Reinforcement Learning for Wind Turbine Load Control: How AI can drive tomorrow‘s wind turbines

Load control strategies for wind turbines are used to reduce the structural wear of the turbine without reducing energy yield. Until now, these control strategies are almost exclusively built up-on linear approaches like PID and model-based controllers due to their robustness. However, advances in turbine size and capabilities create a need for more complex control strategies that can effectively address design challenges in modern turbines. This work presents WINDL, a load control policy based on a neural network, which is trained through model-free Reinforcement Learning (RL) on a simulated wind turbine. While RL has achieved great success in the past on games and simple simulation benchmarks, applications to more complex control problems are starting to emerge just recently. We show that through the usage of regularization techniques and signal transformations, such an application to the field of wind turbine load control is possible. Using a smoothness regularizer, we incentivize the highly non-linear neural network policy to output control actions that are safe to apply to a wind turbine. The Coleman transformation, a common tool for the design of traditional PID-based load control strategies, is used to project signals into a stationary coordinate space, increasing robustness and final policy performance. Trained to control a large offshore turbine in a model-free fashion, WINDL finds a control policy that outperforms a state-of-the-art controller based on the IPC strategy with respect to the prima-ry optimization goal blade loads. Using the DEL metric, we measure 54.1% lower blade loads in the steady wind and 13.45% lower blade loads in the turbulent wind scenario. While such levels of blade reduction come with slightly worse performance on secondary optimi-zation goals like pitch wear and power production, we demonstrate the ability to control the trade-off between different optimization goals on the example of pitch versus blade loads. To comple-ment our findings, we perform a qualitative analysis of the policy behavior and learning process. We believe our work to be the first application of RL to wind turbine load control that exceeds baseline performance in the primary optimization metric, opening up the possibility of including specialized load controllers for targeting critical design driving scenarios of modern large wind turbines.:Problem Method Aim Results Conclusio

Active flap control with the trailing edge flap hinge moment as a sensor: using it to estimate local blade inflow conditions and to reduce extreme blade loads and deflections

Active trailing edge flaps are a promising technology that can potentially enable further increases in wind turbine sizes without the disproportionate increase in loads, thus reducing the cost of wind energy even further. Extreme loads and critical deflections of the blade are design-driving issues that can effectively be reduced by flaps. In this paper, we consider the flap hinge moment as a local input sensor for a simple flap controller that reduces extreme loads and critical deflections of the DTU 10 MW Reference Wind Turbine blade. We present a model to calculate the unsteady flap hinge moment that can be used in aeroelastic simulations in the time domain. This model is used to develop an observer that estimates the local angle of attack and relative wind velocity of a blade section based on local sensor information including the flap hinge moment of the blade section. For steady wind conditions that include yawed inflow and wind shear, the observer is able to estimate the local inflow conditions with errors in the mean angle of attack below 0.2∘ and mean relative wind speed errors below 0.4 %. For fully turbulent wind conditions, the observer is able to estimate the low-frequency content of the local angle of attack and relative velocity even when it is lacking information on the incoming turbulent wind. We include this observer as part of a simple flap controller to reduce extreme loads and critical deflections of the blade. The flap controller's performance is tested in load simulations of the reference turbine with active flaps according to the IEC 61400-1 power production with extreme turbulence group. We used the lifting line free vortex wake method to calculate the aerodynamic loads. Results show a reduction of the maximum out-of-plane and resulting blade root bending moments of 8 % and 7.6 %, respectively, when compared to a baseline case without flaps. The critical blade tip deflection is reduced by 7.1 %. Furthermore, a sector load analysis considering extreme loading in all load directions shows a reduction of the extreme resulting bending moment in an angular region covering 30∘ around the positive out-of-plane blade root bending moment. Further analysis reveals that a fast reaction time of the flap system proves to be critical for its performance. This is achieved with the use of local sensors as input for the flap controller. A larger reduction potential of the system is identified but not reached mainly because of a combination of challenging controller objectives and the simple controller architecture.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Pressure-based lift estimation and its application to feedforward load control employing trailing-edge flaps

This experimental load control study presents results of an active trailing-edge flap feedforward controller for wind turbine applications. The controller input is derived from pressure-based lift estimation methods that rely either on a quasi-steady method, based on a three-hole probe, or on an unsteady method that is based on three selected surface pressure ports. Furthermore, a standard feedback controller, based on force balance measurements, is compared to the feedforward control. A Clark-Y airfoil is employed for the wing that is equipped with a trailing-edge flap of x/c=30% chordwise extension. Inflow disturbances are created by a two-dimensional active grid. The Reynolds number is Re=290 000, and reduced frequencies of k=0.07 up to k=0.32 are analyzed. Within the first part of the paper, the lift estimation methods are compared. The surface-pressure-based method shows generally more accurate results, whereas the three-hole probe estimate overpredicts the lift amplitudes with increasing frequencies. Nonetheless, employing the latter as input to the feedforward controller is more promising as a beneficial phase lead is introduced by this method. A successful load alleviation was achieved up to reduced frequencies of k=0.192.DFG, 218736457, Experimentelle Untersuchung von Strömungskontrollmethoden zur Lastkontrolle an Windkraftanlagen mittels einer Forschungswindkraftanlage im WindkanalDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

Measuring the Neutrino Cross Section Using 8 years of Upgoing Muon Neutrinos Observed with IceCube

The IceCube Neutrino Observatory detects neutrinos at energies orders of magnitude higher than those available to current accelerators. Above 40 TeV, neutrinos traveling through the Earth will be absorbed as they interact via charged current interactions with nuclei, creating a deficit of Earth-crossing neutrinos detected at IceCube. The previous published results showed the cross section to be consistent with Standard Model predictions for 1 year of IceCube data. We present a new analysis that uses 8 years of IceCube data to fit the ν$_{μ}$ absorption in the Earth, with statistics an order of magnitude better than previous analyses, and with an improved treatment of systematic uncertainties. It will measure the cross section in three energy bins that span the range 1 TeV to 100 PeV. We will present Monte Carlo studies that demonstrate its sensitivity