Wind turbine generator group noise reduction equipment and method based on sound wave interference
By using a noise reduction device for wind turbine clusters based on acoustic interference, noise signals are monitored and analyzed in real time, generating antiphase sound waves and emitting interference sound waves. This solves the complex noise processing problem caused by the superposition of noise from multiple wind turbines, achieving efficient noise reduction and intelligent equipment management, and adapting to complex environmental changes.
Patent Information
- Application Number
- CN202511193841.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-25
- Publication Date
- 2025-11-14
AI Technical Summary
Existing technologies cannot effectively handle the complex noise environment resulting from the superposition of noise from multiple wind turbines. Existing noise reduction technologies are insufficient to meet increasingly stringent noise control standards, and environmental factors interfere with the sound wave propagation path and interference effect.
A noise reduction device for wind turbine groups based on acoustic wave interference is adopted, including a noise monitoring module, a data processing and control module, an acoustic wave emission module and a power supply module. By monitoring, calibrating and analyzing noise signals in real time, it generates anti-phase acoustic waves with opposite phase and matching amplitude to the noise, and uses a loudspeaker array to emit interference acoustic waves to cancel the noise.
It achieves efficient noise reduction for complex noise, significantly reducing noise in residential areas by 10-20 decibels. It adapts to different operating conditions and environmental changes of multiple wind turbines in a wind farm, ensuring continuous and stable noise reduction effects, reducing manual maintenance costs, and improving equipment reliability and stability.
Smart Images
Figure CN120954375A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of wind power generation technology, specifically relating to a noise reduction device and method for wind turbine groups based on acoustic interference. Background Technology
[0002] With the increasing global demand for clean energy, wind power has been widely adopted. However, the noise generated by wind turbines has severely impacted the living environment of surrounding residential areas. Currently, noise reduction measures for wind turbines mainly include passive noise reduction methods such as optimizing blade design and using sound insulation materials, as well as active noise reduction methods such as installing silencers near the turbines. However, these methods have certain limitations: passive noise reduction methods have limited effectiveness and are difficult to meet increasingly stringent noise control standards; while existing active noise reduction methods are usually designed for single wind turbines and cannot effectively handle the complex noise environment of multiple turbines combined and propagating to residential areas. When multiple turbines operate simultaneously in a wind farm, the noise generated by each turbine overlaps during propagation, forming a mixed noise with a complex spectrum and high intensity. Existing noise reduction technologies are unable to effectively suppress this, resulting in the persistent problem of excessive noise in residential areas.
[0003] While existing noise reduction techniques based on acoustic wave interferometry theoretically possess noise reduction potential, they face numerous challenges in practical applications. The core principle of acoustic wave interferometry is that when two sound waves with similar frequencies, amplitudes, and opposite phases meet, their peaks and troughs cancel each other out, thus attenuating noise energy. However, current technologies cannot accurately monitor the complex and variable noise characteristics of wind turbine clusters, resulting in a mismatch between the generated anti-phase sound waves and the actual noise, leading to significant signal processing errors. Furthermore, environmental factors such as wind speed and direction severely interfere with the sound wave propagation path and interference effect, significantly reducing the noise reduction efficiency and failing to meet practical engineering requirements.
[0004] Therefore, it is necessary to develop a new noise reduction device and method for wind turbine groups based on acoustic interference. Summary of the Invention
[0005] The purpose of this invention is to provide a noise reduction device and method for wind turbine groups based on acoustic interference, which can effectively cancel noise propagating to residential areas.
[0006] In a first aspect, the wind turbine group noise reduction device based on acoustic interference according to the present invention comprises: Noise monitoring module: includes a calibration unit and multiple microphone array units connected to the calibration unit. The microphone array units are used to capture noise signals of different directions and frequencies. Each microphone array unit integrates a temperature sensor, a humidity sensor, and a barometric pressure sensor to acquire environmental parameters in real time. The calibration unit is used to calibrate the noise signals in real time according to the environmental parameters. Data processing and control module: connected to the noise monitoring module, used to receive noise signals and perform spectrum and time domain analysis on the noise signals to obtain frequency, amplitude and phase characteristics, and generate an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with the preset noise reduction target; Sound wave transmitting module: connected to the data processing and control module, the sound wave transmitting module includes multiple speaker array units, a speaker array driving circuit connected to each speaker array unit, and a power amplifier connected to the speaker array driving circuit, used to receive the anti-phase sound wave control signal and convert it into interference sound waves before transmitting it; Power supply module: Connected to the noise monitoring module, data processing and control module and sound wave emission module respectively, and used to supply power to the noise monitoring module, data processing and control module and sound wave emission module.
[0007] Optionally, the data processing and control module includes: The data receiving unit is used to receive noise signals; The data processing unit is used to perform spectrum and time domain analysis on the noise signal, obtain frequency, amplitude and phase characteristics, and generate an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with a preset noise reduction target. The operation and fault diagnosis unit is used to control the equipment, monitor the equipment's operating status, and perform fault diagnosis based on the operating status.
[0008] Optionally, the data processing and control module further includes: The error optimization unit is configured with a preset mapping relationship between noise signal characteristics and environmental factors and equipment operating status. Based on environmental parameters and equipment operating status, it analyzes and predicts the noise signal and dynamically adjusts the parameters of the anti-phase acoustic wave control signal, including at least one of phase, amplitude, and frequency. This reduces the inaccuracy of interference acoustic waves caused by signal processing errors, improves the generation accuracy of the anti-phase acoustic wave control signal, and ensures that the generated anti-phase acoustic wave is highly matched with the actual noise in terms of frequency, amplitude, and phase, thereby enhancing the noise reduction effect of acoustic wave interference.
[0009] Optionally, the data processing and control module further includes: The transmission parameter optimization unit acquires meteorological data, including wind speed, wind direction, temperature, and humidity. Based on this meteorological data, it predicts the propagation paths and characteristic changes of noise and interfering sound waves in the current environment using a preset fluid dynamics simulation model. Based on the prediction results, it adjusts the transmission parameters of the sound wave transmission module in advance. These transmission parameters include at least one of sound wave frequency, intensity distribution, and transmission angle. Through meteorological data and the fluid dynamics simulation model, the propagation patterns of noise and interfering sound waves in complex environments can be understood in advance, allowing for timely adjustment of transmission parameters to ensure that the interfering sound waves accurately encounter the noise and produce effective interference.
[0010] Optionally, the loudspeaker array unit includes a plurality of loudspeakers arranged in an array, an angle adjustment motor connected to the loudspeakers, and an angle adjustment motor drive circuit connected to the angle adjustment motor, for dynamically adjusting the emission angle of the loudspeakers according to the emission parameters; The acoustic wave emitting module is also surrounded by an acoustic reflector unit, which includes an acoustic reflector, an acoustic reflector adjustment mechanism connected to the acoustic reflector, and an acoustic reflector control circuit connected to the acoustic reflector adjustment mechanism.
[0011] Secondly, the wind turbine group noise reduction method based on acoustic interference described in this invention employs the wind turbine group noise reduction equipment based on acoustic interference as described in this invention, and the method includes the following steps: It can capture noise signals of different directions and frequencies in real time, and acquire environmental parameters in real time. The captured noise signal is calibrated in real time based on the environmental parameters acquired in real time. The system receives the calibrated noise signal, performs spectral and time-domain analysis on the noise signal to obtain the frequency, amplitude, and phase characteristics of the noise signal, and generates an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with a preset noise reduction target. The anti-phase acoustic wave control signal is converted into an interference acoustic wave and emitted. The noise-reduced signal is acquired, and it is determined whether the noise reduction effect has reached the preset target. If not, the anti-phase sound wave control signal and the transmission parameters of the sound wave transmission module are dynamically adjusted in combination with environmental information.
[0012] Optionally, it also includes: Construct a mapping relationship between noise signal characteristics and environmental factors and equipment operating status; The system acquires real-time environmental parameters and equipment operating status. By analyzing and predicting the noise signal through the mapping relationship between the noise signal characteristics and environmental factors and equipment operating status, it dynamically adjusts the parameters of the anti-phase acoustic wave control signal, including at least one of phase, amplitude, and frequency. This reduces the inaccuracy of interference acoustic waves caused by signal processing errors, improves the generation accuracy of the anti-phase acoustic wave control signal, and ensures that the generated anti-phase acoustic wave is highly matched with the actual noise in frequency, amplitude, and phase, thereby enhancing the noise reduction effect of acoustic wave interference.
[0013] Optionally, the mapping relationship between the noise signal characteristics and environmental factors and equipment operating status is established in the following way: Collect a large amount of historical noise data, as well as environmental parameters and equipment operating status data at the corresponding time. A machine learning algorithm is used, with historical noise data as input and corresponding environmental parameters and equipment operating status data as labels, to train the machine learning model; During the training process, the parameters of the machine learning model are continuously adjusted so that it can accurately learn the relationship between noise signal characteristics and environmental factors and equipment operating status. The trained machine learning model is validated and optimized until a mapping relationship between noise signal characteristics and environmental factors and equipment operating status that meets preset requirements is obtained. Using historical noise data and deep learning algorithms, the nonlinear relationship between complex noise signals and environmental factors can be learned.
[0014] Optionally, it also includes: Meteorological data, including wind speed, wind direction, temperature, and humidity, is acquired. Based on this data, a pre-defined fluid dynamics simulation model is used to predict the propagation paths and characteristic changes of noise and interfering sound waves in the current environment. The transmission parameters of the sound wave transmission module are determined in advance based on the prediction results. These transmission parameters include at least one of sound wave frequency, intensity distribution, and transmission angle. Considering that environmental factors can alter the propagation characteristics of sound waves and affect the interference effect, the propagation patterns of noise and interfering sound waves in complex environments can be understood in advance through meteorological data and the fluid dynamics simulation model. This allows for timely adjustment of transmission parameters, ensuring that the interfering sound waves accurately encounter the noise and produce effective interference.
[0015] Optionally, it also includes: The system controls and monitors the equipment's operating status, and performs fault diagnosis based on this status. If a fault is detected, an alarm is issued and the fault information is recorded. This enables intelligent operation and self-management of the equipment, reducing manual maintenance costs and improving equipment reliability and stability.
[0016] The present invention has the following unexpected technical effects: 1. This invention comprehensively monitors and precisely analyzes the superimposed noise from multiple wind turbine units in a wind farm. Utilizing the principle of acoustic interference, it generates anti-phase sound waves that match the complex noise characteristics, achieving highly efficient noise reduction. This effectively reduces noise in residential areas by 10-20 decibels, significantly improving the living environment for residents. Compared to traditional noise reduction technologies, this invention deeply understands and applies the principle of acoustic interference, optimizing the entire process from noise acquisition and signal processing to sound wave emission to ensure maximum interference effect.
[0017] 2. This invention can monitor changes in noise signals in real time, automatically adjust the parameters of the anti-phase sound wave, adapt to changes in noise characteristics under different operating conditions of multiple wind turbines in a wind farm, and mitigate the impact of environmental factors such as wind speed and direction on noise propagation, ensuring a continuous and stable noise reduction effect. By introducing environmental parameter compensation, deep learning algorithms, and linkage with meteorological data, signal processing errors are effectively reduced; through the sound wave emission module and fluid dynamics simulation prediction, the interference of environmental factors on the propagation of interference waves is reduced. In the complex and ever-changing wind farm environment, this invention can dynamically adapt to environmental changes and maintain excellent sound wave interference noise reduction performance at all times.
[0018] 3. The data processing and control module of this invention integrates advanced noise reduction control algorithms, fault diagnosis functions, deep learning models, and fluid dynamics simulation modules to realize intelligent operation and self-management of equipment, reduce manual maintenance costs, and improve equipment reliability and stability. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the wind turbine group noise reduction device based on acoustic interference described in the embodiments of this application; Figure 2 This is a flowchart of the wind turbine group noise reduction method based on acoustic interference described in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the working principle of the wind turbine group noise reduction device based on acoustic interference described in the embodiments of this application. Detailed Implementation
[0020] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.
[0021] like Figure 1As shown in the embodiments of this application, a wind turbine group cooling device based on acoustic interference includes a noise monitoring module, a data processing and control module, an acoustic emission module, and a power supply module.
[0022] The noise monitoring module includes a calibration unit and multiple microphone array units connected to the calibration unit. The microphone array units are used to capture noise signals from different directions and frequencies. Each microphone array unit integrates a temperature sensor, a humidity sensor, and a barometric pressure sensor to acquire environmental parameters in real time. The calibration unit is used to calibrate the noise signals in real time based on the environmental parameters.
[0023] The data processing and control module is connected to the noise monitoring module to receive noise signals and perform spectrum and time domain analysis on the noise signals to obtain frequency, amplitude and phase characteristics. It also generates an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with the preset noise reduction target.
[0024] The acoustic wave transmitting module is connected to the data processing and control module. The acoustic wave transmitting module includes multiple speaker array units, a speaker array driving circuit connected to each speaker array unit, and a power amplifier connected to the speaker array driving circuit. It is used to receive the anti-phase acoustic wave control signal, convert it into interference acoustic waves, and then transmit it.
[0025] The power supply module is connected to the noise monitoring module, the data processing and control module, and the sound wave emission module, respectively, and is used to supply power to the noise monitoring module, the data processing and control module, and the sound wave emission module.
[0026] The following is a detailed explanation of each module: 1. Noise monitoring module: The microphone array unit comprises multiple high-precision microphones arranged in an array (i.e., employing a redundant microphone design), capable of comprehensively capturing noise signals from different directions and frequencies, thus improving the accuracy and completeness of noise monitoring. In use, the microphone array unit is installed around residential areas and at key locations in wind farms. Each microphone array unit is equipped with a temperature sensor, humidity sensor, and barometric pressure sensor to monitor environmental parameters in real time and synchronously transmit these parameters to the data processing and control module. By establishing a compensation model between environmental parameters and microphone sensitivity, the collected noise signals are calibrated in real time, reducing signal errors caused by environmental factors.
[0027] Because multiple microphones are deployed at the same monitoring point, and signal comparison and weighted averaging algorithms are used, the impact of a single microphone failure or anomaly on signal acquisition can be reduced. Based on the principle of acoustic wave interference, accurately acquired noise signals are a prerequisite for generating effective interference sound waves (i.e., anti-phase sound waves). Error optimization and improvement ensure that the acquired noise signals can truly reflect the noise characteristics of the wind turbine group, laying the foundation for subsequent interference noise reduction.
[0028] 2. Data Processing and Control Module: A high-performance digital signal processor (DSP) and noise reduction control algorithm are employed. The data processing and control module receives noise signals transmitted from the noise monitoring module and performs real-time analysis, including spectrum analysis and time-domain analysis, to obtain characteristic parameters such as the frequency, amplitude, and phase of the noise. Based on the analysis results and in conjunction with the preset noise reduction target, the noise reduction control algorithm calculates and generates an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the superimposed noise, and transmits this anti-phase acoustic wave control signal to the acoustic wave transmitting module. Simultaneously, the data processing and control module is also responsible for monitoring and controlling the overall operating status of the equipment, enabling intelligent operation and fault diagnosis.
[0029] First, a mapping relationship is established between noise signal characteristics and environmental factors and equipment operating status. Specifically, this involves collecting a large amount of historical noise data and corresponding environmental parameters and equipment operating status data. A machine learning algorithm (such as a Convolutional Neural Network - CNN) is used, with historical noise data as input and corresponding environmental parameters and equipment operating status data as labels, to train the machine learning model. During training, the parameters of the machine learning model are continuously adjusted to ensure it accurately learns the relationship between noise signal characteristics and environmental factors and equipment operating status. The trained machine learning model is then validated and optimized until a mapping relationship between noise signal characteristics and environmental factors and equipment operating status that meets preset requirements is obtained. After establishing this mapping relationship, real-time environmental parameters and equipment operating status are acquired during real-time processing. The noise signal is analyzed and predicted using this mapping relationship, and the parameters of the anti-phase acoustic wave control signal are dynamically adjusted, including at least one of phase, amplitude, and frequency. This reduces the inaccuracy of interference acoustic waves caused by signal processing errors. Deep learning algorithms can learn the nonlinear relationship between complex noise signals and environmental factors, improve the generation accuracy of antiphase sound wave control signals, and ensure that the generated antiphase sound waves are highly matched with the actual noise in terms of frequency, amplitude, and phase, thereby enhancing the noise reduction effect of sound wave interference.
[0030] The data processing and control module has a built-in meteorological data interface to acquire real-time meteorological data from surrounding weather stations, including wind speed, wind direction, temperature, and humidity. Combining this meteorological data with fluid dynamics simulation models (such as Computational Fluid Dynamics - CFD), it predicts the propagation paths and characteristic changes of noise and interfering sound waves in the current environment. Based on the prediction results, the transmission parameters of the sound wave transmission module are adjusted in advance, such as changing the transmission angle of the loudspeaker array and adjusting the sound wave frequency and intensity distribution, to adapt to the impact of environmental changes on the propagation of interfering sound waves. Considering that environmental factors can alter the propagation characteristics of sound waves and affect the interference effect, by using meteorological data and fluid dynamics simulation models, the propagation laws of noise and interfering sound waves in complex environments can be understood in advance, allowing for timely adjustments to transmission parameters to ensure that the interfering sound waves accurately encounter the noise and produce effective interference.
[0031] To achieve the above functions, the data processing and control module includes a data receiving unit, a data processing unit, an operation and fault diagnosis unit, an error optimization unit, and a transmission parameter optimization unit. The data receiving unit receives noise signals. The data processing unit performs spectral and time-domain analysis on the noise signals to obtain frequency, amplitude, and phase characteristics. It then generates an anti-phase acoustic wave control signal with opposite phase and matched amplitude to the noise signal, based on a preset noise reduction target. This data processing unit is connected to the data receiving unit. The operation and fault diagnosis unit controls the equipment, monitors its operating status, and performs fault diagnosis based on the operating status. This unit is connected to the data processing unit. The error optimization unit is configured with a preset mapping relationship between noise signal characteristics and environmental factors and equipment operating status. Based on environmental parameters and equipment operating status, it analyzes and predicts the noise signal, dynamically adjusting the parameters of the anti-phase acoustic wave control signal, including at least one of phase, amplitude, and frequency. This error optimization unit is connected to the data processing unit. The transmission parameter optimization unit acquires meteorological data, including wind speed, wind direction, temperature, and humidity. Based on this meteorological data, it uses a pre-set fluid dynamics simulation model to predict the propagation paths and characteristic changes of noise and interference sound waves in the current environment. Based on the prediction results, it adjusts the transmission parameters of the sound wave transmission module in advance. The transmission parameters include at least one of sound wave frequency, intensity distribution, and transmission angle. The transmission parameter optimization unit is connected to the data processing unit.
[0032] 3. Acoustic wave emitting module: The loudspeaker array unit includes multiple loudspeakers arranged in an array, an angle adjustment motor connected to the loudspeakers, and an angle adjustment motor drive circuit connected to the angle adjustment motor. The angle adjustment motor drive circuit is connected to the power supply module and is used to dynamically adjust the emission angle of the loudspeakers according to the emission parameters. An acoustic reflector unit is also arranged around the sound wave emission module. The acoustic reflector unit includes an acoustic reflector, an acoustic reflector adjustment mechanism connected to the acoustic reflector, and an acoustic reflector control circuit connected to the acoustic reflector adjustment mechanism. The acoustic reflector control circuit is connected to the power supply module.
[0033] In use, multiple speaker array units are installed in suitable locations around the residential area, with optimized orientation and layout. The sound wave transmitting module receives the anti-phase sound wave control signal transmitted from the data processing and control module, converts it into interference sound wave (i.e., anti-reverse sound wave) signals, and transmits them. During transmission, the speaker array units can adjust the transmission angle and intensity of the interference sound waves according to different noise frequencies and directions, ensuring that the interference sound waves accurately interfere with the superimposed noise propagating into the residential area, achieving effective noise reduction.
[0034] Environmental Adaptability Improvements: The loudspeaker array unit features a shell made of windproof, waterproof, and corrosion-resistant materials, reducing the impact of harsh environments on equipment performance. Each loudspeaker is equipped with an angle adjustment motor, which adjusts the emission angle in real time according to instructions from the data processing and control module to compensate for sound wave propagation path deviations caused by changes in wind speed and direction. Simultaneously, acoustic reflectors are installed around the loudspeaker array unit. By optimizing the reflector angle, interference from building reflections on the propagation of interfering sound waves is reduced, enhancing the superposition effect of interfering sound waves in the target area. Through these designs, it is ensured that even in complex environments, the anti-phase sound waves emitted by the loudspeaker array unit propagate along the expected path, accurately interfering with noise and fully utilizing the noise reduction effectiveness of sound wave interference.
[0035] 4. Power supply module: Provides a stable power supply for the noise monitoring module, data processing and control module and acoustic wave emission module.
[0036] The power module includes a DC / DC power conversion unit, a battery pack, a solar panel, and an AC power input. The battery pack, solar panel, and AC power input are all connected to the DC / DC power conversion unit.
[0037] The power module employs a combined solar and mains power supply method. On sunny days, it prioritizes solar panel power generation to supply the equipment, with excess power stored in batteries. On cloudy days or at night, it automatically switches to mains power to ensure continuous and stable operation, reducing operating costs and dependence on traditional energy sources. This combined solar and mains power supply method reduces the consumption of traditional energy sources during equipment operation, lowers operating costs, and aligns with the concept of green and environmentally friendly development.
[0038] like Figure 2 and Figure 3 As shown in the embodiments of this application, a wind turbine group noise reduction method based on acoustic interference is provided, employing the wind turbine group noise reduction device based on acoustic interference as described in the embodiments of this application. The method includes the following steps: It can capture noise signals of different directions and frequencies in real time, and acquire environmental parameters in real time.
[0039] The captured noise signal is calibrated in real time based on the environmental parameters acquired in real time.
[0040] The system receives the calibrated noise signal, performs spectral and time-domain analysis on the noise signal to obtain its frequency, amplitude, and phase characteristics, and generates an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with the preset noise reduction target. The anti-phase acoustic wave control signal is converted into an interference acoustic wave and emitted.
[0041] The noise-reduced signal is acquired, and it is determined whether the noise reduction effect has reached the preset target. If not, the anti-phase sound wave control signal and the transmission parameters of the sound wave transmission module are dynamically adjusted in combination with environmental information.
[0042] In one possible embodiment, noise signals of different directions and frequencies are captured in real time, and environmental parameters are acquired in real time, specifically as follows: The noise monitoring module uses a microphone array unit to collect real-time superimposed noise signals propagating from multiple wind turbines into residential areas during operation (i.e., collecting noise signals from the noise superposition zone), and converts the analog signals into digital signals for transmission to the data processing and control module. Simultaneously, environmental sensors equipped with the microphone array unit collect environmental parameters such as temperature, humidity, and air pressure, and transmit these to the data processing and control module for real-time noise signal calibration.
[0043] In one possible embodiment, after receiving the calibrated noise signal, spectral and time-domain analysis are performed on the noise signal to obtain its frequency, amplitude, and phase characteristics. Then, a pre-defined noise reduction target is used to generate an anti-phase acoustic wave control signal that is opposite in phase and matches the amplitude of the noise signal. Specifically: The data processing and control module performs spectral analysis and time-domain analysis on the received noise signal. Combining environmental parameters and meteorological data, it uses deep learning algorithms and fluid dynamics simulation models to obtain characteristic parameters such as the noise's frequency, amplitude, and phase, and predicts the propagation characteristics of the noise and interfering sound waves in the current environment. Then, based on the preset noise reduction target and algorithm, and using the principle of sound wave interference, it calculates and generates an anti-phase sound wave control signal with opposite phase and matching amplitude to the superimposed noise, and dynamically adjusts the control signal parameters according to environmental changes.
[0044] In one possible embodiment, the anti-phase acoustic wave control signal is converted into an interference acoustic wave and emitted, specifically as follows: The acoustic wave transmitting module receives the anti-phase acoustic wave control signal transmitted from the data processing and control module, converts it into an acoustic wave signal, and transmits it according to the optimized transmission angle and intensity. During transmission, the speaker array adjusts the transmission parameters in real time according to the control signal to ensure that the anti-phase acoustic wave can accurately interfere with the superimposed noise propagating into the residential area, canceling the noise energy and reducing the noise level in the residential area.
[0045] In one possible embodiment, the noise-reduced signal is acquired, and it is determined whether the noise reduction effect has reached a preset target. If not, the anti-phase acoustic wave control signal and the transmission parameters of the acoustic wave transmission module are dynamically adjusted based on environmental information. Specifically: The noise monitoring module continuously monitors the noise signal after noise reduction and feeds it back to the data processing and control module. The data processing and control module evaluates the noise reduction effect based on the feedback signal. If the noise reduction effect does not meet the preset target, it automatically adjusts the anti-phase sound wave control signal based on environmental changes to further optimize the emission parameters of the sound wave emission module until the noise level in the residential area meets the noise reduction requirements.
[0046] In one possible embodiment, a noise reduction method for wind turbine clusters based on acoustic interference further includes: controlling the equipment, monitoring the operating status of the equipment, and performing fault diagnosis based on the operating status of the equipment; if a fault is detected, issuing an alarm and recording the fault information.
[0047] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.
Claims
1. A noise reduction device for wind turbine clusters based on acoustic wave interference, characterized in that, Include: Noise monitoring module: includes a calibration unit and multiple microphone array units connected to the calibration unit. The microphone array units are used to capture noise signals of different directions and frequencies. Each microphone array unit integrates a temperature sensor, a humidity sensor, and a barometric pressure sensor to acquire environmental parameters in real time. The calibration unit is used to calibrate the noise signals in real time according to the environmental parameters. Data processing and control module: connected to the noise monitoring module, used to receive noise signals and perform spectrum and time domain analysis on the noise signals to obtain frequency, amplitude and phase characteristics, and generate an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with the preset noise reduction target; Sound wave transmitting module: connected to the data processing and control module, the sound wave transmitting module includes multiple speaker array units, a speaker array driving circuit connected to each speaker array unit, and a power amplifier connected to the speaker array driving circuit, used to receive the anti-phase sound wave control signal and convert it into interference sound waves before transmitting it; Power supply module: Connected to the noise monitoring module, data processing and control module and sound wave emission module respectively, and used to supply power to the noise monitoring module, data processing and control module and sound wave emission module.
2. The noise reduction device for wind turbine clusters based on acoustic interference according to claim 1, characterized in that, The data processing and control module: The data receiving unit is used to receive noise signals; The data processing unit is used to perform spectrum and time domain analysis on the noise signal, obtain frequency, amplitude and phase characteristics, and generate an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with a preset noise reduction target. The operation and fault diagnosis unit is used to control the equipment, monitor the equipment's operating status, and perform fault diagnosis based on the operating status.
3. The noise reduction device for wind turbine clusters based on acoustic interference according to claim 2, characterized in that, The data processing and control module also includes: The error optimization unit is configured with a preset mapping relationship between noise signal characteristics and environmental factors and equipment operating status. Based on environmental parameters and equipment operating status, it analyzes and predicts noise signals and dynamically adjusts the parameters of the anti-phase acoustic wave control signal, including at least one of phase, amplitude and frequency.
4. The noise reduction device for wind turbine groups based on acoustic interference according to claim 2, characterized in that, The data processing and control module also includes: The transmission parameter optimization unit is used to acquire meteorological data, including wind speed, wind direction, temperature and humidity. Based on the meteorological data, it uses a preset fluid dynamics simulation model to predict the propagation path and characteristic changes of noise and interference sound waves in the current environment, and adjusts the transmission parameters of the sound wave transmission module in advance according to the prediction results. The transmission parameters include at least one of sound wave frequency, intensity distribution and transmission angle.
5. The wind turbine group noise reduction device based on acoustic interference according to claim 4, characterized in that, The loudspeaker array unit includes multiple loudspeakers arranged in an array, an angle adjustment motor connected to the loudspeakers, and an angle adjustment motor drive circuit connected to the angle adjustment motor, used to dynamically adjust the emission angle of the loudspeakers according to the emission parameters. The acoustic wave emitting module is also surrounded by an acoustic reflector unit, which includes an acoustic reflector, an acoustic reflector adjustment mechanism connected to the acoustic reflector, and an acoustic reflector control circuit connected to the acoustic reflector adjustment mechanism.
6. A noise reduction method for wind turbine clusters based on acoustic wave interference, characterized in that, The method of using the wind turbine group noise reduction equipment based on acoustic wave interference as described in any one of claims 1 to 5 includes the following steps: It can capture noise signals of different directions and frequencies in real time, and acquire environmental parameters in real time. The captured noise signal is calibrated in real time based on the environmental parameters acquired in real time. The system receives the calibrated noise signal, performs spectral and time-domain analysis on the noise signal to obtain the frequency, amplitude, and phase characteristics of the noise signal, and generates an anti-phase acoustic wave control signal with opposite phase and matching amplitude to the noise signal in combination with a preset noise reduction target. The anti-phase acoustic wave control signal is converted into an interference acoustic wave and emitted. The noise-reduced signal is acquired, and it is determined whether the noise reduction effect has reached the preset target. If not, the anti-phase sound wave control signal and the transmission parameters of the sound wave transmission module are dynamically adjusted in combination with environmental information.
7. The noise reduction method for wind turbine clusters based on acoustic interference according to claim 6, characterized in that, Also includes: Construct a mapping relationship between noise signal characteristics and environmental factors and equipment operating status; The system acquires real-time environmental parameters and equipment operating status, analyzes and predicts noise signals by mapping the noise signal characteristics with environmental factors and equipment operating status, and dynamically adjusts the parameters of the anti-phase acoustic wave control signal, including at least one of phase, amplitude, and frequency, to improve the matching degree between the interference acoustic wave and the noise signal.
8. The noise reduction method for wind turbine clusters based on acoustic interference according to claim 7, characterized in that, The mapping relationship between the noise signal characteristics and environmental factors and equipment operating status is established in the following way: Collect a large amount of historical noise data, as well as environmental parameters and equipment operating status data at the corresponding time. A machine learning algorithm is used, with historical noise data as input and corresponding environmental parameters and equipment operating status data as labels, to train the machine learning model; During the training process, the parameters of the machine learning model are continuously adjusted so that it can accurately learn the relationship between noise signal characteristics and environmental factors and equipment operating status. The trained machine learning model is validated and optimized until a mapping relationship between noise signal characteristics and environmental factors and equipment operating status that meets the preset requirements is obtained.
9. The noise reduction method for wind turbine clusters based on acoustic interference according to claim 6, characterized in that, Also includes: Meteorological data, including wind speed, wind direction, temperature, and humidity, is acquired. Based on the meteorological data, a preset fluid dynamics simulation model is used to predict the propagation path and characteristic changes of noise and interference sound waves in the current environment. The transmission parameters of the sound wave transmission module are determined in advance according to the prediction results. The transmission parameters include at least one of sound wave frequency, intensity distribution, and transmission angle.
10. The method for noise reduction of wind turbine clusters based on acoustic interference according to claim 6, characterized in that, Also includes: Control the equipment, monitor its operating status, and diagnose faults based on the equipment's operating status; If a fault is detected, an alarm will be issued and the fault information will be recorded.