Growing concerns about climate change have led to an increased interest and investment in the development and deployment of low carbon technologies.
Wind farms are now ubiquitous as a sustainable method for harvesting wind energy. Due to the availability of adequate area and levels of nuisance to human environment, these are commonly deployed in remote sites and off-shore locations where monitoring and maintenance of units is typically costly. Remote acoustic monitoring presents advantages that could provide an efficient solutions. An independent and remote system, i.e not mounted on the turbine, is able to monitor various elements of a turbine and detect various types of fault from the sound emitted by the turbines. This PhD project aims to research methods to extract a wind turbine’s acoustic signature and, by advanced analysis, determine the condition of its various components. Signal detection and analysis techniques will be a starting point, although more advanced techniques such as blind source separation and machine learning methods will also be part of the doctoral research.