Wind Turbine Sound Pattern Analysis

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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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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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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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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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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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.

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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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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.

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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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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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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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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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Fast method for predicting wind turbine aerodynamic noise that allows accurately calculating wind turbine noise without the need for complex CFD simulations. The method involves dividing the wind turbine blade into sections, calculating the effective wind speed and angle of attack on each section, applying noise models to those sections, and then summing the section noise levels to get the overall turbine noise. This allows rapid prediction of wind turbine noise without the computational resources and time needed for full CFD simulations.

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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.

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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.

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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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