Yaw Misalignment Control in Wind Turbines

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Yaw optimization method for wind turbines that uses short-term wind direction prediction to improve turbine performance. The method involves collecting real-time wind direction data, predicting the short-term future wind direction based on that data, and using the predicted wind direction to optimize the turbine yaw strategy. This allows the turbine to better anticipate and respond to changing wind conditions for improved efficiency. The prediction can be done using statistical analysis of historical wind data.

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Yaw control method for wind turbines to reduce loads during blade jamming and shutdown to reduce costs. The method involves adjusting yaw angle based on wind speed and blade positions. When blades are stuck but wind is low, face the wind to reduce vortex loads. When blades are stuck and wind is high, turn sideways to balance forces. This prevents unbalanced aerodynamic forces when turbine is stopped.

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Reducing the cost of yaw control in multi-rotor wind turbines by using the thrust forces of the rotors instead of a separate yaw motor. The method involves adjusting the pitch angles of the rotors in response to a yaw error greater than a threshold. By making the wind forces on the rotors unequal, it generates a yaw moment to reduce the yaw error. This eliminates the need for a dedicated yaw motor and reduces tower diameter requirements compared to traditional yaw systems.

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A wind turbine yaw control method using reinforcement learning to adaptively optimize yaw tracking for wind turbines. The method involves training a Q-learning agent to find optimal yaw control actions for different wind conditions using a model-free learning algorithm. The agent learns to control the yaw system based on wind speed and direction inputs. This allows the yaw controller to adapt and improve tracking performance compared to fixed parameters.

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Controlling a wind turbine to reduce loads during yaw misalignment by stalling the blades instead of shutting down. The method involves monitoring the yaw angle, wind direction, and wind speed. If the angular misalignment exceeds a threshold, possibly dependent on wind speed, the blades are stalled by pitching beyond a critical angle or activating trim devices. This deteriorates blade efficiency but reduces loads compared to normal operation. This allows continued turbine operation instead of shutdown during misalignment.

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Calibrating yaw systems of wind turbines to improve performance and reduce fatigue. The method involves selectively yawing the turbine in opposite directions, measuring wind direction and performance parameters before and after, and comparing the differences. By averaging multiple yaw events, it isolates the true yaw misalignment from other factors. This active yaw calibration can be combined with passive yaw events to further refine the misalignment estimate.

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A method to reduce vibrations in wind turbine rotor blades without significant power loss by actively dampening the vibrations using the wind turbine's yaw system. The yaw control algorithm is modified to also depend on a value indicative of the rotor blade vibration mode frequency. This allows the yaw drives to actively dampen the vibrations by controlling the nacelle rotation speed at the vibration frequency. A feedback loop adjusts the yaw torque based on the measured yaw speed to counteract the blade vibrations.

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A method to optimize yaw control of wind turbines in turbulent wind conditions. It involves adjusting the yaw system response based on efficiency considerations. The method involves capturing yaw error, processing it with efficiency information, and using the result to generate the yaw control signal. If the captured error exceeds a threshold, a true yaw control signal is generated. But if the filtered error is below the threshold, a false signal is generated to inhibit yaw actuation. This prevents excessive yawing in turbulent winds without compromising long-term orientation. The threshold and filter adjustments are optimized to balance yaw stability and efficiency.

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A wind turbine yaw control system that improves accuracy and efficiency of yawing to face the wind while reducing yawing frequency and forces on the turbine. The system uses a laser radar to accurately measure wind direction instead of relying solely on a wind vane. It analyzes wind direction data to determine the optimal yaw angles and stopping points based on wind variability. This prevents excessive yawing and continuous yawing that wears components. The system also has features like untwisting control to prevent cable entanglement during frequent yawing.

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Optimized method for automated yaw control of wind turbines using vibration monitoring instead of wind direction sensors. If the wind direction signal is normal, the turbine calculates deviation from received wind direction. If wind direction is abnormal, it calculates wind direction from vibration sensor readings and uses that to yaw the turbine. This allows the turbine to continue yawing even if wind direction sensors fail.

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Method to optimize power production in wind farms by dynamically deciding whether to use yaw control to mitigate wake effects based on variables like wind speed, shear, turbulence, and orientation. The method involves determining a decision function using simulation to compare energy production with and without yaw control. This function provides activation parameters for individual turbines. The controller uses the function to decide whether to enable or disable yaw control for each turbine based on current conditions. This adaptive approach considers the variable interactions and probabilistic nature of wind variability to improve wake loss compensation.

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Wind direction prediction and yaw control method for wind turbines that improves efficiency by accurately predicting wind direction and avoiding excessive yawing. The method involves using a wind direction detector to continuously monitor wind directions at multiple turbines. The data is decomposed into intrinsic mode components (IMFs) representing different frequencies. Each IMF is separately predicted using neural networks. The predicted IMFs are combined to reconstruct the overall wind direction. This predicted wind direction is used to determine if yawing is needed. If the nacelle is already aligned, yawing is avoided to save time and reduce bearing wear. If the predicted wind angle exceeds a threshold, yawing is executed using a yaw deviation threshold specific to wind speed.

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