TY - JOUR

T1 - Drone-based large-scale particle image velocimetry applied to tidal stream energy resource assessment

AU - Fairley, Ian

AU - Williamson, Benjamin

AU - McIlvenny, Jason

AU - King, Nicholas

AU - Masters, Ian

AU - Lewis, Matthew

AU - Neill, Simon

AU - Glasby, David

AU - Coles, Daniel

AU - Powell, Ben

AU - Naylor, Keith

AU - Robinson, Max

AU - Reeve, Dominic E.

PY - 2022/8/1

Y1 - 2022/8/1

N2 - 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.

AB - 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.

KW - Ocean energy

KW - Oceanography

KW - Remote sensing

KW - Resource mapping

KW - Surface velocimetry

KW - Unmanned aerial vehicles

U2 - 10.1016/j.renene.2022.07.030

DO - 10.1016/j.renene.2022.07.030

M3 - Article

VL - 196

SP - 839

EP - 855

JO - Renewable Energy

JF - Renewable Energy

SN - 0960-1481

ER -