Wind Turbine Noise Prediction for Effective Reduction

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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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.

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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.

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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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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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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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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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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.

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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.

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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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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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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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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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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.

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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.

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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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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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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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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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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.

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