# Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
## 1 Introduction  
Wind turbine wakes are turbulent low-velocity flow structures that interact with downstream turbines, decreasing the inflow speed and turbine performance while increasing the blade fatigue [1, 2, 3, 4]. Knowledge of wake trajectory, geometry and orientation is pertinent to power production and fatigue analyses. However, there is uncertainty in both identifying wakes and defining wake metrics. Wake identification and characterization methods have been introduced to quantify the behavior of wakes and applied to simulation outputs and measurements over flat terrain [5, 6]. Even under these relatively simple datasets, methodologies still exhibit difficulties in distinguishing the wind turbine wake from local turbulent flows, particularly at distances far behind the rotor [7]. Further, variations in the local orography in and around wind farms enhance turbulence and flow complexity, thus rendering wind turbine wake identification, characterization and tracking more difficult [8]. Methods to identify wakes from remote sensing (e.g., lidar) data commonly depend on the availability of information, such as the wind turbine rotor diameter, free-stream wind speed, atmospheric stability, scan resolution and geometry [9, 10]. Many wake detection methods approximate the location and area of the wake's center through fitting Gaussiancurves to line-of-sight (LOS) velocity deficits, implementing contour-based methods utilizing momentum flux or a velocity deficit and using the turbulence profile method [5; 11; 12]. Wake detection methods in complex terrain have been implemented with varying degrees of success, and focus on identifying the wake center for a single wind turbine [13; 14]. An automated wake detection method was proposed to identify the wake centerline from lidar measurements in complex terrain by using knowledge of the turbine's location and flow-field anomalies [8]. A second dynamic model for wake identification and characterization in complex terrain using lidar measurements identified the wake centerline from a single wind turbine using a wake velocity deficit and dynamic wake meandering models [15]. Although identifying the wake centerline of a single wind turbine is pertinent to understanding the propagation of wakes downstream in complex terrain, there remains a need for automated (scan campaigns often result in thousands of scans that require processing) holistic wake identification and characterization methods that can be implemented independently of varying experimental parameters and applied to flow fields with any number of wind turbine wakes. Furthermore, a method that can track the entirety of the wake (as well as partial wakes), including its exact shape as it forms and dissipates, is necessary to progress toward an enhanced understanding of wind turbine wake dynamics in complex terrain. Previously proposed methods exhibit a relatively high fidelity for identifying well-defined single wakes, but lack skill in identifying and characterizing asymmetric (skewed) or partial wakes resulting from turbulence or intentional wake steering. As many wake identification methods employ assumptions about the wake shape (e.g., Gaussian distributions), behavior of the wake center (e.g., maximum velocity deficit method) and definition of wake edges (e.g., assuming the wake diameter is equal to the rotor diameter), a model that can operate independently of these assumptions is pertinent to a broader range of datasets and applications. Machine-learning techniques are adept at modeling, learning and predicting patterns from data [16; 17], and are being used for applications such as wind plant power output prediction, wind resource prediction and even wake modeling [18; 19; 20; 21; 22]. Neural networks are machine-learning models that iteratively learn and assign meaning to patterns in input data through a series of neurons (also known as nodes) arranged in layers [23; 24; 25].