Wind turbine noise prediction requires processing multiple signal components across varying operational states and environmental conditions. Field measurements show that amplitude modulation (AM) noise can exceed 40 dB at distances up to 1km, while mechanical signatures from gear meshes and bearing contacts create distinct tonal elements between 100-1000 Hz. These acoustic patterns shift with wind speed, turbulence intensity, and atmospheric stability.

The fundamental challenge lies in accurately modeling the complex interactions between aerodynamic noise sources, mechanical vibrations, and environmental propagation effects while maintaining sufficient computational efficiency for real-time control applications.

This page brings together solutions from recent research—including machine learning approaches for AM noise prediction, acoustic-based condition monitoring systems, optimization methods for noise-constrained operation, and selective turbine control strategies. These and other approaches focus on practical implementation in operating wind farms while balancing noise reduction with power production goals.

1. Wind Farm Noise Reduction System with AI-Driven Turbine Parameter Adjustment

YUNDA ENERGY TECH GROUP CO LTD, YUNDA ENERGY TECHNOLOGY GROUP CO LTD, 2024

Optimizing wind farm noise reduction using AI to improve accuracy and efficiency compared to manual methods. The method involves using a noise reduction optimization model along with the wind turbine power generation model to find the optimal noise reduction solution that meets noise standards while maximizing power output. This is done by iteratively adjusting turbine operating parameters like blade pitch and tip speed ratio. The AI model learns the noise-power tradeoff relationship through training on wind farm data.

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2. Machine Learning-Based Method for Predicting and Controlling Amplitude Modulation Noise in Wind Turbines

VESTAS WIND SYSTEMS AS, 2024

Method to predict and control amplitude modulation (AM) noise generated by wind turbines, with the goal of mitigating AM noise levels perceived by people living near wind farms. The method involves: 1. Obtaining wind turbine data like conditions, sensor readings, and power output. 2. Predicting AM noise at a distance from the turbine using a machine learning model trained on historical turbine data. 3. Generating control data for the turbine based on the predicted AM noise. 4. Compare the predicted and measured AM noise to assess turbine contribution. 5. Retrain the model with measured noise if prediction accuracy is poor. 6. Store reduction data from control interventions to refine rankings.

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3. Machine Learning-Based Amplitude Modulation Noise Prediction and Control System for Wind Turbines

VESTAS WIND SYSTEMS AS, 2024

Predicting and mitigating noise generated by wind turbines using machine learning. The method involves predicting amplitude modulation (AM) noise, a low frequency pulsing sound, from wind turbine data using a machine learning model. This allows controlling the turbine operation to reduce AM noise at distances from the turbine. The AM noise prediction is based on factors like wind speed, direction, turbulence, etc. If measured AM exceeds the prediction, indicating nearby turbines contribute, control triggers like power reduction are set. If measured AM is lower, control is avoided. After intervention, AM reduction is monitored to refine predictions.

4. Wind Turbine Sensor Network with Real-Time Data Transmission and Cloud-Based Analysis

UNIV WUXI, WUXI UNIVERSITY, 2024

Wind power equipment data acquisition system for monitoring and optimizing wind turbine performance. The system uses sensors on wind turbines to collect real-time data on parameters like wind speed, rotor speed, temperature, and vibration. This data is transmitted to a cloud for analysis. By comparing real-time and predicted performance, issues can be identified, efficiency improved, and downtime reduced. Sensors also monitor noise intensity to detect turbine anomalies. The system provides timely performance data for maintenance scheduling and prevents safety hazards.

5. Wind Turbine Noise Management System Using Operating Parameters and Rainfall Data

CHINA STATE SHIPBUILDING CORPORATION WIND POWER DEV CO LTD, CHINA STATE SHIPBUILDING CORPORATION WIND POWER DEVELOPMENT CO LTD, CSSC WIND POWER CLEAN ENERGY TECH BEIJING CO LTD, 2024

Adjusting wind turbine operation to meet noise limits in nearby sensitive areas without requiring additional on-site noise measurement devices. The method calculates the noise in the sensitive area based on wind turbine operating parameters and nearby rainfall. It then adjusts the turbine to keep the total noise below a threshold. This allows optimizing power generation while avoiding excessive noise in sensitive locations.

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6. Wind Turbine Blade with Adjustable Trailing Edge Comb Serrations

WOBBEN PROPERTIES GMBH, 2024

Adaptive trailing edge comb on wind turbine blades to reduce noise emissions. The trailing edge comb has serrations that can be adjusted in geometry and orientation based on parameters like air density, wind speed, and load. This allows optimizing the comb shape for specific operating conditions to reduce noise compared to fixed comb designs.

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7. Noise Control System and Method for Wind Turbine Using Onboard Estimation and Adjustment Mechanisms

SIEMENS GAMESA RENEWABLE ENERGY INNOVATION & TECHNOLOGY SL, 2024

Method and arrangement for controlling a wind turbine to reduce noise while meeting local noise limits. The method involves estimating the turbine noise based on wind conditions and operational parameters. If the estimated noise exceeds a reference, the turbine is controlled to reduce noise. This can involve power reduction, blade pitch adjustment, or operating at a different power-speed curve. The noise estimation and control is implemented using an onboard arrangement with modules for noise estimation, noise reference management, and noise control.

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8. Acoustic Signal Analysis and Machine Learning Classification for Wind Turbine Yaw System Fault Detection

Guodian Power Hunan New Energy Development Co., Ltd., GD POWER HUNAN NEW ENERGY DEVELOPMENT CO LTD, 2023

Diagnosing abnormal sounds in wind turbine yaw systems using acoustic analysis to monitor and detect faults. The method involves capturing acoustic signals from the yaw system components, extracting features from the signals, and using machine learning to classify the features as normal or abnormal. This allows early detection and diagnosis of yaw system faults through acoustic analysis instead of relying on manual inspection or vibration sensors.

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9. Wind Turbine Noise Calculation Model Incorporating Terrain Correction Parameters

CSSC HAIZHUANG WIND POWER CO LTD, 2023

Accurately calculating wind farm noise levels taking into account terrain effects to improve prediction accuracy. The method involves modifying the wind turbine noise calculation model using terrain correction parameters for the specific site. This accounts for how terrain affects noise generation. The modified model is then used to calculate noise levels at predicted points in the site. Initial noise values are obtained. Then noise attenuation is calculated for factors beyond terrain. The final site-specific noise values are determined by subtracting attenuation from initial values.

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10. Wind Turbine Blade Noise and Vibration Reduction System with Active and Passive Control Mechanisms

HUANENG LANCANG RIVER HYDROPOWER CO LTD, XIAN THERMAL POWER RES INST CO, XIAN THERMAL POWER RESEARCH INSTITUTE CO LTD, 2023

A wind farm noise and vibration reduction system that uses active noise control and passive noise control techniques to effectively reduce wind turbine blade noise and vibration. The system involves installing secondary sound sources on the wind turbine blades, collecting blade noise signals, and using adaptive filtering algorithms to generate control signals that drive the secondary sources to emit offsetting sound waves. Passive mufflers are also used to control mid- and high-frequency noise. This allows real-time adaptation to blade noise changes, improving noise reduction and energy savings without increasing blade resistance or impacting performance.

11. Noise Mitigation Method for Wind Farms Using Selective Turbine Operation Based on Noise Propagation Model

VESTAS WIND SYSTEMS AS, 2023

Optimizing wind farm noise mitigation without reducing power output. The method involves using a noise propagation model that considers interactions between wind turbines and predicts noise levels at surrounding locations. To keep noise below limits, turbines are selectively operated based on optimization with constraints like max noise and total power. This reduces noise at critical points without excessive power loss.

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12. Noise Management Method for Wind Farms Using Predictive Model-Based Turbine Operation Adjustment

VESTAS WIND SYS AS, VESTAS WIND SYSTEMS AS, 2023

Method for controlling noise generated by a wind farm to reduce noise levels without significantly impacting power production. The method involves using a noise propagation model that takes into account wind farm layout, wind conditions, and turbine operating states to predict noise levels at nearby locations. When noise limits are exceeded, the method selects specific turbines to reduce power or stop based on the model to minimize overall farm power loss. This targeted approach allows meeting noise constraints without overly decreasing farm output.

13. Simulation Method for Noise Prediction Using Digital Blade Geometry and Computational Fluid Dynamics

DASSAULT SYSTEMES SIMULIA CORP, 2023

Computer-aided simulation method to accurately predict noise generated by wind turbine blades without the need for physical prototypes. The method involves using a digital representation of the blade geometry and importing it into a simulation software. The simulation calculates segmental airflow parameters, boundary layer transitions, and acoustic noise based on blade element momentum theory and viscous airfoil polar calculations. This allows predicting blade noise accurately without building physical prototypes.

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14. Method for Noise Representation of Rotating Wind Turbine Blade Using Computational Simulations and Blending Techniques

Dassault Systemes Simulia Corp., 2023

Method for accurately representing noise of a rotating wind turbine blade using computer simulations and blending techniques. The method involves importing a 3D blade geometry, extracting constructive parameters like airfoil profile and chord, and calculating low-order airflow using BEMT. This provides sectional angle of attack and velocity for scale-resolving CFD simulations. Noise spectra are computed at virtual microphones for each section. These are blended into a smooth variation over a rotor revolution. Inverse Fourier transforms generate synthetic signals with phase variations. Doppler correction and summation across revolutions complete the noise representation. Ground and atmospheric absorption are also applied.

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15. Wind Turbine Noise Management via Controlled Aerodynamic Device Deployment

Siemens Gamesa Renewable Energy A/S, 2023

Controlling wind turbine noise levels without sacrificing power output by optimizing add-on device usage. The method involves setting a target noise level and controlling the turbine's add-on aerodynamic devices to stay below that level while maximizing power generation. It limits the number of devices allowed to extend at once based on the noise requirements. This prevents reducing turbine speed or output power to lower noise.

16. Wind Turbine Noise Control System with Selective Operational Adjustment Based on Environmental Monitoring

HUANENG CLEAN ENERGY RES INSTITUTE, HUANENG CLEAN ENERGY RESEARCH INSTITUTE, HUANENG SHAANXI DINGBIAN ELECTRIC POWER CO LTD, 2023

Active noise control for wind turbines to reduce noise levels near residential areas without shutting down the turbines. The method involves monitoring the noise levels in the surrounding sensitive areas and selectively adjusting the wind turbine operation to mitigate the noise. This is done by comparing the sensitive area noise levels to a background level and choosing a noise reduction control scheme based on that difference. The chosen scheme, like varying generator speed, is then implemented to reduce the wind turbine noise. The goal is to effectively reduce wind turbine noise without resorting to shutdowns or other measures that impact power generation.

17. Sound Pressure Data Processing and Optimization Algorithm for Wind Turbine Fan Operation Parameters

HE ELECTRIC WIND POWER CO LTD, 2022

A method to reduce noise and minimize power loss in wind turbines by optimizing fan operation based on a noise control model. The method involves acquiring sound pressure data, establishing a sound pressure distribution model, processing it to obtain a modified model, and creating optimization indices for noise and power. These indices are then optimized using an algorithm to find the best fan operation parameters that minimize noise and loss.

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18. Wind Turbine Control System with Perturbation-Enhanced Set Point for Noise Emission Reduction

VESTAS WIND SYS AS, VESTAS WIND SYSTEMS AS, 2022

Reducing noise emissions of a wind turbine by applying a perturbation signal to the optimal operating set point to increase temporal variation and prevent resonances from building up. The method involves receiving wind data, determining the optimal operating set point based on that data, and applying a perturbation signal to the set point to modify it. This modified set point with greater variation is then used to control the wind turbine and reduce noise emissions compared to using the original set point.

19. System and Method for Real-Time Wind Turbine Noise Level Calculation and Selective Power Adjustment

BEIJING GOLDWIND SCIENCE & CREATION WINDPOWER EQUIPMENT CO LTD, 2022

Method, device, and system for predicting wind farm noise levels and optimizing wind turbine operations to mitigate noise pollution. The method involves calculating noise levels from individual turbines at real-time wind speeds, accounting for propagation losses, and summing to predict overall farm noise. By selectively adjusting turbine power instead of blanket shutdowns, it maximizes farm output while keeping total noise below limits.

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20. Spectral Analysis System for Identifying Tonal Noise and Operating Parameters in Wind Turbines

VESTAS WIND SYSTEMS AS, 2022

Analysis of noise emission from wind turbines to improve the reliability of the wind turbine. The analysis includes identifying tonal noise in a spectra of noise data, identifying the operating parameters of the wind turbine that are responsible for the generation of the tonal noise.

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21. Adaptive Wind Farm Noise Control via Dynamic Turbine Speed Adjustment Based on Environmental Noise Assessment

22. Method for Analyzing Wind Turbine Operating Parameters Correlated with Tonal Noise Generation

23. Vibration Sensor Correlation Method for Tonal Noise Prediction in Wind Turbines

24. Method for Dynamic Adjustment of Wind Turbine Rotational Speeds Based on Critical Location Noise Measurement

25. Wind Turbine Control Method Utilizing Predicted Operating Trajectories for Noise-Conscious Parameter Adjustment

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