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Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment
Renewable Energy, Volume: 196, Pages: 839 - 855
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Resource quantification is vital in developing a tidal stream energy site but challenging in high energy areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide a novel, low cost, low risk approach that improves spatial coverage compared to ADCP methods. For the first time, thi...
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Resource quantification is vital in developing a tidal stream energy site but challenging in high energy areas. Drone-based large-scale particle image velocimetry (LSPIV) may provide a novel, low cost, low risk approach that improves spatial coverage compared to ADCP methods. For the first time, this study quantifies performance of the technique for tidal stream resource assessment, using three sites. Videos of the sea surface were captured while concurrent validation data were obtained (ADCP and surface drifters). Currents were estimated from the videos using LSPIV software. Variation in accuracy was attributed to wind, site geometry and current velocity. Root mean square errors (RMSEs) against drifters were 0.44 m s−1 for high winds (31 kmh) compared to 0.22 m s−1 for low winds (10 kmh). Better correlation was found for the more constrained site (r2 increased by 4%); differences between flood and ebb indicate the importance of upstream bathymetry in generating trackable surface features. Accuracy is better for higher velocities. A power law current profile approximation enables translation of surface current to currents at depth with satisfactory performance (RMSE = 0.32 m s−1 under low winds). Overall, drone video derived surface velocities are suitably accurate for “first-order” tidal resource assessments under favourable environmental conditions.
ocean energy; resource mapping; unmanned aerial vehicles; surface velocimetry; oceanography; remote sensing
College of Engineering
The authors would like to acknowledge the financial support of the EPSRC Supergen ORE Hub (EP/S000747/1) funded V-SCORES project. The financial support of the Selkie Project is also acknowledged. The Selkie Project is funded by the European Regional Development Fund through the Ireland Wales Cooperation programme. We also acknowledge the support of SEEC (Smart Efficient Energy Centre) at Bangor University, part-funded by the European Regional Development Fund (ERDF), administered by the Welsh Government. M Lewis also wishes to acknowledge the EPSRC fellowship METRIC: EP/R034664/1. D Coles acknowledges the financial support of the EPSRC fellowship which is co-financed by the European Regional Development Fund through the Interreg France (Channel) England Programme.