Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

Aird, Jeanie A.; Quon, Eliot W.; Barthelmie, Rebecca J.; Debnath, Mithu; Doubrawa, Paula; Pryor, Sara C.

doi:10.3390/rs13214438

Title: Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

Journal Article · 03 November 2021 · Remote Sensing

DOI: https://doi.org/10.3390/rs13214438 · OSTI ID:1829173

Aird, Jeanie A.; Quon, Eliot W.;

;

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC36-08GO28308; SC0016605

OSTI ID:: 1829173

Alternate ID(s):: OSTI ID: 1834311

Journal Information:: Remote Sensing, Journal Name: Remote Sensing Journal Issue: 21 Vol. 13; ISSN 2072-4292; ISSN RSBSBZ

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

References (43)

Wind Turbine Wake Definition and Identification Using Velocity Deficit and Turbulence Profile Panossian, Nadia; Herges, Thomas G.; Maniaci, David C. 2018 Wind Energy Symposium https://doi.org/10.2514/6.2018-0514	conference	January 2018
Modelling and measuring flow and wind turbine wakes in large wind farms offshore Barthelmie, R. J.; Hansen, K.; Frandsen, S. T. Wind Energy, Vol. 12, Issue 5, p. 431-444 https://doi.org/10.1002/we.348	journal	July 2009
Techniques of Wind Vector Estimation from Data Measured with a Scanning Coherent Doppler Lidar Smalikho, Igor Journal of Atmospheric and Oceanic Technology, Vol. 20, Issue 2 https://doi.org/10.1175/1520-0426(2003)020<0276:TOWVEF>2.0.CO;2	journal	February 2003
Subject independent facial expression recognition with robust face detection using a convolutional neural network Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke Neural Networks, Vol. 16, Issue 5-6 https://doi.org/10.1016/S0893-6080(03)00115-1	journal	June 2003
Application of support vector machine models for forecasting solar and wind energy resources: A review Zendehboudi, Alireza; Baseer, M. A.; Saidur, R. Journal of Cleaner Production, Vol. 199 https://doi.org/10.1016/j.jclepro.2018.07.164	journal	October 2018
Spatial study of the wake meandering using modelled wind turbines in a wind tunnel España, G.; Aubrun, S.; Loyer, S. Wind Energy, Vol. 14, Issue 7, p. 923-937 https://doi.org/10.1002/we.515	journal	October 2011
A Survey on Deep Transfer Learning Tan, Chuanqi; Sun, Fuchun; Kong, Tao Artificial Neural Networks and Machine Learning – ICANN 2018 https://doi.org/10.1007/978-3-030-01424-7_27	book	January 2018
Wind Shear and Turbulence Effects on Rotor Fatigue and Loads Control Eggers,, A. J.; Digumarthi, R.; Chaney, K. Journal of Solar Energy Engineering, Vol. 125, Issue 4 https://doi.org/10.1115/1.1629752	journal	November 2003
Vector neural net identifying many strongly distorted and correlated patterns Kryzhanovsky, Boris V.; Mikaelian, Andrei L.; Fonarev, Anatoly B. Photonics Asia 2004, SPIE Proceedings https://doi.org/10.1117/12.572334	conference	January 2005
A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings Li, Ying; Xu, Weipan; Chen, Haohui Remote Sensing, Vol. 13, Issue 6 https://doi.org/10.3390/rs13061070	journal	March 2021
Wind turbine wake characterization in complex terrain via integrated Doppler lidar data from the Perdigão experiment Barthelmie, R. J.; Pryor, S. C.; Wildmann, N. Journal of Physics: Conference Series, Vol. 1037 https://doi.org/10.1088/1742-6596/1037/5/052022	journal	June 2018
Machine learning of optical properties of materials – predicting spectra from images and images from spectra Stein, Helge S.; Guevarra, Dan; Newhouse, Paul F. Chemical Science, Vol. 10, Issue 1 https://doi.org/10.1039/C8SC03077D	journal	January 2019
Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study Vollmer, Lukas; Steinfeld, Gerald; Heinemann, Detlev Wind Energy Science, Vol. 1, Issue 2 https://doi.org/10.5194/wes-1-129-2016	journal	January 2016
Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data Aitken, Matthew L.; Banta, Robert M.; Pichugina, Yelena L. Journal of Atmospheric and Oceanic Technology, Vol. 31, Issue 4 https://doi.org/10.1175/JTECH-D-13-00104.1	journal	April 2014
Convolutional recurrent neural networks for music classification Choi, Keunwoo; Fazekas, Gyorgy; Sandler, Mark 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) https://doi.org/10.1109/ICASSP.2017.7952585	conference	March 2017
FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings Salscheider, Niels Ole 2020 25th International Conference on Pattern Recognition (ICPR) https://doi.org/10.1109/ICPR48806.2021.9412930	conference	January 2021
Lidar arc scan uncertainty reduction through scanning geometry optimization Wang, Hui; Barthelmie, Rebecca J.; Pryor, Sara C. Atmospheric Measurement Techniques, Vol. 9, Issue 4 https://doi.org/10.5194/amt-9-1653-2016	journal	January 2016
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation Girshick, Ross; Donahue, Jeff; Darrell, Trevor IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, Issue 1 https://doi.org/10.1109/TPAMI.2015.2437384	journal	January 2016
Image processing with neural networks—a review Egmont-Petersen, M.; de Ridder, D.; Handels, H. Pattern Recognition, Vol. 35, Issue 10 https://doi.org/10.1016/S0031-3203(01)00178-9	journal	October 2002
Feature Pyramid Networks for Object Detection Lin, Tsung-Yi; Dollar, Piotr; Girshick, Ross 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2017.106	conference	July 2017
Transfer Learning Torrey, Lisa; Shavlik, Jude Handbook of Research on Machine Learning Applications and Trends https://doi.org/10.4018/978-1-60566-766-9.ch011	book	January 2010
Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images Puri, Munish ASSAY and Drug Development Technologies, Vol. 18, Issue 1 https://doi.org/10.1089/adt.2019.919	journal	January 2020
Wake modeling of wind turbines using machine learning Ti, Zilong; Deng, Xiao Wei; Yang, Hongxing Applied Energy, Vol. 257 https://doi.org/10.1016/j.apenergy.2019.114025	journal	January 2020
Comparison of Rotor Wake Identification and Characterization Methods for the Analysis of Wake Dynamics and Evolution Quon, E. W.; Doubrawa, P.; Debnath, M. Journal of Physics: Conference Series, Vol. 1452 https://doi.org/10.1088/1742-6596/1452/1/012070	journal	January 2020
Dynamic wake tracking and characteristics estimation using a cost-effective LiDAR Lio, Wai Hou; Larsen, Gunner C.; Poulsen, Niels K. Journal of Physics: Conference Series, Vol. 1618 https://doi.org/10.1088/1742-6596/1618/3/032036	journal	September 2020
Atmospheric and Wake Turbulence Impacts on Wind Turbine Fatigue Loadings Lee, Sang; Churchfield, Matthew; Moriarty, Patrick 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition https://doi.org/10.2514/6.2012-540	conference	November 2012
Using machine learning to predict wind turbine power output Clifton, A.; Kilcher, L.; Lundquist, J. K. Environmental Research Letters, Vol. 8, Issue 2 https://doi.org/10.1088/1748-9326/8/2/024009	journal	April 2013
Monitoring of wind farms’ power curves using machine learning techniques Marvuglia, Antonino; Messineo, Antonio Applied Energy, Vol. 98 https://doi.org/10.1016/j.apenergy.2012.04.037	journal	October 2012
Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements Doubrawa, Paula; Barthelmie, Rebecca; Wang, Hui Remote Sensing, Vol. 8, Issue 11 https://doi.org/10.3390/rs8110939	journal	November 2016
Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data Maxwell, Aaron E.; Pourmohammadi, Pariya; Poyner, Joey D. Remote Sensing, Vol. 12, Issue 3 https://doi.org/10.3390/rs12030547	journal	February 2020
Diagnosing wind turbine faults using machine learning techniques applied to operational data Leahy, Kevin; Hu, R. Lily; Konstantakopoulos, Ioannis C. 2016 IEEE International Conference on Prognostics and Health Management (ICPHM) https://doi.org/10.1109/ICPHM.2016.7542860	conference	June 2016
Mask R-CNN He, Kaiming; Gkioxari, Georgia; Dollar, Piotr 2017 IEEE International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2017.322	conference	October 2017
Recent advances in efficient computation of deep convolutional neural networks Cheng, Jian; Wang, Pei-song; Li, Gang Frontiers of Information Technology & Electronic Engineering, Vol. 19, Issue 1 https://doi.org/10.1631/FITEE.1700789	journal	January 2018
Skeleton-based morphological coding of binary images Kresch, R.; Malah, D. IEEE Transactions on Image Processing, Vol. 7, Issue 10 https://doi.org/10.1109/83.718480	journal	January 1998
High resolution wind turbine wake measurements with a scanning lidar Herges, T. G.; Maniaci, D. C.; Naughton, B. T. Journal of Physics: Conference Series, Vol. 854 https://doi.org/10.1088/1742-6596/854/1/012021	journal	May 2017
Automated wind turbine wake characterization in complex terrain Barthelmie, Rebecca J.; Pryor, Sara C. Atmospheric Measurement Techniques, Vol. 12, Issue 6 https://doi.org/10.5194/amt-12-3463-2019	journal	January 2019
Meteorological Controls on Wind Turbine Wakes Barthelmie, Rebecca J.; Hansen, Kurt S.; Pryor, Sara C. Proceedings of the IEEE, Vol. 101, Issue 4 https://doi.org/10.1109/JPROC.2012.2204029	journal	April 2013
Fast R-CNN Girshick, Ross 2015 IEEE International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2015.169	conference	December 2015
Neural Networks for Pattern Recognition Bishop, Christopher M. https://doi.org/10.1093/oso/9780198538493.001.0001	book	November 1995
Principles of neurodynamics. Perceptrons and the theory of brain mechanisms Rosenblatt, Frank https://doi.org/10.21236/AD0256582	report	March 1961
Microsoft COCO: Common Objects in Context Lin, Tsung-Yi; Maire, Michael; Belongie, Serge ECCV 2014: Computer Vision – ECCV 2014 https://doi.org/10.1007/978-3-319-10602-1_48	book	January 2014
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Milletari, Fausto; Navab, Nassir; Ahmadi, Seyed-Ahmad 2016 Fourth International Conference on 3D Vision (3DV) https://doi.org/10.1109/3DV.2016.79	conference	October 2016
SAR ship detection using sea-land segmentation-based convolutional neural network Liu, Yang; Zhang, Miao-hui; Xu, Peng 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP) https://doi.org/10.1109/RSIP.2017.7958806	conference	May 2017

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