LIDAR-Assisted Wind Turbine Control for Induction Correction
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Solution Overview
Problem
Wind turbines face challenges in accurately predicting and responding to wind gusts and changes in wind conditions due to time lags in measurement and control, leading to excessive loading and potential damage, which existing technologies fail to adequately address.
Innovation Solution
The implementation of LIDAR-assisted wind turbine control systems that use upwind speed measurements and model-based processors to generate mean and dynamic induction models, correcting LIDAR wind speed estimates for static and dynamic induction effects, enabling preemptive control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If LIDAR measurements are used to predict wind gusts, then response time is improved, but measurement accuracy deteriorates due to induction effects
Solution Approach 1:
The system uses rotor speed measurements as feedback to continuously correct the LIDAR wind speed estimates. The controller monitors actual rotor speed and compares it with predicted rotor speed based on LIDAR measurements, then applies correction factors to account for induction effects, thereby maintaining measurement accuracy while preserving the fast response time advantage of LIDAR.
Solution Approach 2:
The system dynamically adjusts the wind speed measurement parameters by applying correction factors that change based on operating conditions. The controller modifies the effective wind speed value used for control decisions by incorporating induction effects, transforming the raw LIDAR measurement into an accurate representation of actual wind conditions at the rotor plane.
2Device complexity
If induction effects are not corrected, then system complexity is reduced, but wind speed estimation accuracy deteriorates
Solution Approach 1:
The system introduces an intermediary correction mechanism that mediates between the raw LIDAR measurements and the final control decisions. The controller acts as an intermediary by processing rotor speed data and generating correction factors that adjust the wind speed estimates, thereby achieving accurate measurement without requiring complex direct measurement systems.
3Strength
If preemptive control actions are taken, then turbine loading is reduced, but control system complexity increases
Solution Approach 1:
The system performs preliminary action by using LIDAR measurements to predict wind gusts before they reach the rotor. The controller proactively adjusts blade pitch angles based on predicted wind conditions, preventing excessive loading before it occurs. This preemptive control is enabled by the fast response time of LIDAR and the continuous correction mechanism that accounts for induction effects.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the accuracy of wind speed estimation and prediction, reducing excessive loading on wind turbines by accounting for induction effects, thereby enhancing operational efficiency and reducing the risk of damage.
Implementation Method 1
LIDAR (light detection and ranging) sensors can be used to measure wind speed, direction, and other parameters upwind of the turbine
Implementation Method 2
LIDAR emits laser and/or three-dimensional scanning, which is reflected onto one or several targets
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to provide wind turbine 100 control and compensate for wind induction effects 320. An example method includes receiving wind speed data from a Light Detecting and Ranging (LIDAR) sensor 148. The example method includes receiving operating data 520 indicative of wind turbine 100 operation. The example method includes determining an apriori induction correction for wind turbine 100 operating conditions with respect to the LIDAR wind speed data based on the operating data. The example method includes estimating a wind signal from the LIDAR sensor 148 that is adjusted by the correction. The example method includes generating a control signal for a wind turbine based on the adjusted LIDAR estimated wind signal 670.