Active Noise Control for Wind Turbines

Blow-molded containers for food packaging with selective barrier layers to improve shelf life. The containers have a two-part molding process. First, a top portion is injection molded from a single material. Then, a bottom portion with inner, outer, and intermediate sections is molded using a combination of the main material and a barrier material like an oxygen scavenger. The intermediate section with the barrier is positioned between the inner and outer sections. This allows a barrier layer to be concentrated and selectively positioned within the wall thickness. The bottom molding is done after the top to avoid trimming waste.

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Preserving plant parts like seeds, flowers, and leaves by placing them in containers with low oxygen environments to prevent discoloration and enhance germination. The low oxygen environments contain oxygen scavengers like enzymes or chemicals to remove oxygen that can cause pigment degradation. The containers may also have nitric oxide, stably maintained in deoxygenated water, to further promote germination. The oxygen scavengers can be in separate sachets or packets inside the containers. The containers can be made of polymer materials and the scavengers can be applied as films. This preserves plant parts like seeds without drying or freezing them.

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Oxygen removal module for fresh food storage in refrigerators that prevents food spoilage due to oxygen. The module has an electrolyte tank with an air pressure balance port connected to a water tank. The water tank supplements water to the electrolyte tank. This prevents water shortage in the electrolyte tank after long operation due to oxygen consumption. The water tank also has a feeding port with a plug. The electrolyte tank has an oxygen exhaust hole with a floating ball that blocks it when the tank tips over. A pressure release valve is at the top. This allows oxygen removal from the food storage space while preventing water loss issues.

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Plastic containers for food and beverages that have improved shelf life without compromising recyclability. The containers have selective placement of barrier materials like oxygen scavengers near the center to reduce the total amount needed. This allows using less of the expensive barrier materials while still providing the same or better shelf life. By concentrating the barrier materials in certain areas, less overall barrier material is needed compared to uniform distribution. This enables recycling the containers without yellowing issues that occur with high barrier concentrations. The containers are made by selectively injecting barrier materials into specific layers of the preform.

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The rise of industrial machinery and military training activities has significantly contributed to low-frequency noise pollution, which can penetrate traditional passive isolation methods pose serious health risks, including irreversible hearing damage. To address this challenge, study proposes a hybrid active control (HANC) system, integrating an adaptive (AANC) module based on the filtered-x least mean squares (FxLMS) algorithm audio-balance circuit (ABCC). AANC system actively generates anti-noise signals mitigate disturbances, while ABCC enhances voice clarity protects users from excessive impulse noise. MATLAB R2023b simulations hardware implementations validate system’s effectiveness, achieving reduction up 21.8 dB in controlled environments. Additionally, proposed feedback architecture ensures robust performance under dynamic conditions, improving stability response time. By both software-based filtering design, provides comprehensive mitigation solution with potential applications military, industrial, vehicular

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Reducing low frequency noise from wind turbine generators to mitigate tonality without using higher harmonic compensation techniques. The method involves controlling the electrical generator current by adding a compensation current to counteract a fractional harmonic current component that causes low frequency noise. The compensation current is calculated based on the generator's rotor position and the electrical phase angle of the fundamental generator current. This uses the rotor position as a reference to identify the correct fundamental period for the compensation calculation. It provides an accurate compensation current even with an estimated rotor position.

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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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Controlling noise emissions from individual blades of a wind turbine using a model-based control algorithm. The algorithm involves defining a wind turbine model that describes the intensity and direction of noise emissions from each blade as a function of azimuth angle. The model is used to determine control outputs for each blade to reduce noise. This allows targeted blade control to mitigate blade-specific noise sources, unlike conventional methods that aim to reduce overall turbine noise. The blade-by-blade control can potentially reduce noise levels compared to global controls.

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Reducing noise from wind turbine blades by using sensors and actuators on the blade surface to actively cancel out edge noise. The blade has sensors spaced non-uniformly along the edge to detect flow characteristics. An actuator near the sensor provides an anti-noise signal based on the sensor data. This cancels out flow-induced edge noise generated by the blade. By arranging the sensors and actuator with spacing that avoids aliasing, it allows separation of efficiently radiated noise vs inefficient noise.

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Reducing noise from wind turbines using an active noise cancellation system. The system has sensors on the blades and tower to measure blade tip turbulence and tower-side noise. An actuator on the blade emits an anti-noise signal based on the sensor outputs. This cancels out blade tip noise in the far field. By coordinating blade and tower sensors, the system can adaptively cancel noise as the blade position changes.

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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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Noise reduction system for wind turbine blades that combines passive serrations with active sensors and actuators to significantly reduce trailing edge noise. The system uses sensors near the jagged edges to detect turbulent flow conditions. An actuator is controlled based on the sensor output to generate an anti-noise signal that cancels out some of the blade noise. This active noise cancellation complements the passive serrations to provide improved overall noise reduction compared to just using the serrations alone.

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Active noise reduction for wind turbines that uses sensors and actuators on the blades and tower to cancel trailing edge noise. The system has unsteady pressure sensors on the blades to detect turbulent flow conditions. Noise sensors on the tower or nacelle measure blade noise. An adaptive filter controlled by a unit on the blades generates an anti-noise signal based on the sensor outputs. Actuators like speakers emit the anti-noise to counter blade noise. The filter adjusts based on blade orientation to optimize cancellation when the blade is downward.

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Method for masking tonal noise from wind turbines by emitting directional masking noise from a dedicated generator that points away from the turbine's rotor axis. This supplements natural noise masking and reduces annoyance from tonal turbine noise. The masking direction can be fixed or change with rotor yaw. The masking noise generator can also be rotated to align the masking direction with the changing natural noise direction. This allows more effective masking as the turbine rotates.

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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.

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Method to construct a high-quality reference signal for active noise control (ANC) systems in fan ducts using microphone arrays. The method aims to improve the quality of the reference signal in feedforward ANC systems by accounting for coherent and incoherent interferences in fan ducts. It involves using a microphone array in the duct to capture the fan noise and extracting the fan rotational noise component using beamforming. This extracted signal is used as the reference for the ANC. It also compensates for signal delays in the duct using quadratic interpolation. This improves the reference signal quality by reducing interference compared to using a single microphone.

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Active noise reduction system for wind turbines that uses sensors on the blades and tower to cancel blade noise. The system has actuators, pressure sensors, and noise sensors. The pressure sensors measure turbulent flow on the blades, and the noise sensors detect blade noise at the nacelle or tower. A control unit uses the sensor signals to generate anti-noise signals sent to the blade actuators. This cancels blade noise in the far field, reducing overall wind turbine noise. The sensors are arranged on the blades and tower to match blade alignment during rotation. The control unit adjusts the filter function based on blade orientation.

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Active noise cancellation system for wind turbines that reduces noise emissions using sensors and actuators on the blades and tower. The system has unsteady pressure sensors on the blades, a noise sensor on the tower/nacelle, and a control unit. The control unit uses the blade pressure sensor output to generate an anti-noise signal that is sent to an actuator on the blade. It also uses the tower noise sensor to adjust the anti-noise signal. This active cancellation aims to counteract the blade trailing edge noise that contributes to wind turbine noise. The control unit can adapt the cancellation based on blade orientation using sensors on the blade.

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Noise suppression system and method for wind turbines that reduces noise impact on surrounding areas without affecting power generation. The system involves adding components like positioning devices, environmental noise sensors, sound generators, and a controller to the turbine. It uses a database of noise-sensitive areas and geographic info around the turbine. The method involves: 1) Detecting external noise level, 2) Compensating for it by generating opposite sound, 3) Collecting the corrected noise, 4) Transmitting it to the controller, 5) Using the data to further optimize the counter-noise generation.

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