Abstract
In light of the extended exposure of tidal stream energy generation systems to seawater, which can lead to marine biofouling and subsequent reductions in turbine efficiency, this paper presents a deep learning-based method for the identification of pollutant adhesion. This method aims to rapidly assess the status of turbine pollutant adhesion in tidal energy generation systems. A dataset of adhesion images from tidal stream energy generation system under varying levels of pollution was obtained through underwater experiments with artificial fouling. Three different deep learning algorithms were employed to investigate the enhancement of underwater biofouling image quality. Image segmentation algorithms were used to extract and identify information related to the location and area of biofouling. The results demonstrate that the proposed deep learning-based pollutant adhesion identification method effectively recognizes the adhesion status on turbine blades, improving the accuracy of pollutant identification. This approach provides an efficient and accurate means of pollutant adhesion detection and management for the operation and maintenance of tidal stream energy generation systems, ultimately reducing operational costs.
Published Version
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