Wind Turbine Control Using Spatial Wind Field Estimation
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Solution Overview
Problem
Current wind turbine control technologies fail to accurately account for blade dynamics, leading to a lag in estimating effective wind speed and resulting in increased mechanical stress and reduced power performance due to extreme wind conditions such as gusts and shear.
Innovation Solution
A system and method that estimates a three-dimensional spatial wind field by monitoring operating conditions and determining individual wind speeds for each rotor blade, using a physics-based model and extended Kalman filter to account for rotor imbalance moments and azimuth angles, providing accurate spatial wind speed distribution for optimized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current control technologies estimate effective wind speed based on power, pitch angle, and generator speed, then the estimation process is simple, but the estimation lags behind actual wind conditions due to not accounting for blade dynamics
Solution Approach 1:
The patent implements feedback by using measured blade root flapwise moments and rotor azimuth angle to continuously update the wind speed estimation. The physics-based model processes these feedback signals to calculate individual wind speeds for each blade, correcting the lag inherent in traditional methods that don't account for blade dynamics.
Solution Approach 2:
The patent introduces blade root flapwise moments as an intermediary measurement to bridge the gap between direct wind speed measurement and turbine response. These moments serve as a mediator that captures the dynamic interaction between wind and blades, enabling more accurate wind speed estimation without requiring direct wind sensors on the blades.
2Reliability
If blade dynamics are not accounted for in wind speed estimation, then the control system is simpler to implement, but mechanical stress increases due to extreme wind conditions
Solution Approach 1:
The patent applies preliminary action by estimating individual wind speeds for each blade before extreme loads occur. The physics-based model continuously predicts wind conditions affecting each blade, allowing the control system to prepare and adjust pitch angles proactively, reducing mechanical stress from sudden extreme wind events.
Solution Approach 2:
The patent implements local quality by determining individual wind speeds for each rotor blade rather than using a single average wind speed. This allows differential pitch control where each blade can be optimized for its specific local wind conditions, improving reliability under extreme wind while managing complexity through targeted local adjustments.
3Productivity
If average wind speed is used for control decisions, then the control logic is simpler, but power performance decreases due to inability to optimize for spatial wind variations
Solution Approach 1:
The patent applies segmentation by dividing the wind field into individual wind speeds for each rotor blade rather than treating it as a single average value. This segmentation enables the control system to optimize pitch angles for each blade according to its specific wind conditions, maximizing power extraction from spatial wind variations across the rotor sweep.
Solution Approach 2:
The patent introduces another dimension by adding the spatial distribution dimension to wind speed measurement. Instead of a single scalar average wind speed, the system creates a spatial wind field model with wind speeds distributed across different blade positions, enabling three-dimensional optimization of turbine performance based on spatial wind variations.
Data Source
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AI summary
The present disclosure is directed to a method for controlling a wind turbine 10 having a rotor 18 with a plurality of rotor blades 22 mounted thereto based on a spatial wind speed distribution 115. The method includes monitoring, via at least one sensor 46, 48, one or more operating conditions of the wind turbine 10. The method also includes determining a rotor azimuth angle 106 of the wind turbine 10. In addition, the method includes determining, via a physics-based model 102, at least one individual wind speed for one or more of the rotor blades 22 of the wind turbine 10 based on the one or more operating conditions and the rotor azimuth angle 106. The method also includes determining a spatial wind speed distribution 115 of the wind turbine 10 based on the at least one individual wind speed. Thus, the method further includes controlling the wind turbine 10 based on the spatial wind speed distribution 115.