Numerical investigation of wind turbine control schemes for load alleviation and wake effects mitigation

For more than 3000 years, windmills have been used to harvest energy from the wind. Over the past 30 years, electrical generators and advanced aerodynamics have turned them into the wind turbines we know. From hereon, how can the coming 30 years see them transition from outsider to key player of the energy system to help reach carbon neutrality? Increasing rotor diameters is a possible answer, clustering turbines into wind farms is another. Yet, with both options come new challenges, as the sensitivity of components to fatigue increases with the size of rotors and the wake phenomenon is responsible for power losses in wind farms. This thesis falls within the context of using control strategies to tackle these challenges. More specifically, this work relies on high fidelity simulations to investigate control approaches numerically. When it comes to reducing fatigue loads, individually controlling the pitch of each blade has proven to be efficient. This thesis introduces a novel controller architecture that relies on a neural network trained with reinforcement learning. This work demonstrates that a neural network can learn how to alleviate loads in simple wind conditions and that it is also capable of transferring that knowledge to realistic ones, such as turbulence and wakes. Regarding the question of wake mitigation, dynamically controlling wind turbines is gaining interest. While some strategies enhance the lateral displacement of the wake, others periodically modify its intensity. This work provides some insights into the mechanisms relating dynamic actuation of the blades to power gains in the wake. To do so, the effects of dynamic flow control on the wake destabilization and recovery processes are investigated. Attention is also paid to quantifying the impacts of such strategies on both power production and loads at the scale of a pair of turbines.(FSA - Sciences de l'ingénieur) -- UCL, 202

Handling Individual Pitch Control within an Actuator Disk framework: verification against the Actuator Line method and application to wake interaction problems

The present study aims at assessing the Actuator Disk (AD) method supplemented with an Individual Pitch Control (IPC) strategy, at a resolution appropriate for the Large Eddy Simulation of large wind farms. The IPC scheme is based on a state-of-the art individual pitch control, generalized to be applied to an AD approach. This procedure also requires an accurate recovery of the flapwise bending moment on each blade, which is not trivial for a disk-type model. In order to compute flapwise moments on each blade, blade trajectories are reproduced through the disk and the AD aerodynamic forces are interpolated onto these virtual blades at each time step. We verify the AD model with IPC in simulations of an isolated wind turbine, for different wind speeds and turbulence intensities, and in a configuration with two rotors. We compare the AD statistics with those obtained using an Actuator Line (AL) method. The comparison done in terms of equivalent moment shows that the AD and AL simulations produce very similar results

A reinforcement-learning approach for individual pitch control

Individual pitch control has shown great capability of alleviating the oscillating loads experienced by wind turbine blades due to wind shear, atmospheric turbulence, yaw misalignment or wake impingement. This work presents a novel controller structure that relies on the separation of low-level control tasks and high-level ones. It is based on a neural network that modulates basic periodic pitch angle signals. This neural network is trained with reinforcement learning, a trial and error way of acquiring skills, in a low-fidelity environment exempt from turbulence. The trained controller is further deployed in large eddy simulations to assess its performances in turbulent and waked flows. Results show that the method enables the neural network to learn how to reduce fatigue loads and to exploit that knowledge to complex turbulent flows. When compared to a state-of-the-art individual pitch controller, the one introduced here presents similar load alleviation capacities at reasonable turbulence intensity levels, while displaying very smooth pitching commands by nature

Data assimilation for the prediction of wake trajectories within wind farms

In this paper, we formulate a physics-based surrogate wake model in the framework of online wind farm control. A flow sensing module is coupled with a wake model in order to predict the behavior of the wake downstream of a wind turbine based on its loads, wind probe data and operating settings. Information about the incoming flow is recovered using flow sensing techniques and then fed to the wake model, which reconstructs the wake based on this limited set of information. Special focus is laid on limiting the number of input parameters while keeping the computational cost low in order to facilitate the tuning procedure. Once calibrated, comparison with high-fidelity numerical results retrieved from Large Eddy Simulation (LES) of a wind farm confirms the good potential of the approach for online wake prediction within farms. The two approaches are further compared in terms of their wake center and time-averaged speed deficit predictions demonstrating good agreement in the process

Multiphysics simulations of the dynamic and wakes of a floating Vertical Axis Wind Turbine

A framework performing Large Eddy Simulations of the flow past Floating Offshore Vertical Axis Wind Turbines (FOVAWT) mounted on semi-submersible platforms is presented. The simulation tool captures the unsteady flow and the resolved 6-DOF motion of the system. A state-of-the-art vortex particle-mesh method solves the aerodynamics and wakes of the turbines. The hydrodynamic loads are accounted for via the relative form of the Morison's equations while the mooring lines tension is computed through a lumped-mass model. Realistic conditions are further obtained by introducing a sheared turbulent inflow and erratic waves in a simulation domain that extends ten diameters downstream of the machine. First, analyses of an isolated FOVAWT wake and motion are performed and the impact of prominent environmental phenomena is assessed. In a second step, the interactions of two FOVAWTs throughout their wakes are studied. In general, the incoming wake signature induces larger oscillation of the downstream machine, alters the shape of the wakes and shortened the transition to turbulence

Characterisation and Online Update of a Vorticity-Based Wind Skeleton Wake Model

Wind turbine wake physics is by nature unsteady and highly sensitive to the local wind characteristics. While modern Computational Fluid Dynamic methods (eg: Large Eddy Simulation) allow to accurately capture the flow at the wind farm scale, they still come at a prohibitive computational cost preventing their use for online control or Machine Learning schemes. This work pursues the recent efforts undertaken by Marichal et al. (2017) to develop a computationally affordable yet accurate unsteady Wake Model. Marichal et al. (2017) introduced and successfully tested a vorticity-based skeleton Wake Model (WM). This vorticity-based skeleton essentially consists of a regularized Vortex Sheet Tube (VST) in the near wake which then transitions into a Vortex Dipole Line (VDL) in the far wake. The rotor operation itself is modelled using the Blade Element theory. The present study further assesses the performances of the WM: it extends the validation procedure to various wind turbine settings (ie: different Tip Speed Ratios) and inflow conditions. The data recovered from the wake model is compared to that extracted from high fidelity numerical simulations performed on the NREL 5MW wind turbine using an Immersed Lifting Line-enabled Vortex Particle-Mesh (VPM) flow solver. Online model update strategies are then discussed. Indeed, the existing WM still requires the knowledge of the upstream wind conditions in order to provide an accurate downstream wind field estimate. Following Bottasso et al. (2018), an Extended Kalman Filter (EKF) estimating the Rotor-Effective Wind Speed by a Blade-Load- based Estimator is implemented. This EKF allows to estimate the wind profile upstream the wind turbine and eventually to feed it to the vorticity-based skeleton wake model as an input parameter. We finally plan to extend the tools developed to a two-turbines system. The downstream wind profile provided by the Kalman estimator will be compared to that given by the wake model computed by the upstream turbine. Data assimilation techniques will then be used to correct the wake model online

Biomimetic individual pitch control for load alleviation

Individual pitch control has shown great capability of alleviating the oscillating loads experienced by wind turbine blades due to wind shear, atmospheric turbulence, yaw misalignement or wake impingement. This work presents a bio-inspired structure for individual pitch control where neural oscillators produce basic rhythmic patterns of the pitch angles, while a deep neural network modulates them according to the environmental conditions. This mimics, respectively, the central patterns generators present in the spinal chord of animals and their cortex. The mimicry further applies to the neural network as it is trained with reinforcement learning, a method inspired by the trial and error way of animal learning. Large eddy simulations of the reference NREL 5MW wind turbine using this biomimetic controller show that the neural network learns how to reduce fatigue loads by producing smooth pitching commands

Tackling control and modeling challenges in wind energy with data-driven tools

Driven by the abundance of data, new paradigms have lately emerged in science and engineering fields. Wind energy makes no exception and an increasing number of data-driven methods are used to tackle one of today’s biggest challenges in the wind community: reduce the levelized cost of energy. Many underlying themes are concerned, ranging from wind turbine and wind farm control for power maximization and fatigue reduction to wake modeling. We present a couple of efforts into exploiting such data-driven paradigms to address important matters in wind energy. At the turbine scale, we focus on the detrimental effects of atmospheric boundary layer and turbulence on structural components. We propose an individual pitch controller based on a neural network trained with reinforcement learning. As for the wind farm scale, wake interaction is a big challenge as it generates major power losses. We investigate wake redirection strategies under the lens of reinforcement learning. The investigations reveal the need for low-cost yet accurate wake modeling, which leads us to bring data assimilation and physics together and develop an online dynamic wake meandering model

Performance assessment of wake mitigation strategies

Many control strategies have emerged in the field of wind energy to address wake interaction effects and try to maximize wind farm power production. They rely either on wake redirection or on induction control. While wake redirection is easily performed by yawing the turbine, efficient and practically deployable induction control is not as straightforward. Recent studies paved the way with periodic dynamic induction control (PDIC), a strategy that generates pulsatile patterns in the wake and increases wake remixing. This study aims at further investigating the physics behind PDIC through large eddy simulations at high resolution. Results show the wake shear layer destabilization leading to the pulsatile wake. This paper also presents a comprehensive comparison between greedy control, PDIC and static yaw. It focuses both on power and loads at the scale of a pair of in-line turbines. It shows that PDIC significantly increases fatigue loads and leads to moderate power gains, while properly oriented yaw leads to fatigue load reduction on the upstream turbine and noticeable increase of the power production