Wind Turbine Yaw Offset Control for Wake Interference
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Solution Overview
Problem
Wind turbines in a farm face reduced overall performance due to wake interference from upstream turbines, making it challenging to achieve maximum aggregate power output when operated at individual optimum points.
Innovation Solution
Implementing a reinforcement learning algorithm to control yaw offsets of wind turbines, allowing for data-driven adjustments to minimize wake interference by determining optimal yaw offset settings based on current states of both upstream and downstream turbines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If each wind turbine is operated at its individual optimum operation point, then individual power production is maximized, but total power output of the wind farm decreases due to wake interference
Solution Approach 1:
The patent changes the operational parameters of wind turbines by introducing non-zero yaw offsets. Instead of operating each turbine at its individual optimum with zero yaw offset, the system adjusts the yaw angle parameter to deflect wakes away from downstream turbines, thereby increasing total farm power output while maintaining acceptable individual production levels
Solution Approach 2:
The patent implements a feedback mechanism where the control system continuously monitors the performance of upstream and downstream wind turbines and adjusts yaw offsets accordingly. This feedback loop allows the system to optimize wake management dynamically, balancing individual turbine performance with overall farm productivity
Data Source
AI summary
Methods, systems, and devices for controlling a yaw offset of an upstream wind turbine based on reinforcement learning are provided. The method includes receiving data indicative of a current state of the first wind turbine and of a current state of a second wind turbine adjacent to the first wind turbine downstream along a wind direction, determining one or more controlling actions associated with the yaw offset of the first wind turbine based on the current state of the first wind turbine, the current state of the second wind turbine, and a reinforcement learning algorithm, and applying the determined one or more controlling actions to the first wind turbine.