Wind Turbine Wake Control Using Real-Time LIDAR Modeling
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Solution Overview
Problem
Current wake management strategies for wind turbines in wind farms are inefficient due to reliance on theoretical models that diverge from actual environmental conditions, leading to suboptimal operation and potential damage from wake effects.
Innovation Solution
Determine a current wake model based on real-time wind parameters from both affected wind turbines, using LIDAR measurements and operational data to optimize their operation, potentially switching to a previously determined model if divergence is within a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If theoretical wake models with predefined empirical factors are used to model wake effects, then wake management strategies can be implemented, but the models diverge from actual environmental conditions leading to suboptimal operation
Solution Approach 1:
The patent changes the parameters of the wake model by replacing predefined empirical factors with dynamically determined parameters based on real-time wind measurements from LIDAR and operational data from multiple wind turbines. This allows the model to adapt to actual environmental conditions while maintaining ease of implementation through systematic parameter adjustment.
Solution Approach 2:
The patent implements feedback by continuously measuring wind parameters using LIDAR and operational data from wind turbines, then using this feedback to determine and update wake model parameters in real-time. This closed-loop approach ensures the model remains accurate despite environmental variations.
2Measurement precision
If real-time wake modeling using LIDAR measurements and operational data is implemented, then wake modeling accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by using a single integrated control system that performs multiple functions: LIDAR data processing, operational data collection, wake model parameter determination, and control signal generation. This multi-functional approach improves accuracy while managing system complexity through consolidation.
Solution Approach 2:
The system applies self-service by using its own operational data and LIDAR measurements to automatically determine wake model parameters and optimize its performance without external intervention. The control system self-adjusts based on real-time conditions, reducing the need for complex external management.
3Reliability
If wake management strategies are implemented to reduce wake effects, then loads and power production are improved, but control system complexity increases
Solution Approach 1:
The patent applies dynamics by implementing a dynamic control system that continuously adjusts wake management strategies based on real-time wake model parameters. The control signals sent to pitch systems and brakes are dynamically modified according to current wake conditions, improving reliability while managing complexity through adaptive control.
Solution Approach 2:
The system applies preliminary action by determining wake model parameters and optimizing control strategies in advance based on predicted wake conditions. The control system prepares adjustment signals before wake effects fully manifest, allowing proactive load management while simplifying real-time control decisions.
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 allows for more accurate modeling and optimization of wind turbine operations, reducing wake-induced loads and power deficits, thereby improving the overall performance and longevity of wind turbines.
Implementation Method 1
determining one or more parameters of the wind at the first wind turbine, and determining one or more parameters of the wind at the second wind turbine
Data Source
Figure 1
Figure 2a~2b
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AI summary
Methods are provided of operating first and second wind turbines in a situation wherein presence of the first wind turbine affects the wind so that a wake is generated that affects the second wind turbine. These methods comprise determining parameters of the wind at first wind turbine and at second wind turbine. These methods further comprise determining a value of a parameter of a previously determined wake model to determine an current wake model. This value is determined based on the parameters of the wind at first wind turbine and at second wind turbine. These methods still further comprise optimizing the operation of the first and second wind turbines based on the current wake model. Control systems are also provided which are suitable for performing any of said methods of operating wind turbines. Wind farms are also provided comprising any of said control systems.