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.
- 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}
}
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