Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization from Scanning Lidars

Jeanie Aird, Eliot Quon, Rebecca Barthelmie, Sara Pryor

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations


A convolutional neural network is applied to lidar scan images from three experimental campaigns to identify and characterize wind turbine wakes. Initially developed as a proof-of-concept model and applied to a single data set in complex terrain, the model is now improved and generalized and applied to two other unique lidar data sets, one located near an escarpment and one located offshore. The model, initially developed using lidar scans collected in predominantly westerly flow, exhibits sensitivity to wind flow direction. The model is thus successfully generalized through implementing a standard rotation process to scan images before input into the convolutional neural network to ensure the flow is westerly. The sample size of lidar scans used to train the model is increased, and along with the generalization process, these changes to the model are shown to enhance accuracy and robustness when characterizing dissipating and asymmetric wakes. Applied to the offshore data set in which nearly 20 wind turbine wakes are included per scan, the improved model exhibits a 95% success rate in characterizing wakes and a 74% success rate in characterizing dissipating wake fragments. The improved model is shown to generalize well to the two new data sets, although an increase in wake characterization accuracy is offset by an increase in model sensitivity and false positive wake identifications.

Original languageAmerican English
Article numberArticle No. 032077
Number of pages11
JournalJournal of Physics: Conference Series
Issue number3
StatePublished - 2 Jun 2022
Event2022 Science of Making Torque from Wind, TORQUE 2022 - Delft, Netherlands
Duration: 1 Jun 20223 Jun 2022

Bibliographical note

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

NREL Publication Number

  • NREL/JA-5000-81957


  • condition monitoring
  • image processing
  • neural network
  • wake characterization
  • wake identification
  • wake steering
  • wind energy
  • wind turbine


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