Yaw Misalignment Control in Wind Turbines

Abstract. Accurately predicting the aerodynamic loads on wind turbine blades under yawed inflow remains a major challenge due to complexity of threedimensional unsteady flow phenomena. This study combines high-fidelity computational fluid dynamics (CFD) simulations and NREL Phase VI experimental data with newly proposed normalized absolute error metric evaluate prediction accuracy modified blade element momentum (BEM) method non-yawed conditions, thereby quantitatively assessing differences between different yaw angles, velocities, spanwise positions. In addition, CFD results are employed analyze potential mechanisms responsible for deterioration BEM accuracy. The show that while maintains high nonyawed attached its performance deteriorates significantly separation inflow. At angle 30 an velocity 15 m/s, force coefficient (Cn) at root increases 48.6 %, exceeding case by more than 20 %. Flow field analyses reveal intensifies vortex interactions leeward side induces strong bands driven Coriolis forces, causing stall regions propagate from tip root. These phenomena lead severe loc... Read More

Source

Abstract. Floating offshore wind turbine (FOWT) systems are subject to complex environmental loads, with significant potential for damage in extreme storm conditions. Design simulations these conditions required assess the survivability of device some level confidence. Aero-hydro-servo-elastic engineering tools can be used a reasonable balance accuracy and computational efficiency. The models require many input parameters describe air water conditions, system properties, load calculations. Each has possible range, due either statistical uncertainty or variations time. Variation have important effects on resulting but it is not practical perform detailed assessments impact this every parameter. This work demonstrates method identify that most loads focus further inspection. process done specifically cases defined International Electrotechnical Commission design requirements floating turbines. analysis was performed using Energy Agency Wind 15 MW reference atop University Maine VolturnUS-S platform two US regions, Gulf Humboldt Bay. It found direction incident waves current, yaw misali... Read More

Source

Monitoring yaw system faults in wind turbines to improve reliability and reduce forced nacelle misalignment. The method involves checking if changes in yaw angle match control signals. If a yaw change occurs without a corresponding control signal, it indicates a yawing fault. By comparing yaw angle vs control signal, wind speed thresholds, and service intervals, the method detects faults like contact force loss, holding system failure, or wind misbalance. This enables proactive service planning and parts ordering to mitigate yaw misalignment risks.

Source

Control system for aligning a wind turbine nacelle with a target yaw angle, like downwind orientation, during extreme weather events when wind sensors fail or provide erroneous readings. The system uses other wind forces measurements to determine the current yaw angle. It yaws the nacelle until alignment, then stops. If sensors fail again, it keeps yawing. This ensures reliable downwind alignment without misalignment during wind sensor failures.

Source

Optimizing wind farm power generation using a system identification method to determine the optimal yaw angles and axial induction factors of wind turbines without needing a detailed wind farm model. The method involves iterative optimization using gradients obtained from a system identification model. The gradients are calculated by applying small excitation signals to the turbine setpoints and measuring the power output. This allows finding the gradients without a full wind farm model. The iterative optimization uses these gradients to adjust turbine angles and induction factors for maximum farm power. The optimization groups turbines based on wind direction to reduce variable count.

Source

Abstract Wake steering is a wind farm control strategy where the yaw angle of turbines intentionally misaligned with incoming direction to deflect wake in such way as increase power production from downstream exchange for producing less upstream turbines. This paper presents Deep Reinforcement Learning approach predicting optimal turbine misalignment using steadystate flow simulations and Proximal Policy Optimization (PPO) algorithm. leads 2.255.27% improvement over greedy strategy, averaged all incident directions, computing time than 30 s per configuration, although it does not outperform stateoftheart optimizers.

Source

Wind energy is paramount to the European Unions decarbonization and electrification goals. As wind farms expand with larger turbines more powerful generators, conventional greedy control strategies become insufficient. Coordinated approaches are increasingly needed optimize not only power output but also structural loads, supporting longer asset lifetimes enhanced profitability. Despite recent progress, effective implementation of multi-objective farm strategiesespecially those involving yaw-based wake steeringremains limited fragmented. This study addresses this gap through a structured review developments that consider both maximization fatigue load mitigation. Key concepts introduced support interdisciplinary understanding. A comparative analysis studies conducted, highlighting optimization strategies, modelling approaches, fidelity levels. The identifies shift towards surrogate-based frameworks balance computational cost physical realism. reported benefits include gains up 12.5% blade root reductions exceeding 30% under specific scenarios. However, challenges in mo... Read More

Source

Reducing load imbalance in wind turbine yaw systems during parked states to prevent overloading of components. The method involves balancing loads experienced by the yaw drives after stopping rotation and closing motor brakes. This is done by temporarily reducing brake friction or by early activation of the hydraulic brake during yawing. These steps help balance loads as the pinions engage the annular gear at different positions due to factors like motor brake wear. By reducing imbalance during parked states, it prevents overloading of yaw drive components that could lead to damage.

Source

Optimizing power output of multi-rotor wind turbines by accurately aligning the rotors with the wind. The method involves measuring the wind power parameter (Pw) for each rotor nacelle assembly at multiple relative wind directions. It finds the maximum Pw for each nacelle and averages them to get an optimal control wind direction. The yaw system is then controlled based on that optimal direction to align the rotors with the wind for maximum combined power.

Source

Method for controlling yaw of a wind turbine system using multiple yaw drive actuators to improve lifetime of components. The method involves calculating a single required motor torque reference for all actuators when the yaw system is operating. The mean motor speed is calculated from all actuators or a subset. This ensures even load distribution across the actuators. The required torque is limited and the speed is controlled to prevent excessive speeds or power use.

Source

Active control system for stabilizing the motion of floating wind turbines to improve performance and reduce loads. The system monitors the offset and oscillations of the pitch and yaw angles from their balanced positions. Actuators then adjust the angles and oscillations to bring them back to balance. This prevents excessive motion that can affect power production and cause loads. The system uses sensors like pressure, wind, strain gauges, and operation mode detectors. It can stabilize pitch and yaw separately or jointly.

Source

Controlling wind farm wake interactions is crucial for enhancing power generation efficiency, especially under the challenge of fluctuating conditions. This study tackles this imperative by leveraging deep reinforcement learning (DRL) to refine yaw control strategies. The approach innovatively segments conditions into discrete intervals, each governed a customized DRL policy, adeptly handling variability bolster system's adaptive capability and efficiency. comparative analysis incorporates traditional greedy control, differential evolution optimal model predictive DRL-based strategy, with evaluations grounded in extensive simulations tunnel tests. experiments conducted represent notable step forward, providing empirical evidence strategy's effectiveness practical applications first time. approach, characterized its model-free adaptability across diverse scenarios, achieves 9.27% enhancement total output compared during gust events. underscores capacity not only maintain but also surpass benchmarks varying conditions, while concurrently mitigating mechanical stress on turbines. DRL-co... Read More

Source

Shrouded fluid turbine with a hybrid yaw system that provides improved performance and protection in extreme conditions. The turbine has a ringed airfoil surrounding the rotor and an ejector shroud behind it. This shrouded design increases energy extraction by accelerating the fluid flow into the rotor and transferring energy from the bypass flow. The turbine also uses a hybrid yaw system combining passive yawing due to asymmetric forces and active yawing to counteract it. This allows the turbine to automatically orient in high winds to prevent damage. The support structure offset from the center of gravity and pressure passively yaws the turbine. An active yaw system overrides the passive yaw and holds the orientation.

Source

A system to optimize power output of a wind turbine when operating at yaw misalignment. The system has a control device that calculates target pitch and torque setpoints based on the turbine's yaw misalignment to compensate for reduced efficiency. This allows operating turbines at yaw angles for wake steering without degrading performance. A farm-wide control can also adjust turbine yaw angles to optimize overall power.

Source

Wind turbine yaw correction method and system that improves the accuracy of yawing wind turbines to align with the wind direction. The method involves using multiple lidars deployed based on the turbine locations to identify wind conditions in the target area. Real-time wind data is generated, angles calculated, and power generation changes determined. Comparing actual vs theoretical generation generates a yaw correction strategy. Simulating the turbine with the strategy provides yaw correction results. If within a threshold, the turbine is yawed. This rigorous, complete yaw correction process addresses limitations of existing methods.

Source

Method for controlling the yaw system of large wind turbines based on wind prediction to improve power generation efficiency and extend yaw system life. The method involves decomposing wind speed and direction time series using wavelet transformation to extract high-frequency and low-frequency components. A prediction model using an ultra-short-term memory network is trained on the low-frequency components to accurately predict wind speed and direction. The yaw angle is adjusted based on the predicted wind direction instead of real-time wind direction. This reduces yaw system movement for minor wind direction changes, improving life, while still responding to significant wind direction shifts for optimal power generation.

Source

Yaw and wind control method for wind turbines that optimizes yaw movements to better follow wind direction changes. The method involves determining the current yaw angle, checking if yaw conditions are met based on current wind deviation, starting a delay timer if conditions are met, and initiating yaw control when the delay time expires. The delay time increases with power and decreases with wind deviation to balance yaw frequency and output.

Source

Automatic yaw error correction for wind turbines using machine learning to improve power generation efficiency. The method involves collecting yaw feature values and measuring yaw angles, normalizing the features, predicting an offset interval using a model, calculating the yaw angle correction based on the measured values and median offset, and applying the correction to align the turbine with the wind.

Source

Yaw control method for wind turbines that improves power generation by reducing yaw oscillations. The method involves dynamically adjusting blade pitch during yaw maneuvers to balance loads. The yaw decision is made when wind misalignment exceeds a threshold for a certain duration. During yaw, blades pitch based on a gain determined from wind deviation. This increases hub torque moment, reducing yaw rate and deviation. The method avoids continuous yawing by stopping once misalignment falls below a threshold.

Source

Method for accurately controlling wind turbines in a wind farm during wind events using a yaw bias correction based on wind speed and historical wind data. The method involves calculating a yaw bias for each turbine during an event using historical wind direction and nacelle heading data. This bias is then applied to correct the turbine's yaw offset calculation during the event, accounting for biased wind sensors. This corrected yaw signal is then used to control the turbine during the event.

Source