Wind Turbine Cluster Control for Wake Loss Mitigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wind turbines in a wind farm experience efficiency and power production losses due to wake interactions, which are influenced by varying wind conditions and cannot be completely eliminated by new layout optimizations.
Innovation Solution
A computer-implemented method that receives power production signals from wind turbines, estimates wake travel times, calculates correlations among all pairs of turbines, identifies turbine clusters based on dominant wake interaction directions, and applies a control strategy to optimize power production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wind turbines are placed closer together to increase wind farm density, then land use efficiency is improved, but wake interactions between turbines increase causing power production losses
Solution Approach 1:
The wind farm is segmented into multiple turbine clusters based on wake interaction analysis. By dividing the wind farm into discrete clusters with identified wake relationships, the system can apply targeted control strategies to each cluster, allowing dense packing while managing wake losses through localized coordination rather than treating the entire farm as a single unit.
Solution Approach 2:
The system dynamically identifies turbine clusters and their wake interactions in real-time based on varying wind conditions. The cluster configurations are not fixed but adapt as wind direction and speed change, allowing the wind farm to maintain optimal power production despite changing environmental conditions and close turbine spacing.
2Productivity
If turbine layout is optimized to reduce wake interactions, then power production efficiency is improved, but the ability to adapt to varying wind conditions is reduced
Solution Approach 1:
The turbine cluster configurations are determined dynamically based on real-time wind conditions rather than being fixed in the layout design. The system continuously analyzes wake interactions and reconfigures clusters as wind direction and speed vary, allowing the wind farm to adapt to changing conditions while maintaining efficiency optimizations for current conditions.
Solution Approach 2:
The system changes operational parameters of turbines within identified clusters (such as yaw angles and rotor speeds) based on wake interaction analysis. By adjusting these parameters dynamically, the system can optimize power production efficiency for current wind conditions while maintaining the ability to adapt when conditions change.
Data Source
AI summary
Enabling control of wind turbines is provided. The method comprises receiving power production signals from wind turbines comprising a wind farm and estimating wake travel times from upstream wind turbines to downstream turbines. Correlations of the power production signals are calculated among all pairs of wind turbines in the wind farm. Wind turbines with a power production correlation above a specified threshold at an expected time are considered to have wake interaction. A probability density function of northing directions is calculated for the wind turbine pairs with wake interaction. A determination is made whether the probability density function has a dominant direction. Responsive to the probability density function having a dominant direction, the wind turbine pairs with wake interaction are identified as turbine clusters. A control strategy is applied to each turbine cluster as an operational unit to optimize power production of the wind farm.