Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
Abstract
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.
- Authors:
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office; National Science Foundation (NSF)
- OSTI Identifier:
- 1829173
- Alternate Identifier(s):
- OSTI ID: 1834311
- Report Number(s):
- NREL/JA-5000-80496
Journal ID: ISSN 2072-4292; PII: rs13214438
- Grant/Contract Number:
- AC36-08GO28308; SC0016605; DGE-1650441
- Resource Type:
- Published Article
- Journal Name:
- Remote Sensing
- Additional Journal Information:
- Journal Name: Remote Sensing Journal Volume: 13 Journal Issue: 21; Journal ID: ISSN 2072-4292
- Publisher:
- MDPI AG
- Country of Publication:
- Switzerland
- Language:
- English
- Subject:
- 17 WIND ENERGY; complex terrain; convolution neural network; image processing; lidar; machine learning; wake characterization; wind turbine wakes
Citation Formats
Aird, Jeanie A., Quon, Eliot W., Barthelmie, Rebecca J., Debnath, Mithu, Doubrawa, Paula, and Pryor, Sara C. Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain. Switzerland: N. p., 2021.
Web. doi:10.3390/rs13214438.
Aird, Jeanie A., Quon, Eliot W., Barthelmie, Rebecca J., Debnath, Mithu, Doubrawa, Paula, & Pryor, Sara C. Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain. Switzerland. https://doi.org/10.3390/rs13214438
Aird, Jeanie A., Quon, Eliot W., Barthelmie, Rebecca J., Debnath, Mithu, Doubrawa, Paula, and Pryor, Sara C. Thu .
"Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain". Switzerland. https://doi.org/10.3390/rs13214438.
@article{osti_1829173,
title = {Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain},
author = {Aird, Jeanie A. and Quon, Eliot W. and Barthelmie, Rebecca J. and Debnath, Mithu and Doubrawa, Paula and Pryor, Sara C.},
abstractNote = {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.},
doi = {10.3390/rs13214438},
journal = {Remote Sensing},
number = 21,
volume = 13,
place = {Switzerland},
year = {2021},
month = {11}
}
https://doi.org/10.3390/rs13214438
Works referenced in this record:
Mapping the Topographic Features of Mining-Related Valley Fills Using Mask R-CNN Deep Learning and Digital Elevation Data
journal, February 2020
- Maxwell, Aaron E.; Pourmohammadi, Pariya; Poyner, Joey D.
- Remote Sensing, Vol. 12, Issue 3
Wind Shear and Turbulence Effects on Rotor Fatigue and Loads Control
journal, November 2003
- Eggers,, A. J.; Digumarthi, R.; Chaney, K.
- Journal of Solar Energy Engineering, Vol. 125, Issue 4
High resolution wind turbine wake measurements with a scanning lidar
journal, May 2017
- Herges, T. G.; Maniaci, D. C.; Naughton, B. T.
- Journal of Physics: Conference Series, Vol. 854
Spatial study of the wake meandering using modelled wind turbines in a wind tunnel
journal, October 2011
- España, G.; Aubrun, S.; Loyer, S.
- Wind Energy, Vol. 14, Issue 7, p. 923-937
Using machine learning to predict wind turbine power output
journal, April 2013
- Clifton, A.; Kilcher, L.; Lundquist, J. K.
- Environmental Research Letters, Vol. 8, Issue 2
Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images
journal, January 2020
- Puri, Munish
- ASSAY and Drug Development Technologies, Vol. 18, Issue 1
Comparison of Rotor Wake Identification and Characterization Methods for the Analysis of Wake Dynamics and Evolution
journal, January 2020
- Quon, E. W.; Doubrawa, P.; Debnath, M.
- Journal of Physics: Conference Series, Vol. 1452
Wind turbine wake characterization in complex terrain via integrated Doppler lidar data from the Perdigão experiment
journal, June 2018
- Barthelmie, R. J.; Pryor, S. C.; Wildmann, N.
- Journal of Physics: Conference Series, Vol. 1037
Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements
journal, November 2016
- Doubrawa, Paula; Barthelmie, Rebecca; Wang, Hui
- Remote Sensing, Vol. 8, Issue 11
Techniques of Wind Vector Estimation from Data Measured with a Scanning Coherent Doppler Lidar
journal, February 2003
- Smalikho, Igor
- Journal of Atmospheric and Oceanic Technology, Vol. 20, Issue 2
Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
journal, January 2016
- Girshick, Ross; Donahue, Jeff; Darrell, Trevor
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, Issue 1
Image processing with neural networks—a review
journal, October 2002
- Egmont-Petersen, M.; de Ridder, D.; Handels, H.
- Pattern Recognition, Vol. 35, Issue 10
Monitoring of wind farms’ power curves using machine learning techniques
journal, October 2012
- Marvuglia, Antonino; Messineo, Antonio
- Applied Energy, Vol. 98
Wake modeling of wind turbines using machine learning
journal, January 2020
- Ti, Zilong; Deng, Xiao Wei; Yang, Hongxing
- Applied Energy, Vol. 257
Lidar arc scan uncertainty reduction through scanning geometry optimization
journal, January 2016
- Wang, Hui; Barthelmie, Rebecca J.; Pryor, Sara C.
- Atmospheric Measurement Techniques, Vol. 9, Issue 4
Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: an LES study
journal, January 2016
- Vollmer, Lukas; Steinfeld, Gerald; Heinemann, Detlev
- Wind Energy Science, Vol. 1, Issue 2
Application of support vector machine models for forecasting solar and wind energy resources: A review
journal, October 2018
- Zendehboudi, Alireza; Baseer, M. A.; Saidur, R.
- Journal of Cleaner Production, Vol. 199
Dynamic wake tracking and characteristics estimation using a cost-effective LiDAR
journal, September 2020
- Lio, Wai Hou; Larsen, Gunner C.; Poulsen, Niels K.
- Journal of Physics: Conference Series, Vol. 1618
A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings
journal, March 2021
- Li, Ying; Xu, Weipan; Chen, Haohui
- Remote Sensing, Vol. 13, Issue 6
Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data
journal, April 2014
- Aitken, Matthew L.; Banta, Robert M.; Pichugina, Yelena L.
- Journal of Atmospheric and Oceanic Technology, Vol. 31, Issue 4
Recent advances in efficient computation of deep convolutional neural networks
journal, January 2018
- Cheng, Jian; Wang, Pei-song; Li, Gang
- Frontiers of Information Technology & Electronic Engineering, Vol. 19, Issue 1
Modelling and measuring flow and wind turbine wakes in large wind farms offshore
journal, July 2009
- Barthelmie, R. J.; Hansen, K.; Frandsen, S. T.
- Wind Energy, Vol. 12, Issue 5, p. 431-444
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
journal, June 2017
- Ren, Shaoqing; He, Kaiming; Girshick, Ross
- IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, Issue 6
Automated wind turbine wake characterization in complex terrain
journal, January 2019
- Barthelmie, Rebecca J.; Pryor, Sara C.
- Atmospheric Measurement Techniques, Vol. 12, Issue 6
Meteorological Controls on Wind Turbine Wakes
journal, April 2013
- Barthelmie, Rebecca J.; Hansen, Kurt S.; Pryor, Sara C.
- Proceedings of the IEEE, Vol. 101, Issue 4
Vector neural net identifying many strongly distorted and correlated patterns
conference, January 2005
- Kryzhanovsky, Boris V.; Mikaelian, Andrei L.; Fonarev, Anatoly B.
- Photonics Asia 2004, SPIE Proceedings
Subject independent facial expression recognition with robust face detection using a convolutional neural network
journal, June 2003
- Matsugu, Masakazu; Mori, Katsuhiko; Mitari, Yusuke
- Neural Networks, Vol. 16, Issue 5-6
Skeleton-based morphological coding of binary images
journal, January 1998
- Kresch, R.; Malah, D.
- IEEE Transactions on Image Processing, Vol. 7, Issue 10
Machine learning of optical properties of materials – predicting spectra from images and images from spectra
journal, January 2019
- Stein, Helge S.; Guevarra, Dan; Newhouse, Paul F.
- Chemical Science, Vol. 10, Issue 1