Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment. (August 2022)

• Record Type: Journal Article
• Authors: Fairley, Iain; Williamson, Benjamin J.; McIlvenny, Jason; King, Nicholas; Masters, Ian; Lewis, Matthew; Neill, Simon; Glasby, David; Coles, Daniel; Powell, Ben; Naylor, Keith; Robinson, Max; Reeve, Dominic E.
• Is Part Of: Renewable energy. Volume 196(2022)
• Page Start: 839
• Page End: 855
• Publication Date: 2022-08
• Subjects: Ocean energy -- Resource mapping -- Unmanned aerial vehicles -- Surface velocimetry -- Oceanography -- Remote sensing; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2022.07.030
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: 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 km/h) compared to 0.22 m s −1 for low winds (10 km/h). Better correlation was found for the more constrained site (r 2 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. Highlights: Drones recorded video footage of the water surface at tidal stream energy sites. Synchronous validation data were obtained with ADCPs and surface drifters. Surface currents derived from video using LSPIV were compared to in-situ data. Method is sufficiently accurate for initial tidal stream site resource assessment. Approach also suitable for pollution tracking and other rapid response incidents.