Wind Turbine Reinforcement Learning for Adaptive Cut-In Control
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Solution Overview
Problem
Existing wind turbine control systems rely on predefined cut-in wind speed thresholds, which fail to account for various environmental parameters like wind shear, turbulence, and air density, leading to suboptimal power output and inefficient operation.
Innovation Solution
Implementing a reinforcement learning algorithm that receives data on the current environmental state of the wind turbine to determine and apply controlling actions, such as start-up or idling, based on a range of environmental parameters including wind speed, turbulence, temperature, and site elevation, allowing for adaptive and optimal energy production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a predefined cut-in wind speed threshold is used for wind turbine control, then the control system is simple and easy to operate, but the power output is not optimal and the control precision is insufficient
Solution Approach 1:
The patent applies parameter changes by transitioning from a single fixed cut-in wind speed threshold to multiple dynamic thresholds that vary based on environmental conditions. The system adjusts the cut-in wind speed parameter according to temperature, air density, and other environmental factors, allowing the wind turbine to start operating under a broader range of conditions and maximize power output while maintaining operational simplicity through automated parameter adjustment.
2Ease of operation
If a predefined cut-in wind speed threshold is used for wind turbine control, then the control system is simple, but the measurement precision of environmental parameters is insufficient
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring environmental parameters such as temperature, air density, and wind speed, and using this information to dynamically adjust the cut-in wind speed threshold. The system receives feedback from sensors measuring these parameters and automatically updates the control thresholds, thereby improving measurement precision and environmental adaptability while keeping the control system relatively simple through automated feedback loops.
3Productivity
If environmental parameters like wind shear and turbulence are considered, then the power output optimization is improved, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the control system into modular components, each responsible for specific environmental parameters (wind speed, temperature, air density, wind shear, turbulence). Each module processes one type of parameter independently and contributes to the overall cut-in threshold determination. This modular segmentation allows the system to consider multiple complex parameters while maintaining manageable system complexity through organized, independent functional blocks.
4Ease of operation
If a local cut-in wind speed is defined, then the control system is simple, but the adaptability to different environmental conditions is poor
Solution Approach 1:
The patent implements dynamics by transforming the static, fixed cut-in wind speed threshold into a dynamic threshold that automatically adjusts according to real-time environmental conditions. The system continuously updates the cut-in threshold based on current temperature, air density, wind shear, and turbulence levels, enabling the wind turbine to adapt to varying environmental conditions while maintaining operational simplicity through automated dynamic adjustment rather than manual reconfiguration.
Data Source
AI summary
Methods, systems, and devices for wind turbine control based on reinforcement learning are disclosed. The method comprises receiving data indicative of a current environmental state of the wind turbine, determining one or more controlling actions of the wind turbine based on the current environmental state of the wind turbine and a reinforcement learning algorithm, and applying the determined one or more controlling actions to the wind turbine.