37 patents in this list

Updated:

Wind turbines are powerful sources of renewable energy, yet their noise can disrupt nearby communities. The sound varies with wind speed, blade position, and environmental conditions, creating a dynamic challenge for engineers. Understanding and predicting noise levels is essential to minimize impact while maintaining efficiency in energy production.

Engineers face complex challenges in noise management, including identifying noise sources, predicting fluctuations, and implementing effective reduction strategies. These tasks require precise data and advanced analysis to balance operational performance with community comfort.

This page explores advanced methods for noise prediction and reduction, featuring research on machine learning algorithms and sensor networks. These approaches enable real-time monitoring and adjustment, ensuring quieter operation without compromising energy output. By leveraging these insights, professionals can improve turbine design and operation for more harmonious integration into their environments.

1. 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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2. 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.

3. 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.

4. 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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5. 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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6. 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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7. 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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8. Blade Noise Comparison and Anomaly Detection System for Wind Turbine Blades

China Energy Conservation and Environmental Protection Wind Power Co., Ltd., CHINA ENERGY CONSERVATION WIND POWER CO LTD, 2023

System and method for detecting abnormal noise in wind turbine blades to identify specific blades with problems like damage or contamination. The system involves collecting blade noise and rotational speed, comparing between blades, and using noise analysis to detect anomalies. If differences exceed a threshold, it indicates noise issues on those blades.

9. Acoustic Signal Processing System for Identifying Surface Irregularities on Wind Turbine Blades

ALTRAN INNOVACION S L, ALTRAN INNOVACIÓN SL, 2023

Detecting irregularities and deterioration on the surface of wind turbine blades using acoustic analysis. The system involves mounting microphones on the turbine to capture blade noise during operation. The captured signals are processed to identify irregularities like ice, dirt, or damage that alter blade aerodynamics. The processing involves extracting blade-specific acoustic patterns and comparing them to known patterns with irregularities. By detecting deviations from normal blade noise, the system can identify irregularities without visual inspection.

10. Wind Turbine Noise Suppression System with Positioning Device, Environmental Noise Sensor, Sounding Device, and Controller with Noise Pollution Avoidance Zones Database

Taiyuan Heavy Industry Co., Ltd., TAIYUAN HEAVY INDUSTRY CO LTD, 2023

A wind turbine noise suppression system to mitigate wind turbine noise impact without affecting power generation. The system uses a positioning device, environmental noise sensor, sounding device, and controller in the turbine. The controller has a database with noise pollution avoidance zones around the turbine. The steps are: position the turbine to avoid noise zones, measure external noise, play a sound to match it, and adjust turbine operation if needed.

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11. 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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12. 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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13. 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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14. Adaptive Noise Monitoring and Control System for Wind Farms with Dynamic Operation Adjustment Mechanism

ZHEJIANG WINDEY CO LTD, 2021

Wind farm noise monitoring and control system that reduces noise levels near sensitive areas like residential zones without excessive power loss. The system has a monitoring unit near the sensitive area that collects noise data. If noise exceeds thresholds, it triggers signal transmission to the main control system. The main control system then reduces unit operation to lower noise levels. This dynamic, adaptive noise reduction avoids fixed reductions that can lead to excessive power loss.

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

Vestas Wind Systems Group Company, VESTAS WIND SYSTEMS AS, 2021

Method to analyze and predict tonal noise emissions from wind turbines and control turbine operation to reduce or avoid tonal noise. The method involves identifying the vibration sensor whose data correlates with tonal noise in a specific region of interest. This sensor can then be used to predict and monitor tonal noise generation. By capturing vibration data from multiple sensors around the turbine, the method determines which sensor's vibrations best indicate tonal noise. This allows more accurate prediction and prevention compared to just using overall turbine vibration levels.

16. Method for Analyzing and Correcting Tonal Audibility of Wind Turbine Noise Using Operating and Shutdown State Data

CHINA ELECTRIC POWER RES INST CO LTD, CHINA ELECTRIC POWER RESEARCH INSTITUTE CO LTD, STATE GRID CORP CHINA, 2021

Determining the sound value of wind turbine noise to accurately assess the audibility of wind turbine noise compared to background noise. The method involves collecting operating state and shutdown state noise data, screening the shutdown state data to remove noise sources, analyzing the screened data to determine tonal audibility, and correcting the operating state tonal audibility based on the shutdown state results to get the true audibility. This considers background noise influence and provides a more accurate sound value measurement.

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17. Noise Sensor System for Monitoring Transmission Chain Acoustic Signals in Wind Turbine Nacelles

SPIC GUANGXI XINGAN WIND POWER CO LTD, 2020

Online noise monitoring system and method for the transmission chain of a wind turbine nacelle to diagnose and maintain fan noise issues. The system involves placing a unique noise sensor inside the nacelle above the gearbox to collect noise signals during operation. The signals are processed and uploaded to a data system for analysis. This allows real-time monitoring of nacelle transmission chain noise levels and changes under different operating conditions. It provides accurate and efficient detection of fan noise issues without complex measurements or sensors in the surrounding environment.

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18. Correlating Sensor Data with Community Noise Notifications for Tonal Noise Detection in Wind Turbines

VESTAS WIND SYSTEMS AS, 2020

Monitoring tonal noise emissions from wind turbines using sensor data and noise notifications received from nearby communities. The method involves correlating sensor data from wind turbine operating parameters with noise notifications received from nearby communities to determine if the turbine is generating tonal noise. If so, the turbine's operating parameters can be adjusted to reduce or mask the tonal noise.

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19. Cooperative Active Control System for Wind Turbine Speed and Blade Pitch Adjustment Based on Real-Time Noise and Wind Data Monitoring

UNIV YANGZHOU, YANGZHOU UNIVERSITY, 2019

Cooperative active control of wind turbine noise and power generation to reduce noise levels in residential areas without shutting down turbines. The method involves monitoring noise and wind data near homes using sensors. When noise exceeds standards, turbines near the homes adjust speed and blade pitch to lower noise without shutting down. This allows optimal power generation while meeting noise limits. It uses real-time monitoring and intelligent control instead of blanket noise reduction methods.

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20. Noise-Conscious Cooling Adjustment Method for Wind Turbine Component Management

VESTAS WIND SYSTEMS AS, 2019

Method for controlling wind turbine noise by optimizing component cooling to avoid exceeding noise limits when reducing turbine operation for noise reduction. The method involves determining the contribution of cooling device noise to overall turbine noise, based on inputs indicating the noise generated by the cooling devices. It then adjusts the cooling device operation to balance cooling needs with noise limits. This prevents overcooling that could cause issues due to lack of cooling, while avoiding excessive cooling noise.

21. Vibration Sensor-Based Wind Turbine Control Method for Tonal Noise Mitigation

22. Microphone-Equipped Nacelle System for Real-Time Acoustic Noise Data Correlation and Wind Turbine Parameter Adjustment

23. Wind Turbine Acoustic Noise Monitoring and Control System with Nacelle-Mounted Microphones and Real-Time Data Correlation

24. Active Noise Control System for Wind Turbine Blades with Sensor-Driven Blade Configuration Adjustments

25. System for Selective Noise Contribution Reduction in Wind Turbines Through Individual Turbine Control

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