# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 4 Discussion and Conclusions  
We implemented a Mask R-CNN model with a ResNet 101 FPN backbone for wake characterization and identification in complex terrain from lidar arc scans. The lidar data were collected during a campaign spanning July 2018-December 2018 (inclusive) at a wind farm in the U.S. Pacific Northwest. The dataset contains a wide variety of experimental parameters, such as range gate, resolution, number of wakes captured and scanning time. The variety of the dataset makes it ideal for evaluation of the model under a wide range of experimental parameters. A subset of 354 arc scans with approximately 1000 wind turbine wakes is employed in this proof-of-principle analysis. Two-thirds of the scans are used for model training, whereas the other one-third is split evenly among model validation and testing. Many of the scans contain regions of missing velocity measurements as a result of CNR filtering, and the wakes are in various stages of formation and dissipation because of terrain complexity, resulting in wakes with multiple fragments throughout the flow field. These fragments and wakes are often asymmetric, a geometric quality that presents challenges for current wake identification and characterization methods. Thus, common techniques for wake characterization and identification are difficult to implement for the dataset. The CNN applied here is successful regardless of these difficulties, exhibiting high skill in identifying wakes and dissipating wakes from full and lower resolution lidar arc scan images. The success rate for wake identification from full resolution scan images in which wakes are present is 95%. The most common failure mode for full wind turbine wakes is a false negative. In this case, the model does not identify a wake when one is present. The most common error in identifying dissipated wake fragments within the scans is a result of false positives, which are defined as the model identifying a flow field structure as a dissipating wake fragment when it is not. When the scan resolution is degraded by removing data from every other range gate, an increase in false negatives is observed when identifying dissipating wake fragments. This indicates that wake fragment flow contours under full resolution are unique, and that the CNN utilizes specific flow patterns to differentiate between wake fragments and other low-velocity turbulent flow. However, the model still exhibits skill in identifying wakes under reduced resolution, with a success rate of 92%. Although reductions in the range gate alter wake characteristics numerically, when the scans are clustered by prevailing wind speed and atmospheric stability, the behavior of wake characteristics between each group is conserved. Further, median wake characteristics per group (grouped by inflow velocity and time of scan collection to provide a broad metric for atmospheric stability) are broadly consistent with a priori expectations, further indicating model accuracy and applicability. Results indicate that the Mask R-CNN has a wide applicability to a range of challenges in wind energy science, including improving existing simulation tools for wakes in complex terrain, improving knowledge of wake dynamics in complex terrain, enabling real-time wake identification and characterization from remote sensing data and assisting in condition monitoring. Future work will include an implementation of Mask R-CNN models with a higher accuracy (Scoring Mask R-CNN, Cascade Mask R-CNN) and will involve the fine-tuning of model training parameters. Uncertainties due to the flow field reconstruction method utilized will be explored further. For example, scans with wind directions close to normal to the scanning direction were not filtered out in this study. Although flow reconstruction under these conditions may result in flow field errors, the inclusion of these scans in the training dataset may be helpful in terms of training the CNN to recognize wakes even in these conditions.