Steady Power Output in Wind Turbines

Optimizing power generation from clusters of fluid turbines like wind turbines by coordinating their operations to improve efficiency and grid compliance. The method involves using sensors, controllers, and storage devices in each turbine to monitor and regulate power output. When turbines generate below a threshold, excess power is stored. When grid conditions allow, the stored power is released. This allows turbines to keep generating even in low wind conditions, while avoiding grid issues. The method also involves coordinating rectifier inputs, DC-DC converters, and AC inversion to balance voltages across turbines.

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An impeller for wind turbines that maximizes power output at lower wind speeds and reduces blade size compared to conventional wind turbine rotors. The impeller has a rear ring with a central axis that the blades attach to. This allows the blades to pivot around the ring during rotation, increasing the effective swept area of the impeller at lower wind speeds. It also allows using shorter blades for the same power output since the pivoting motion increases the blade tip speed. The pivoting motion is constrained to prevent excessive blade deflection.

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Double-set blade wind turbine design with two concentric cylinders of blades around the central hub. The inner cylinder has blades that rotate with the wind, while the outer cylinder has fixed blades. The blades on the inner rotating cylinder have a different curvature compared to the outer fixed cylinder. This asymmetric blade configuration improves power output by reducing wind interference between the blades as they rotate. The fixed outer blades prevent the wind from stalling the inner rotating blades, allowing them to continue generating power.

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Dual-mode control method and system for timely monitoring of wind turbines based on wind speed to improve wind power generation efficiency. The method involves using machine learning to predict wind speed trends, analyze factors affecting wind speed changes, and quantify wind speed variability. This allows timely adjustment of wind turbine operation to optimize power generation in response to changing wind conditions.

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Optimizing wind farm active power regulation during grid frequency fluctuations using model predictive control to accurately allocate adjustment amounts to each wind turbine. This involves quantifying the active support capability of wind turbines using simulation when they only use rotor kinetic energy for short-term regulation. The turbine's maximum up and down power adjustments are calculated under different wind speeds. Then during frequency deviations, the total farm adjustment is computed and allocated per turbine via MPC to avoid exceeding their support limits and prevent rotor speed issues.

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Power generation control method for wind turbines using a SCADA system to optimize performance and reduce failures. The method involves monitoring wind turbine equipment, generating expected power based on wind speed, comparing actual power, and adjusting parameters if needed. The comparison uses evaluation coefficients to determine when to correct. If the actual power deviates significantly from expected, a correction is generated. If the deviation is smaller, a trend analysis is done to decide if a correction is needed. This allows targeted adjustments to wind turbine parameters based on actual operating conditions.

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Method for evaluating the transient active power regulation capability of wind turbines using rotor kinetic energy control. It provides a rapid and accurate way to assess how much active power a wind farm can provide during grid disturbances. The method involves optimizing wind turbine control instructions to maximize active power support while considering rotor speed, torque, and inverter capacity constraints. An optimization algorithm is used to find the optimal adjustment amounts and response times. This allows rapid calculation of the wind farm's transient active power support capacity.

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Optimizing the droop coefficient of wind turbines using model predictive control to improve frequency regulation and stability of wind power systems. The method involves collecting system data, building a frequency response model, setting an initial droop value, iteratively optimizing the droop through model predictive control, and simulating time domain operation to find the optimal sequence of droop coefficients for wind turbines during frequency disturbances.

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Optimizing wind turbine operation to balance energy production, grid stability, and maintenance scheduling. The method involves forecasting operating parameters like turbine health and wind conditions, then determining a control scheme for the turbine that balances factors like energy production, failure risk, and grid demand. It uses evaluation matrices and optimization techniques to find the best timing and power limits for shutdowns and operation. This balances factors like energy production, grid stability, and maintenance scheduling.

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Method and system for switching operating states of wind turbines that accurately predicts and controls turbine operation for improved efficiency, fault handling, and wind load mitigation. The method involves collecting turbine and wind data, determining status modes like startup, generation, shutdown, etc., predicting optimal adjustments based on models, and regulating blade angle and speed using a PID controller. This allows targeted response to wind conditions and faults rather than blanket shutdowns.

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Online monitoring of wind turbine power using machine learning to improve reliability and timeliness compared to existing methods. The method involves training a wind speed-power mapping model using historical data, calculating power discreteness in wind speed intervals, and using that to assess wind turbine power normality in real time. This allows quick detection of abnormal power and reduces losses compared to waiting for periodic offline monitoring.

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Controlling the rate of change of power in wind farms using inertia response of wind turbines to smooth power output. The method involves monitoring the rate of change of active power in the wind farm, and independently controlling each wind turbine's power change rate based on its inertia response. This reduces turbine-level power rate changes. If the wind farm-wide power rate change exceeds a threshold, coordinated control is triggered to further reduce the overall power rate change. By leveraging turbine inertia, it smoothens wind farm power output compared to just changing power quickly.

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Wind turbine control method that mitigates oscillations and instability caused by fluctuating wind speeds. It considers short-term fluctuations in wind speed and generator speed to determine if the turbine dynamics will be affected. By calculating an index based on these fluctuations, the method can instruct the main turbine control system to change pitch or shut down to prevent oscillations. This prevents dynamic issues caused by imperfect logic when wind speeds fluctuate greatly.

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Intelligent control of wind turbines that can accurately predict and manage the load conditions of the turbine blades under different wind conditions. The method involves training a neural network model using weighted parameter distributions to accurately predict blade load for different wind conditions. This allows safe operation of the turbines by intelligently controlling them based on predicted loads. The weights are determined by analyzing correlation between wind parameters and load for different blade positions.

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Optimizing wind turbine performance during noise reduced operation to maximize energy production without accelerating component wear. The method involves dynamically adjusting turbine power output based on grid conditions instead of fixed noise reduction modes. When prioritizing component life, turbine limits power near sync speed to prevent grid issues. When prioritizing energy, it operates at max rating tracking component degradation. This allows higher output without consuming life faster.

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A wind farm power control system that allows wind turbines to respond to grid dispatch commands for better grid stability. The system involves monitoring the actual power output of each turbine and using a central controller to generate power change signals based on grid commands. These signals are sent to the turbine control modules to adjust turbine parameters and optimize output. This allows the turbines to dynamically respond to grid requirements and improve wind farm power regulation.

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Model predictive control (MPC) of wind turbines that is robust to gusts and parametric variations in operational parameters. The MPC algorithm uses a wind turbine model to predict future states and optimize control actions over a horizon. To match the sampling rate of measured sensor inputs to the predicted values, the MPC calculation rate is lower than the sensor sampling rate. This ensures accurate timing between measured and predicted values. This allows using higher sensor sampling rates than the MPC calculation rate to improve accuracy. The lower MPC calculation rate allows using a more complex wind turbine model with parameters like aerodynamic thrust and power, rather than just rotor speed.

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Optimizing wind turbine performance and control using physics-based modeling and machine learning. The method involves leveraging detailed wind turbine models to estimate aerodynamic states and operating conditions in real time. It uses multiple physics-based aeroelastic estimators and machine learning algorithms to accurately determine wind resource and fault conditions based on operational data. The estimator configurations and control strategies are adaptively configured based on the estimated states to provide optimal performance and fault management. This enables full utilization of aeroelastic predictive control for core turbine functions.

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Controlling the power output of a wind farm to enhance power transfer and stability by determining the grid strength and adjusting power generation accordingly. The method involves receiving grid measurements from the wind farm location, generating a grid model, computing the grid strength, and controlling the wind farm power output based on the computed grid strength. This allows for real-time optimization of the wind farm operation based on the current grid conditions.

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Coordinated control of a multi-rotor wind turbine system using local model predictive control (MPC) for each wind turbine module along with a central controller. The local MPC optimizes module performance while the central controller synchronizes module actions. The local MPC predicts module motion using a simplified structural model that accounts for forces on the module but not other modules. This reduces computational requirements. The central controller provides timing commands to ensure coordinated module state changes.

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