Wind turbine blade health monitoring using acoustic beamforming techniques

Abstract

Wind turbines operate autonomously and can possess reliability issues attributed to manufacturing defects, fatigue failure, or extreme weather events. In particular, wind turbine blades can suffer from leading and trailing edge splits, holes, or cracks that can lead to blade failure and loss of energy revenue generation. In order to help identify damage, several approaches have been used to detect cracks in wind turbine blades; however, most of these methods require transducers to be mounted on the turbine blades, are not effective, or require visual inspection. This paper will propose a new methodology of the wind turbine non-contact health monitoring using the acoustic beamforming techniques. By mounting an audio speaker inside of the wind turbine blade, it may be possible to detect cracks or damage within the structure by observing the sound radiated from the blade. Within this work, a phased array beamforming technique is used to process acoustic data for the purpose of damage detection. Several algorithms are evaluated including the CLEAN-based Subtraction of Point spread function from a Reference (CLSPR) on a composite panel and a section of a wind turbine blade in the laboratory.