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

Iain Fairley, Benjamin J. Williamson, Jason McIlvenny, Nicholas King, Ian Masters, Matthew Lewis, Simon Neill, David Glasby, Daniel Coles, Ben Powell, Keith Naylor, Max Robinson, Dominic E. Reeve

Research output: Contribution to journalArticlepeer-review

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 (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.

Original languageEnglish
Pages (from-to)839-855
Number of pages17
JournalRenewable Energy
Volume196
DOIs
Publication statusPublished - 8 Jul 2022

Keywords

  • Ocean energy
  • Oceanography
  • Remote sensing
  • Resource mapping
  • Surface velocimetry
  • Unmanned aerial vehicles

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