Wind Farm Attenuation Model for Noise-Optimized Turbine Control
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Solution Overview
Problem
Current noise propagation models for wind turbines are unreliable, especially in complex terrain or during crosswind propagation, leading to suboptimal operation and power loss in wind farms due to conservative noise estimates to comply with regulatory constraints.
Innovation Solution
A system and method that estimate attenuation coefficients using source sound power values and receptor sound pressure values to determine optimized turbine set-points for wind turbines, minimizing power loss while adhering to noise regulations, employing a signal acquisition module, attenuation model generator, and farm control optimization module to adjust rotor speed and pitch angles dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise propagation models are used to estimate far-field noise, then regulatory compliance can be assessed, but the models are unreliable in complex terrain or crosswind propagation leading to suboptimal operation
Solution Approach 1:
The system implements feedback by using actual measured noise data from far-field receptors to continuously update and refine the attenuation model. This measured feedback loop allows the model to adapt to real-world conditions including complex terrain and crosswind propagation, improving reliability while enabling optimal turbine operation decisions.
Solution Approach 2:
The system changes the parameters used for noise estimation by transitioning from relying solely on theoretical propagation models to using empirically derived attenuation coefficients based on actual measurements. This parameter change allows the system to account for complex terrain and meteorological conditions that standard models cannot accurately predict.
2Reliability
If conservative noise estimates are used to ensure regulatory compliance, then noise constraints are satisfied, but wind turbines must operate at suboptimal set-points reducing power production
Solution Approach 1:
The system uses feedback from actual noise measurements to determine whether conservative estimates are truly necessary. By comparing predicted noise levels with actual measured levels at receptors, the system can identify when conservative operation is unnecessary and adjust turbine set-points to optimize power production while still ensuring compliance.
Solution Approach 2:
Instead of applying conservative noise estimates uniformly to all turbines, the system applies partial action by using optimization algorithms to determine the minimum necessary derating for each specific turbine and receptor configuration. This allows most turbines to operate at optimal set-points while only applying necessary noise control where actually required.
3Object-generated harmful factors
If wind turbines are de-rated to reduce far-field aerodynamic noise, then noise emissions are reduced, but power production of the wind farm decreases
Solution Approach 1:
The system applies local quality by determining individualized set-points for each wind turbine based on its specific location, operational characteristics, and impact on nearby receptors. This allows noise control to be applied locally only where necessary rather than uniformly across the entire wind farm, minimizing the impact on overall power production.
Solution Approach 2:
The system implements dynamic set-point adjustment based on real-time conditions including wind direction, speed, turbine operational state, and receptor locations. This dynamic approach allows the system to minimize noise only when and where necessary, rather than applying static conservative derating that would continuously reduce power production.
4Ease of operation
If uniform set-points are applied to all wind turbines, then operation is simplified, but power loss occurs due to inability to optimize individual turbine performance
Solution Approach 1:
The system applies segmentation by dividing the wind farm into individual turbine units, each with its own optimized set-point calculated based on its specific characteristics and environmental conditions. This segmentation allows the control system to treat each turbine independently for optimization purposes while still providing a centralized framework for managing the entire wind farm.
Data Source
AI summary
A method implemented using at least one processor module includes receiving a plurality of operational parameters corresponding to a plurality of wind turbines and obtaining a plurality of source sound power values corresponding to the plurality of wind turbines. The method further includes obtaining a receptor sound pressure value corresponding to a receptor location and estimating an attenuation model based on the plurality of source sound power values, and the receptor sound pressure value. The attenuation model disclosed herein comprises a plurality of attenuation coefficients. The method also includes determining at least one turbine set-point corresponding to at least one wind turbine among the plurality of wind turbines based on the plurality of attenuation coefficients, and the plurality of turbine operational parameters.