Wind Farm Control Optimizing Yaw Angles for Energy and Load
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Solution Overview
Problem
Current wind farm control methods fail to optimize energy generation and reduce turbine fatigue effectively across various wind situations, as they lack precision in modeling turbulent wind dynamics and wake propagation, leading to suboptimal energy production and increased structural stress.
Innovation Solution
A real-time wind farm control method that acquires wind speed and direction distributions, constructs wind farm and load models, and determines target operating points for each turbine to balance energy maximization and fatigue reduction, using optimization techniques like weighted sums and Lagrangian methods to adjust yaw angles and other operating parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wake steering is applied to maximize total energy production, then energy generation is improved, but yaw misalignment increases turbine load and fatigue
Solution Approach 1:
The patent optimizes yaw angles by changing the parameter settings to balance energy production and structural load. The control system adjusts yaw angles dynamically based on wind conditions to achieve optimal compromise between maximizing farm production and minimizing individual turbine fatigue
Solution Approach 2:
The patent implements dynamic adjustment of yaw angles based on real-time wind conditions and turbine operating states. The control system continuously optimizes yaw angles to adapt to changing wind scenarios, balancing energy capture with structural load management
2Power
If conventional control strategies are used to maximize individual turbine performance, then single turbine energy capture is improved, but total wind farm production decreases due to wake effects
Solution Approach 1:
The patent merges individual turbine control with farm-level coordination by implementing a centralized optimization system that considers both individual turbine performance and overall farm production. The control strategy integrates wake effect modeling with turbine power optimization to achieve synergistic performance
Solution Approach 2:
The patent applies different control strategies to different turbines based on their position in the wind farm and local wind conditions. Upstream turbines may operate at higher power capture while downstream turbines adjust to wake conditions, optimizing overall farm performance through localized control adjustments
3Measurement precision
If precise modeling of turbulent wind dynamics and wake propagation is implemented, then control precision is improved, but computational complexity increases
Solution Approach 1:
The patent introduces an intermediary optimization layer that simplifies complex wake and turbulence models into practical control parameters. The system uses pre-computed optimization results and lookup tables to translate complex aerodynamic models into actionable yaw angle commands without requiring real-time complex calculations
Data Source
AI summary
The present invention is a wind farm control method implementing an acquisition (ACQ) of a wind speed and direction distribution, an acquisition of the wind speed and direction in real time (Vac), a wind farm model (MOD F) and a load model (MOD C) for each wind turbine. Finally, an optimization step (OPT) allows target operating points to be determined for each turbine. The optimization step implements optimization of an expected value of the energy generated for the entire wind speed and a direction distribution according to an expected value of the load of each turbine for the entire wind speed and direction distribution. The target operating points (target yaw angles for example) are then applied to the turbines of the wind farm (CON).