Wind Farm UAV Sensing for Turbine Load and Energy Control
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Solution Overview
Problem
Existing wind turbine monitoring systems, such as met masts, are expensive and require data filtering, leading to inefficiencies in optimizing wind turbine performance due to unpredictable wind conditions, which can cause component damage and downtime.
Innovation Solution
Utilizing unmanned aerial vehicles (UAVs) equipped with sensors to collect and analyze wind conditions around wind turbines, employing machine learning algorithms to generate control actions for optimizing turbine performance, including rotor blade adjustments and positioning to minimize loads and maximize energy production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If met masts are used to monitor wind conditions, then objective readings of wind speed can be obtained, but the system becomes expensive and requires data filtering
Solution Approach 1:
The patent replaces the mechanical met mast system with an acoustic Doppler velocimetry system that uses sound waves to measure wind velocity. This substitution eliminates the need for physical towers and mechanical sensors, reducing both installation cost and data filtering requirements while maintaining measurement accuracy.
Solution Approach 2:
The patent introduces acoustic waves as an intermediary medium to measure wind conditions. By using sound waves that propagate through the air and reflect off particles, the system can indirectly measure wind velocity without requiring direct physical contact with the wind, thus avoiding the complexity of mechanical measurement systems.
2Ease of operation
If sensors are mounted on the nacelle to monitor wind conditions, then monitoring is integrated with the turbine, but the readings are affected by turbine blade pitch
Solution Approach 1:
The acoustic Doppler system uses sound waves as an intermediary to measure wind conditions in the vicinity of the turbine without being affected by the turbine's mechanical components. The acoustic measurements are taken in the free air stream, isolating the measurement process from blade pitch interference while maintaining integration with the turbine control system.
Solution Approach 2:
The patent replaces mechanical sensors mounted on the nacelle with an acoustic measurement system that operates independently of the turbine's mechanical movements. This substitution eliminates the interference from blade pitch while maintaining ease of operation through electronic integration with the turbine control system.
3Measurement precision
If data filtering is applied to met mast readings, then data quality is improved, but information loss occurs
Solution Approach 1:
The acoustic Doppler velocimetry system inherently provides high-quality data without requiring extensive filtering because it measures wind velocity directly through acoustic wave propagation. The system captures complete wind condition information including directional data and temporal variations, eliminating the need for aggressive data filtering that would otherwise cause information loss.
Solution Approach 2:
By using acoustic waves as an intermediary measurement medium, the system captures comprehensive wind field information that naturally requires minimal filtering. The acoustic method preserves more complete wind condition data compared to mechanical sensors, reducing information loss while maintaining data quality.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and efficiency of wind turbine performance optimization by providing real-time data analysis and predictive capabilities, reducing downtime and component damage through precise control actions.
Implementation Method 1
emitting, via the emitter, an acoustic signal; receiving the acoustic signal via the acoustic receiver; and determining the one or more wind conditions based on changes in the acoustic signal as the acoustic signal moves from the emitter to the receiver
Data Source
AI summary
A method for optimizing performance of a wind farm having at least one wind turbine includes maneuvering a first unmanned aerial vehicle (UAV) having at least one sensor to a first location near the at least one wind turbine of the wind farm; collecting, via the at least one sensor of the first UAV, data corresponding to one or more wind conditions at the at least one wind turbine; receiving the data corresponding to the one or more wind conditions at the at least one wind turbine via a controller; generating a control action for the at least one wind turbine using the data corresponding to the one or more wind condition at the at least one wind turbine; and implementing, via the controller, the control action.