technologies for monitoring the wind farms of the future. Distributed Acoustic Sensing (DAS) is a technology that can measure dynamic strain at meter scale over 10s of kilometers. For these reasons, it is an attractive technology for wind farm operators to leverage for monitoring many turbines at the same time with a single sensing system. The idea was tested at a laboratory scale in 2020 at UC Berkeley’s Richmond Field Station. The test was a success, and demonstrated the potential for DAS to monitor wind turbine tower structures.

Enel Green Power has funded a full-scale implementation at the Rocky Ridge Wind Farm in Oklahoma. UC Berkeley researchers will instrument two active wind turbines and monitor them using several distributed fiber optic sensing technologies, including DAS and a proprietary distributed strain sensing system that was developed by the Soga Research Group. The installation is planned for Summer 2022 and monitoring will go on for a year of evaluation.