Wind Farm Wake-Aware Control Using Predicted Turbine Conditions
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Solution Overview
Problem
Current wind farm control strategies focus on maximizing the power generation capacity of individual wind turbines without considering the wake effect between them, leading to suboptimal overall power generation capacity of the wind farm.
Innovation Solution
A wind farm control strategy method that acquires incoming wind data and restriction relationships between turbines, inputs this data into a pre-trained working condition prediction model to determine optimal operating conditions for maximizing the overall power generation capacity, and adjusts turbine operations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If wind turbine operation is controlled to maximize single turbine power generation capacity, then individual turbine output is improved, but overall wind farm power generation capacity is reduced due to wake effects
Solution Approach 1:
The patent changes operational parameters (yaw angle, pitch angle, rotational speed) of wind turbines based on real-time wind conditions and wake effect predictions. The working condition prediction model predicts optimal parameters that balance individual turbine performance with overall farm productivity, allowing dynamic adjustment to maximize total power generation while accounting for wake effects between turbines.
2Ease of operation
If traditional control strategies are used that consider only individual turbine performance, then single turbine operation is simplified, but wake effects reduce overall wind farm efficiency
Solution Approach 1:
The patent introduces a working condition prediction model as an intermediary between raw wind data and turbine control commands. This model processes incoming flow wind data, turbine layout information, and current working conditions to predict optimal operational parameters, thereby managing the complexity of wake effect considerations while maintaining practical controllability and improving overall farm efficiency.
3Productivity
If real-time optimization of turbine operations is implemented to reduce wake effects, then overall power generation capacity is improved, but system complexity increases
Solution Approach 1:
The patent employs a pre-trained working condition prediction model that has been prepared in advance with training data encompassing various wind conditions and turbine configurations. This preliminary preparation allows the system to quickly predict optimal working conditions during real-time operation without performing complex calculations on-the-fly, thereby reducing computational complexity while maintaining real-time optimization capabilities for wake effect mitigation.
Data Source
AI summary
A wind farm control strategy method, apparatus and device, and a storage medium are provided. The method includes: acquiring incoming flow wind data of a target wind farm, a restriction relationship between wind turbines in the target wind farm and current working condition data of the wind turbines in the target wind farm; inputting the incoming flow wind data, the restriction relationship and the current working condition data into a pre-trained working condition prediction model to obtain target working condition data corresponding to a target generation power of the target wind farm, the target generation power being a maximum generation power of the target wind farm; and controlling operation of the wind turbines in the target wind farm based on the target working condition data.