Wind farm noise prediction method, device and system

Abstract

Provided are a method, device and system for predicting noise in a wind farm. The method comprises: collecting the wind speed at the machine site of at least one wind turbine generator set in the wind farm that affects the noise at a noise detection point in real time; calculating the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed according to a noise sound power level database and a machine set database; calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed according to a wind farm noise propagation database; and calculating the total noise sound pressure level at the noise detection point using the noise sound power level and noise propagation loss value of each wind turbine generator set.

Description

[0001] This application is a divisional application of the invention patent application with the application date of October 31, 2018, application number "201811290818.6", and invention name "Wind Farm Noise Prediction Method, Device and System". Technical Field

[0002] The present invention relates to the technical field of wind power generation, and more particularly to a method, a device and a system for predicting noise of a wind farm. Background Art

[0003] When wind turbines are running at high speed to generate electricity, the operation of wind turbine blades, generators and other components will inevitably bring noise. Now, with the rapid development of wind power generation, the number of wind farms is increasing, and the locations of wind farms are getting closer and closer to people's daily life and work areas. Therefore, wind farm noise has caused a noise pollution problem that cannot be ignored, and has brought serious negative impacts on the surrounding environment, especially causing people's troubles or affecting people's health.

[0004] At present, there are three main methods for predicting wind farm noise: (1) predicting the sound power level of wind turbines based on the blade aeroacoustic theoretical function; (2) using simulation software to build a wind turbine sound source model and propagation model to simulate and predict the noise pressure level at the wind farm noise monitoring point; and (3) directly measuring the sound power level of the wind turbines and the sound pressure level at the wind farm noise detection point on site. However, method (1) uses more ideal conditions and simplified models, resulting in a large difference between the predicted noise results and the measured noise results; method (2) requires a large amount of simulation data and has a slow simulation speed, and cannot provide real-time prediction when the wind turbine is actually running; and method (3) takes a long time to measure the data in the early stage and has high labor costs.

[0005] In addition, regarding wind farm noise control, current technologies are mainly aimed at reducing the noise power level of a single wind turbine generator set. For example, the speed reduction control method, the pitch angle reduction method, the blade structure modification and the serrated trailing edge noise reduction method are used to achieve single-machine noise power level control. However, the existing wind farm noise control methods require an optimization plan to be proposed at the wind farm level to calculate the noise reduction command value that each wind turbine needs to execute in order to put the entire farm in the optimal operating state. It can be seen that there are currently few technologies that can achieve wind farm-level noise control, and the loss of power generation in the entire wind farm due to the need for noise control has not been considered. Summary of the invention

[0006] Exemplary embodiments of the present invention provide a method, device and system for predicting noise in a wind farm, which at least solve the above technical problems and other technical problems not mentioned above, and provide the following beneficial effects.

[0007] An aspect of the present invention is to provide a method for predicting wind farm noise, which may include: real-time collection of the wind speed at the machine site of at least one wind turbine generator set in the wind farm that affects the noise at a noise detection point; calculating the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed based on a noise sound power level database and a unit database; calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed based on a wind farm noise propagation database; and calculating the total noise sound pressure level at the noise detection point using the noise sound power level and noise propagation loss value of each wind turbine generator set.

[0008] A noise sound power level database and a unit database can be constructed based on the noise sound power level data and output power data of wind turbines of the same type as each of the wind turbines at different wind speeds and different rotational speeds, wherein the noise sound power level database includes wind speed, rotational speed and noise sound power level data, and the unit database includes a first database including wind speed, maximum rotational speed and minimum rotational speed data and a second database including wind speed, rotational speed and output power data.

[0009] A wind farm noise propagation database can be constructed based on the following steps: establish a wind farm model; use the established wind farm model to simulate the noise propagation process of the wind farm; and construct a wind farm noise propagation database based on the simulation results, wherein the wind farm noise propagation database includes distance, angle and noise propagation loss value data.

[0010] The step of calculating the noise sound power level of each wind turbine generator set may include: calculating the maximum speed of each wind turbine generator set at the corresponding wind speed by a linear interpolation method based on the first database and the collected wind speed; calculating the noise sound power level of each wind turbine generator set at the corresponding wind speed by a linear interpolation method based on the calculated maximum speed and the noise sound power level database.

[0011] The step of calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed may include: obtaining the position information of each wind turbine generator set and the noise detection point; calculating the straight-line distance between each wind turbine generator set and the noise detection point according to the obtained position information; calculating the angle between each wind turbine generator set and the noise detection point according to the obtained position information and the wind direction at the machine site of each wind turbine generator set collected in real time; and calculating the noise propagation loss value of each wind turbine generator set at the corresponding wind speed by linear interpolation method according to the wind farm noise propagation database and the calculated straight-line distance and angle.

[0012] The step of calculating the total noise sound pressure level at the noise detection point may include: using the noise sound power level and noise propagation loss value of each wind turbine generator set to respectively calculate the noise sound pressure level of each wind turbine generator set at the noise detection point; and superimposing the calculated noise pressure levels of each wind turbine generator set at the noise detection point to calculate the total noise sound pressure level at the noise detection point.

[0013] The method may further include controlling the output power of each wind turbine generator set according to the calculated total noise pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise pressure level does not exceed the noise threshold.

[0014] The step of controlling the output power of each wind turbine generator set according to the calculated total noise sound pressure level at the noise detection point may include comparing the calculated total noise sound pressure level with the noise threshold of the noise detection point, wherein when the total noise sound pressure level is greater than the noise threshold, calculating the output power of each wind turbine generator set at the collected wind speed based on the unit database, and determining the power control command value of each wind turbine generator set when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum based on the calculated output power of each wind turbine generator set by using an optimization algorithm, and using the determined power control command value of each wind turbine generator set to control the operation of the corresponding wind turbine generator set.

[0015] Another aspect of the present invention is to provide a wind farm noise prediction device, which may include: a data acquisition module, used to collect in real time the wind speed at the machine site of at least one wind turbine generator set in the wind farm that affects the noise at the noise detection point; and a data calculation module, used to: calculate the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed according to the noise sound power level database and the machine set database; calculate the noise propagation loss value of each wind turbine generator set at the collected wind speed according to the wind farm noise propagation database; use the noise sound power level and noise propagation loss value of each wind turbine generator set to calculate the total noise sound pressure level at the noise detection point.

[0016] The device may also include a database construction module for constructing a noise sound power level database and a unit database based on the noise sound power level data and output power data of wind turbines of the same type as each wind turbine at different wind speeds and different rotational speeds, wherein the noise sound power level database includes wind speed, rotational speed and noise sound power level data, and the unit database includes a first database including wind speed, maximum rotational speed and minimum rotational speed data and a second database including wind speed, rotational speed and output power data.

[0017] The database construction module can also construct a wind farm noise propagation database according to the result of simulating the noise propagation process of the wind farm, wherein the wind farm noise propagation database includes distance, angle and noise propagation loss value data.

[0018] The data calculation module can also calculate the maximum speed of each wind turbine generator set at the corresponding wind speed through a linear interpolation method based on the first database and the collected wind speed; and calculate the noise sound power level of each wind turbine generator set at the corresponding wind speed through a linear interpolation method based on the calculated maximum speed and noise sound power level database.

[0019] The data acquisition module can also obtain the position information of each wind turbine generator set and the noise detection point and collect the wind direction at the machine site of each wind turbine generator set in real time, and the data calculation module calculates the straight-line distance between each wind turbine generator set and the noise detection point according to the acquired position information, calculates the angle between each wind turbine generator set and the noise detection point according to the acquired position information and the collected direction, and calculates the noise propagation loss value of each wind turbine generator set at the corresponding wind speed through linear interpolation method according to the wind farm noise propagation database and the calculated straight-line distance and angle.

[0020] The data calculation module can use the noise sound power level and noise propagation loss value of each wind turbine to respectively calculate the noise sound pressure level of each wind turbine at the noise detection point; and superimpose the calculated noise pressure levels of each wind turbine at the noise detection point to calculate the total noise sound pressure level at the noise detection point.

[0021] The device may also include a control module for controlling the output power of each wind turbine generator set according to the calculated total noise pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise pressure level does not exceed the noise threshold.

[0022] The control module can also be used to compare the calculated total noise sound pressure level with the noise threshold of the noise detection point, wherein when the total noise sound pressure level is greater than the noise threshold, the output power of each wind turbine generator set at the collected wind speed is calculated based on the unit database, and based on the calculated output power of each wind turbine generator set, a power control command value of each wind turbine generator set is determined by using an optimization algorithm when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum, and the determined power control command value of each wind turbine generator set is used to control the operation of the corresponding wind turbine generator set.

[0023] Another aspect of the present invention is to provide a wind farm noise prediction system, which may include: a memory for storing the wind speed, noise sound power database and unit database at the machine site of at least one wind turbine generator set in the wind farm that affects the noise at the noise detection point, which are collected in real time; and a wind farm controller, which is configured to calculate the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed according to the noise sound power level database and the unit database; calculate the noise propagation loss value of each wind turbine generator set at the collected wind speed according to the wind farm noise propagation database; and use the noise sound power level and noise propagation loss value of each wind turbine generator set to calculate the total noise sound pressure level at the noise detection point, wherein the noise sound power level database and the unit database are constructed based on the noise sound power level data and output power data of wind turbine generator sets of the same type as each wind turbine generator set at different wind speeds and different rotation speeds.

[0024] The noise sound power level database may include wind speed, rotation speed and noise sound power level data, and the unit database includes a first database including wind speed, maximum rotation speed and minimum rotation speed data and a second database including wind speed, rotation speed and output power data.

[0025] The wind farm noise propagation database is constructed based on the results of simulating the noise propagation process of the wind farm, wherein the wind farm noise propagation database includes distance, angle and noise propagation loss value data.

[0026] The wind farm controller can also be configured to calculate the maximum speed of each wind turbine generator set at the corresponding wind speed by a linear interpolation method based on the first database and the collected wind speed; and calculate the noise sound power level of each wind turbine generator set at the corresponding wind speed by a linear interpolation method based on the calculated maximum speed and the noise sound power level database.

[0027] The memory may also store the location information of each wind turbine generator set and the noise detection point and the wind direction at the machine site of each wind turbine generator set collected in real time.

[0028] The wind farm controller can also be configured to calculate the straight-line distance between each wind turbine and the noise detection point according to the acquired position information, calculate the angle between each wind turbine and the noise detection point according to the acquired position information and the direction of collection, and calculate the noise propagation loss value of each wind turbine at the corresponding wind speed by linear interpolation method based on the wind farm noise propagation database and the calculated straight-line distance and angle.

[0029] The wind farm controller can also be configured to use the noise sound power level and noise propagation loss value of each wind turbine to respectively calculate the noise sound pressure level of each wind turbine at the noise detection point; and superimpose the calculated noise pressure levels of each wind turbine at the noise detection point to calculate the total noise sound pressure level at the noise detection point.

[0030] The wind farm controller can also be configured to control the output power of each wind turbine generator set according to the calculated total noise pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise pressure level does not exceed the noise threshold.

[0031] The wind farm controller can also be configured to compare the calculated total noise sound pressure level with the noise threshold of the noise detection point, wherein when the total noise sound pressure level is greater than the noise threshold, the output power of each wind turbine generator set at the collected wind speed is calculated based on the unit database, and based on the calculated output power of each wind turbine generator set, a power control command value of each wind turbine generator set is determined by using an optimization algorithm when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum, and the determined power control command value of each wind turbine generator set is used to control the operation of the corresponding wind turbine generator set.

[0032] One aspect of the present invention is to provide a computer-readable storage medium storing a program, wherein the program may include instructions for executing the above-mentioned wind farm noise prediction method.

[0033] One aspect of the present invention is to provide a computer, comprising a readable medium storing a computer program and a processor, wherein the computer program is executed when the processor runs the computer program.

[0034] Based on the wind farm noise prediction method, device and system described above, the noise sound pressure level at the noise detection point can be predicted more accurately and quickly in real time by using the measured wind turbine noise sound power level database and the simulated offline wind farm noise propagation database, and there is no need to install a sound pressure sensor, reducing the cost and the risk of signal transmission failure. In addition, by introducing an optimization algorithm (for example, the openmdao package), the optimal solution can be quickly found in the wind farm controller program to implement a wind farm control solution in which the noise does not exceed the limit and the wind farm has the maximum power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above features and other purposes, characteristics and advantages of the present invention will become more apparent with reference to the accompanying drawings, in which:

[0036] Figure 1 is a flow chart of a method for predicting wind farm noise according to an example embodiment of the present disclosure;

[0037] Figure 2 is a flow chart of a method for predicting wind farm noise according to another exemplary embodiment of the present disclosure;

[0038] Figure 3 is a block diagram of a wind farm noise prediction device according to an example embodiment of the present disclosure;

[0039] Figure 4 is a block diagram of a wind farm noise prediction device according to another exemplary embodiment of the present disclosure;

[0040] Figure 5 is a block diagram of a wind farm noise prediction system according to an example embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] Reference will now be made in detail to exemplary embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same reference numerals always indicate the same components. The embodiments will be described below with reference to the accompanying drawings to facilitate explanation of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0042] In the present disclosure, terms including ordinal numbers such as "first", "second", etc. may be used to describe various elements, but these elements should not be understood to be limited to these terms. These terms are only used to distinguish one element from other elements. For example, without departing from the scope of the present disclosure, a first element may be referred to as a second element, and vice versa.

[0043] Figure 1 is a flow chart of a wind farm noise prediction method according to an example embodiment of the present disclosure.

[0044] Reference Figure 1 In step S101, the wind speed at the machine site of at least one wind turbine generator set in the wind farm that has an impact on the noise at the noise detection point is collected in real time. In the preliminary preparation process for predicting the noise level of the wind farm, it is necessary to select at least one wind turbine generator set in the wind farm that has an obvious impact on the noise detection point, and then use the selected at least one wind turbine generator set to predict the noise level of the wind farm. When predicting the noise level of the wind farm, the wind speed at the machine site of the selected wind turbine generator set is collected in real time.

[0045] In step S102, the noise sound power level of at least one selected wind turbine generator set at the collected wind speed is calculated respectively according to the noise sound power level database and the unit database. According to an embodiment of the present disclosure, the noise sound power level database and the unit database can be constructed respectively based on the noise sound power level data and output power data of the wind turbine generator set of the same type as the selected at least one wind turbine generator set at different wind speeds and different rotation speeds. The noise sound power level database of the present disclosure may include wind speed data, rotation speed data, and noise sound power level data, as shown in Table 1, wherein Rot Speed ​​represents the rotation speed of the wind turbine generator set at the corresponding wind speed, in rpm, Windspeed represents the wind speed at the machine site of the wind turbine generator set, in m / s, and LW represents the noise sound power level of the wind turbine generator set, in dB.

[0046] Table 1

[0047]

[0048] According to an embodiment of the present disclosure, the unit database may include a first database including wind speed data, maximum rotation speed data, and minimum rotation speed data. Table 2 below shows the first database including wind speed, maximum rotation speed, and minimum rotation speed data, wherein Wind speed represents the wind speed at the machine site of the wind turbine generator set, in m / s, Min RotSpeed ​​represents the minimum rotation speed of the wind turbine generator set at the corresponding wind speed, in rpm, and Max Rot Speed ​​represents the maximum rotation speed of the wind turbine generator set at the corresponding wind speed, in rpm.

[0049] Table 2

[0050]

[0051] After obtaining the noise sound power level database and the unit database, the maximum speed of each wind turbine generator set in the selected at least one wind turbine generator set at the corresponding wind speed can be calculated by the linear interpolation method according to the real-time collected wind speed. Since the data in the constructed unit database are all discrete data, a two-dimensional linear interpolation method is used to calculate the maximum wind speed of the selected wind turbine generator set at the corresponding wind speed. For example, when the wind speed of a wind turbine generator set collected is 4m / s, according to Table 2, when the wind speed is 3m / s, the maximum speed is 8.19rpm, and when the wind speed is 5m / s, the maximum speed is 8.19rpm. According to the two-dimensional linear interpolation method, it can be calculated that the maximum speed is 8.19rpm when the wind speed is 4m / s.

[0052] After calculating the maximum rotation speed at the wind speed collected in real time, the noise sound power level of the wind turbine generator set at the corresponding wind speed can be calculated by using a linear interpolation method according to the noise sound power database (i.e., Table 1). For example, when the wind speed of a wind turbine generator set collected in real time is 4m / s and the maximum rotation speed at this wind speed is 8.19rpm, the noise sound power level of the wind turbine generator set at the corresponding wind speed and rotation speed can be calculated according to Table 1 using a two-dimensional linear interpolation method.

[0053] In step S103, the noise propagation loss value of the at least one wind turbine generator set at the collected wind speed is calculated respectively according to the wind farm noise propagation database. According to an embodiment of the present disclosure, it is necessary to build a wind farm noise propagation database. Specifically, firstly, a model of the wind farm to be predicted is established by wind power simulation software (e.g., windpro), and the wind farm model includes environmental parameters, terrain model, and wind turbine model in the wind farm area. Then, the propagation process of the noise in the wind farm is simulated using the established wind farm model, and the simulation results are analyzed to derive an offline wind farm noise propagation database. The wind farm noise propagation database of the present disclosure may include distance data between the wind turbine generator set and the noise detection point, angle data between the wind turbine generator set and the noise detection point, and noise propagation loss value data, as shown in Table 3 below. Wherein, LW-L represents the noise propagation loss value, in dB, D represents the distance between the wind turbine generator set and the noise detection point, and angle represents the angle between the wind turbine generator set and the noise detection point. The angle in Table 3 is the angle between the wind direction and the line connecting the wind turbine generator set and the noise detection point.

[0054] Table 3

[0055]

[0056] In calculating the noise propagation loss value of the selected wind turbine generator set at the corresponding collected wind speed, the location information of each wind turbine generator set and the noise detection point in the selected wind turbine generator set can be obtained, that is, the geographical location of the wind turbine generator set and the geographical location of the noise detection point, which can be respectively expressed by WT i (x, y) and R(x, y), where i represents the i-th wind turbine in the selected wind turbine, x represents longitude, and y represents latitude. The straight-line distance between the wind turbine and the noise detection point is calculated based on the acquired position information of the wind turbine and the noise detection point, and D(i) can be used to represent the straight-line distance between the i-th wind turbine and the noise detection point.

[0057] The angle between each wind turbine and the noise detection point is calculated based on the acquired position information and the wind direction at the station of each wind turbine collected in real time. Taking a wind turbine as an example, the angle formed by the collected wind direction of the wind turbine and the line between the wind turbine and the noise detection point is determined as the angle between the wind turbine and the noise detection point.

[0058] After calculating the straight-line distance and angle between the wind turbine generator set and the noise detection point, the noise propagation loss value of each selected wind turbine generator set at the corresponding wind speed collected can be calculated by linear interpolation method according to the wind farm noise propagation database (i.e., Table 3). For example, the noise propagation loss value of the wind turbine generator set in the wind farm can be calculated using the same two-dimensional linear interpolation method as that for calculating the noise sound power level. Here, the calculation of the noise loss value is not described in detail.

[0059] In step S104, the total noise sound pressure level at the noise detection point is calculated using the noise sound power level and noise propagation loss value of at least one selected wind turbine generator set. Specifically, after obtaining the noise sound power level and noise loss value of each wind turbine generator set in the selected wind turbine generator set at the corresponding wind speed, the noise sound pressure level of each wind turbine generator set is calculated using the noise sound power level and noise propagation loss value of each wind turbine generator set. The noise sound pressure level of the wind turbine generator set can be calculated according to equation (1):

[0060] SPL(i)=SPL_Source(i)-SPL_Loss(i) (1)

[0061] Among them, SPL(i) represents the noise pressure level of the i-th wind turbine at the noise detection point, SPL_Source(i) represents the noise power level of the i-th wind turbine, and SPL_Loss(i) represents the noise propagation loss value of the i-th wind turbine in the wind farm.

[0062] After calculating the noise pressure level of each selected wind turbine generator set, the noise pressure level of each wind turbine generator set is superimposed to calculate the total noise pressure level at the noise detection point. Specifically, the total noise pressure level can be calculated according to equation (2):

[0063] Lp=10log 10 (10 SPL(1) / 10 +10 SPL(2) / 10 +...+10 SPL(i) / 10 ) (2)

[0064] Wherein, Lp represents the total noise sound pressure level, SPL(1) represents the noise sound pressure level of the first wind turbine at the noise detection point, SPL(2) represents the noise sound pressure level of the second wind turbine at the noise detection point, and SPL(i) represents the noise sound pressure level of the i-th wind turbine at the noise detection point.

[0065] Figure 2 is a flowchart of a method for predicting wind farm noise according to another example embodiment of the present disclosure.

[0066] Reference Figure 2 , in step S201, a noise sound power level database and a unit database are constructed. According to an embodiment of the present disclosure, before predicting the noise sound pressure level of a certain wind farm, at least one wind turbine generator set in the wind farm that has a significant impact on the noise detection point is selected, and then the noise sound power level database and the unit database are respectively constructed according to the noise sound power level data and output power data of the wind turbine generator set of the same type as the selected at least one wind turbine generator set at different wind speeds and different rotation speeds. The constructed noise sound power level database may include wind speed, rotation speed and noise sound power level data, as shown in Table 1. The constructed unit database may include a first database containing wind speed, maximum rotation speed and minimum rotation speed data and a second database containing wind speed, rotation speed and output power data. The first database may be represented by Table 2. Table 4 below shows a second database containing wind speed, rotation speed and output power data, wherein Wind speed represents the wind speed at the machine site of the wind turbine generator set, in m / s, Rot Speed ​​represents the rotation speed of the wind turbine generator set at the corresponding wind speed, in rpm, and P represents the output power of the wind turbine generator set at the corresponding wind speed, in MW.

[0067] Table 4

[0068]

[0069] In step S202, a wind farm noise propagation database is constructed. For example, a wind farm model expected to be predicted can be established by simulation software (e.g., windpro), and the noise propagation process of the wind farm can be simulated using the established wind farm model, and a wind farm noise propagation database can be constructed based on the simulation results. The wind farm noise propagation database can be represented by Table 3. It should be noted that Tables 1, 2, 3, and 4 are only exemplary, and the present disclosure is not limited thereto.

[0070] In step S203, the wind speed at the machine site of at least one wind turbine generator set in the wind farm that has a significant impact on the noise at the noise detection point is collected in real time. For example, when three wind turbine generator sets that have a significant impact on the noise of the wind farm are selected, the wind speed and wind direction at the machine sites of the three wind turbine generator sets need to be collected in real time.

[0071] In step S204, the noise sound power level of at least one selected wind turbine generator set at the wind speed collected in real time is calculated respectively. Specifically, according to the first database and the wind speed collected in real time, the maximum rotation speed of each wind turbine generator set in the selected at least one wind turbine generator set at the corresponding wind speed is calculated respectively by a linear interpolation method, and then, according to the calculated maximum rotation speed and the noise sound power level database, the noise sound power level of each selected wind turbine generator set at the corresponding wind speed is calculated respectively by a linear interpolation method. Taking a wind turbine generator set as an example, after the wind speed at the machine site of the wind turbine generator set is collected, the maximum rotation speed of the wind turbine generator set at the wind speed is calculated by two-dimensional linear interpolation using the wind speed combined with the first database, and the noise sound power level of the wind turbine generator set is calculated by two-dimensional linear interpolation using the calculated maximum rotation speed and the collected wind speed combined with the noise sound power database.

[0072] In step S205, the noise propagation loss value of the selected at least one wind turbine generator set under the wind speed collected in real time is calculated respectively. For example, the position information of each wind turbine generator set and the noise detection point in the selected at least one wind turbine generator set is obtained, and the straight-line distance between each wind turbine generator set and the noise detection point is calculated respectively according to the obtained position information, and the angle between each wind turbine generator set and the noise detection point is calculated respectively according to the obtained position information and the wind direction at the machine site of each wind turbine generator set collected in real time, and the noise propagation loss value of each wind turbine generator set under the corresponding wind speed is calculated respectively by linear interpolation method according to the wind farm noise propagation database and the calculated straight-line distance and angle.

[0073] In step S206, the noise pressure level of the at least one selected wind turbine generator set at the noise detection point is calculated respectively. For example, for each selected wind turbine generator set, the noise pressure level of each wind turbine generator set can be calculated respectively by equation (1).

[0074] In step S207, the total noise pressure level at the noise detection point is calculated. After the noise pressure level of each selected wind turbine generator set at the noise detection point is obtained, the total noise pressure level can be calculated by equation (2).

[0075] After predicting the total noise sound pressure level, in step S208, the calculated total noise sound pressure level is compared with the noise threshold of the noise detection point to determine whether to optimize the control of the selected wind turbine generator set. When the calculated total noise sound pressure level is less than or equal to the set noise threshold, it indicates that the noise generated by the wind turbine generator set in the wind farm is within the allowable range, and the wind turbine generator set continues to operate at the current speed. When the calculated total noise sound pressure level is greater than the set noise threshold, proceed to step S209.

[0076] In step S209, the output power of each wind turbine in the selected at least one wind turbine is calculated based on the unit database at the respective wind speeds collected in real time, and the power control command value of each wind turbine is determined by using an optimization algorithm based on the calculated output power of each wind turbine when the total noise pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum. Specifically, the maximum speed of the wind turbine can be calculated by a two-dimensional linear interpolation method according to the wind speed collected in real time in combination with the first database (i.e., Table 2), and then the calculated maximum speed and the corresponding wind speed are combined with the second database (i.e., Table 4) to calculate the output power of the wind turbine at the corresponding wind speed by a two-dimensional linear interpolation method, and the calculated output power of each wind turbine is input as an initial value into the optimization algorithm, and the optimization algorithm is used to find a control scheme in which the total noise pressure level does not exceed the limit (i.e., less than or equal to the noise threshold) and the output power of the entire wind farm is maximum. For example, the openmdao package can be used to optimize with the maximum power generation of the entire wind farm as the goal and the noise limit as the constraint condition. The openmdao package is combined with the noise sound power level database, wind farm noise propagation database and unit database to find a control solution that does not exceed the total noise sound pressure level (i.e. less than or equal to the noise threshold) and maximizes the output power of the entire wind farm. The openmdao package is a package for optimization based on the least squares method.

[0077] In the present disclosure, the input and output of the optimization algorithm are set according to the noise estimation and optimization characteristics. For example, the noise limit (i.e., the total noise sound pressure level is less than or equal to the noise threshold) is used as a constraint condition to find the power control command value of the selected wind turbine generator set with the largest power generation of the entire wind farm within the constraint condition. However, the optimization algorithm of the present disclosure is not limited to this, and other optimization algorithms such as particle swarm optimization, genetic algorithm, etc. can also be used for optimization.

[0078] In step S210, the power control command value corresponding to the maximum output power of the entire field of each wind turbine generator set is used to control the operation of the corresponding wind turbine generator set. Specifically, the power control command value of the selected wind turbine generator set obtained in step S209 is sent to the controller of the corresponding wind turbine generator set, so that the selected wind turbine generator set operates according to the corresponding power control command value, that is, the wind turbine generator set generates electricity with the output power of the selected wind turbine generator set obtained by the optimization algorithm.

[0079] The purpose of the optimization algorithm disclosed in the present invention is to find a solution that minimizes the output power loss of wind turbines that have a significant impact on noise detection points while meeting noise limits. Since wind turbines that have no impact on noise detection points are capturing maximum wind energy, it is only necessary to find a solution that minimizes the output power loss of wind turbines that have a significant impact on noise detection points, thereby ensuring maximum power generation for the entire field.

[0080] Figure 3 is a block diagram of a wind farm noise prediction device according to an example embodiment of the present disclosure.

[0081] Reference Figure 3 , the wind farm noise prediction device 300 may include a data acquisition module 301, a database construction module 302, and a data calculation module 303. Each module in the device 300 according to the present disclosure may be implemented by one or more modules, and the name of the corresponding module may vary according to the type of the device. In various embodiments, some modules in the device 300 may be omitted, or additional modules may be included. In addition, the modules according to various embodiments of the present disclosure may be combined to form a single entity, and thus may equivalently perform the functions of the corresponding modules before the combination.

[0082] The data acquisition module 301 collects in real time the wind speed at the machine site of at least one wind turbine generator set in the wind farm that has a significant impact on the noise detection point. In the early stage of predicting the noise level of the wind farm, it is necessary to select at least one wind turbine generator set in the wind farm that has a significant impact on the noise detection point, and then use the selected at least one wind turbine generator set to predict the noise level of the wind farm.

[0083] The database construction module 302 can construct a noise sound power level database and a unit database based on the noise sound power level data and output power data of wind turbines of the same type as at least one selected wind turbine at different wind speeds and different rotational speeds, wherein the noise sound power level database includes wind speed, rotational speed and noise sound power level data, as shown in Table 1, and the unit database may include a first database including wind speed, maximum rotational speed and minimum rotational speed data (as shown in Table 2) and a second database including wind speed, rotational speed and output power data (as shown in Table 4).

[0084] The database construction module 302 can also construct a wind farm noise propagation database according to the result of simulating the noise propagation process of the wind farm, wherein the wind farm noise propagation database includes distance, angle and noise propagation loss value data. Specifically, simulation software can be used to first establish a wind farm model for expected prediction, for example, windpro software can be used to establish a wind farm model, and then the established wind farm model is used to simulate the noise propagation process. The database construction module 302 can construct a wind farm noise propagation database according to the simulation results, as shown in Table 3.

[0085] The data calculation module 303 can calculate the maximum speed of each selected wind turbine generator set at the corresponding wind speed based on the first database and the collected wind speed by a linear interpolation method, and calculate the noise sound power level of each wind turbine generator set at the corresponding wind speed based on the calculated maximum speed and the noise sound power level database by a linear interpolation method.

[0086] The data acquisition module 301 can also obtain the location information of each selected wind turbine and noise detection point and collect the wind direction at the machine site of each wind turbine in real time. The data calculation module 303 can calculate the straight-line distance between each wind turbine and the noise detection point according to the acquired location information, calculate the angle between each wind turbine and the noise detection point according to the acquired location information and the collected direction, and calculate the noise propagation loss value of each wind turbine at the corresponding wind speed by linear interpolation method according to the wind farm noise propagation database and the calculated straight-line distance and angle. The process of calculating the noise sound power level and the noise propagation loss value by the data calculation module 303 is the same as step S102 and step S103, which will not be repeated here.

[0087] After obtaining the noise sound power level and noise propagation loss value of each wind turbine generator set, the data calculation module 303 can calculate the noise sound pressure level of each wind turbine generator set at the noise detection point based on the noise sound power level and noise propagation loss value of each selected wind turbine generator set through equation (1), and use equation (2) to superimpose the calculated noise pressure levels of each wind turbine generator set at the noise detection point to calculate the total noise sound pressure level at the noise detection point.

[0088] Figure 4 is a block diagram of a wind farm noise prediction device according to another example embodiment of the present disclosure.

[0089] Reference Figure 4 The wind farm noise prediction device 400 may include a data acquisition module 401, a database construction module 402, a data calculation module 403 and a control module 404. The data acquisition module 401, the database construction module 402 and the data calculation module 403 have the same functions as the data acquisition module 301, the database construction module 302 and the data calculation module 303, which will not be described in detail here.

[0090] The control module 404 can control the output power of each wind turbine generator set according to the calculated total noise sound pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise sound pressure level does not exceed the noise threshold. Specifically, the control module 404 can compare the calculated total noise sound pressure level with the noise threshold of the noise detection point. When the total noise sound pressure level is greater than the noise threshold, the control module 404 calculates the output power of each wind turbine generator set in at least one wind turbine generator set at the collected wind speed based on the unit database, and determines the power control command value of each wind turbine generator set under the condition that the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximized by using an optimization algorithm based on the calculated output power of each wind turbine generator set, and uses the determined power control command value of each wind turbine generator set to control the operation of the corresponding wind turbine generator set.

[0091] For example, the control module 404 can calculate the maximum speed of the wind turbine generator set according to the real-time collected wind speed in combination with the first database (i.e., Table 2) through a two-dimensional linear interpolation method, and then use the calculated maximum speed and the corresponding wind speed in combination with the second database (i.e., Table 4) to calculate the output power of the wind turbine generator set at the corresponding wind speed through a two-dimensional linear interpolation method. Here, the data calculation module 403 can also be used to calculate the output power at the corresponding wind speed.

[0092] The control module 404 inputs the calculated output power of each wind turbine generator set as an initial value into the optimization algorithm, and uses the optimization algorithm to find a control scheme in which the total noise sound pressure level does not exceed the limit (i.e., less than or equal to the noise threshold) and the output power of the entire wind farm is the maximum, that is, to find the power control command value of each wind turbine generator set under the condition that the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is the maximum. For example, the control module 404 can use the optimization algorithm of the openmdao package, genetic algorithm, particle swarm algorithm, etc. to find the optimal control scheme with the maximum power generation of the entire wind farm and noise limit as constraints. Then, the control module 404 sends the power control command value of each wind turbine generator set obtained to the controller of the corresponding wind turbine generator set, so that each wind turbine generator set operates according to the corresponding power control command value.

[0093] Figure 5 is a block diagram of a wind farm noise prediction system according to an example embodiment of the present disclosure.

[0094] Reference Figure 5 The wind farm noise prediction system 500 may include a memory 501 and a wind farm controller 502 .

[0095] The memory 501 can store the wind speed and wind direction data of the wind turbine generator set collected in real time and the location information of the wind turbine generator set and the noise detection point.

[0096] According to an embodiment of the present disclosure, first, at least one wind turbine generator set having a significant impact on the noise at the noise detection point is selected in a wind farm to be predicted, and a noise sound power level database and a unit database are respectively constructed based on the noise sound power level data and output power data of wind turbine generator sets of the same type as the selected wind turbine generator set at different wind speeds and different rotation speeds.

[0097] The constructed noise sound power level database and unit database may be formed into an offline file and stored in the memory 501. The noise sound power level database may include wind speed, rotation speed and noise sound power level data, and the unit database includes a first database including wind speed, maximum rotation speed and minimum rotation speed data and a second database including wind speed, rotation speed and output power data.

[0098] The wind farm controller 502 can calculate the noise sound power level of each selected wind turbine generator set at the corresponding wind speed according to the noise sound power level database and the unit database. Specifically, the wind farm controller 502 can calculate the maximum speed of each selected wind turbine generator set at the corresponding wind speed according to the first database and the wind speed collected in real time by linear interpolation method; and calculate the noise sound power level of each wind turbine generator set at the corresponding wind speed according to the calculated maximum speed and the noise sound power level database by linear interpolation method.

[0099] In addition, in the embodiment of the present disclosure, a wind farm model can be established using simulation software such as windpro, and then the noise propagation process of the wind farm can be simulated using the established wind farm model, and a wind farm noise propagation database can be constructed based on the simulation results. The constructed wind farm noise propagation database can be formed into an offline file and stored in the memory 501. The wind farm noise propagation database includes distance, angle and noise propagation loss value data.

[0100] The wind farm controller 502 can calculate the noise propagation loss value of each selected wind turbine generator set at the wind speed collected in real time according to the wind farm noise propagation database. Specifically, the wind farm controller 502 can calculate the straight-line distance between each wind turbine generator set and the noise detection point according to the stored position information, calculate the angle between each wind turbine generator set and the noise detection point according to the position information and the direction collected in real time, and calculate the noise propagation loss value of each wind turbine generator set at the corresponding wind speed according to the wind farm noise propagation database and the calculated straight-line distance and angle by linear interpolation method.

[0101] After calculating the noise sound power level and noise propagation loss value of each selected wind turbine generator set, the wind farm controller 502 can use the noise sound power level and noise propagation loss value of each wind turbine generator set to calculate the total noise sound pressure level at the noise detection point. Specifically, the wind farm controller 502 can use equation (1) to calculate the noise sound pressure level of each wind turbine generator set at the noise detection point according to the calculated noise sound power level and noise propagation loss value of each wind turbine generator set, and then use equation (2) to perform superposition processing on the calculated noise pressure level of each wind turbine generator set at the noise detection point to calculate the total noise sound pressure level at the noise detection point.

[0102] In the present disclosure, the wind farm controller 502 can control the output power of each wind turbine generator set according to the calculated total noise sound pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise sound pressure level does not exceed the noise threshold. Specifically, the wind farm controller 502 can compare the calculated total noise sound pressure level with the noise threshold of the noise detection point, wherein when the total noise sound pressure level is greater than the noise threshold, the wind farm controller 502 calculates the output power of each wind turbine generator set at the wind speed collected in real time based on the unit database, and determines the power control command value of each wind turbine generator set when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximized based on the calculated output power of each wind turbine generator set by using an optimization algorithm, and uses the determined power control command value of each wind turbine generator set to control the operation of the corresponding wind turbine generator set. For example, the calculated output power of each wind turbine generator set can be input as an initial value into the optimization algorithm, and the optimization algorithm is used to find a control scheme in which the total noise sound pressure level does not exceed the limit (i.e., less than or equal to the noise threshold) and the output power of the entire wind farm is the largest. For example, the openmdao package is combined with the noise sound power level database, the wind farm noise propagation database and the second database to find a control scheme in which the total noise sound pressure level does not exceed the limit (i.e., less than or equal to the noise threshold) and the output power of the entire wind farm is the largest. However, the optimization algorithm disclosed in the present invention is not limited to this, and genetic algorithms, particle calculation algorithms, etc. can also be used as optimization algorithms to implement the above optimization process.

[0103] The wind farm controller 502 can send the obtained power control command value of each wind turbine to the controller of the corresponding wind turbine, so that each wind turbine operates according to the corresponding power control command value to achieve maximum power generation of the wind farm without exceeding the total noise sound pressure level at the noise detection point.

[0104] The wind farm noise prediction method according to the example embodiment disclosed in the present invention may be implemented as a computer-readable instruction on a computer-readable recording medium, or may be sent via a transmission medium. A computer-readable recording medium is any data storage device that can store data that can be read thereafter by a computer system. Examples of computer-readable recording media include read-only memory (ROM), random access memory (RAM), compact disk (CD-ROM), digital versatile disk (DVD), magnetic tape, floppy disk, optical data storage device, but are not limited thereto. Transmission media may include carrier waves sent through a network or various types of communication channels. The computer-readable recording medium may also be distributed to computer systems connected to a network so that computer-readable instructions are stored and executed in a distributed manner.

[0105] According to the wind farm noise prediction method, device and system disclosed in the present invention, a multi-dimensional wind turbine noise source model library can be formed based on the measured wind turbine noise power level data, and a specific wind farm noise propagation model offline file can be prepared in advance through simulation software to improve the real-time and accuracy of noise prediction. In addition, based on the above two offline files, when the total noise sound pressure level at the predicted noise detection point exceeds the limit, an optimization algorithm is used to find a solution that minimizes the loss of power generation in the entire field while the noise does not exceed the limit, and a control instruction is sent to the corresponding wind turbine, so that the wind turbine executes the optimal control strategy to balance environmental friendliness and wind farm economy.

[0106] While the invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the claims.

Claims

1. A wind farm noise prediction method, characterized in that: The method comprises: Real-time collection of wind speed at a machine site of at least one wind turbine generator set in the wind farm that affects the noise at the noise detection point; Calculate the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed according to the noise sound power level database and the unit database; Calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed according to the wind farm noise propagation database; The total noise sound pressure level at the noise detection point is calculated using the noise sound power level and noise transmission loss value of each wind turbine generator set. The noise sound power level database includes wind speed, rotation speed and noise sound power level data, the unit database includes a first database including wind speed, maximum rotation speed and minimum rotation speed data, and the step of calculating the noise sound power level of each wind turbine generator set includes: According to the first database and the collected wind speed, respectively calculating the maximum rotation speed of each wind turbine generator set at the corresponding wind speed by a linear interpolation method; According to the collected wind speed, the calculated maximum rotation speed and the noise sound power level database, the noise sound power level of each wind turbine generator set at the corresponding wind speed is calculated respectively by a linear interpolation method.

2. The method according to claim 1, characterized in that The unit database also includes a second database containing wind speed, rotation speed and output power data.

3. The method according to claim 1, characterized in that The wind farm noise propagation database is constructed based on the following steps: Building wind farm models; Use the established wind farm model to simulate the noise propagation process of the wind farm; Based on the simulation results, a wind farm noise propagation database is constructed. Among them, the wind farm noise propagation database includes distance, angle and noise propagation loss value data.

4. The method according to claim 1, characterized in that The step of calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed comprises: Obtaining the location information of each wind turbine generator set and noise detection point; Calculating the straight-line distance between each wind turbine generator set and the noise detection point according to the acquired position information; Calculate the angle between each wind turbine generator set and the noise detection point according to the acquired position information and the wind direction at the machine site of each wind turbine generator set collected in real time; According to the wind farm noise propagation database and the calculated straight-line distance and angle, the noise propagation loss value of each wind turbine generator set at the corresponding wind speed is calculated respectively by a linear interpolation method.

5. The method according to claim 1, characterized in that The steps for calculating the total noise sound pressure level at the noise detection point include: Calculate the noise sound pressure level of each wind turbine generator set at the noise detection point by using the noise sound power level and the noise propagation loss value of each wind turbine generator set; The calculated noise pressure level of each wind turbine generator set at the noise detection point is superimposed to calculate the total noise sound pressure level at the noise detection point.

6. The method according to claim 1, characterized in that Also includes: The output power of each wind turbine generator set is controlled according to the calculated total noise pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise pressure level does not exceed the noise threshold.

7. The method according to claim 6, characterized in that The step of controlling the output power of each wind turbine generator set according to the calculated total noise sound pressure level at the noise detection point comprises: Compare the calculated total noise sound pressure level with the noise threshold at the noise detection point. Among them, when the total noise sound pressure level is greater than the noise threshold, the output power of each wind turbine generator set at the collected wind speed is calculated based on the unit database, and based on the calculated output power of each wind turbine generator set, the power control command value of each wind turbine generator set is determined by using an optimization algorithm when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum, and the determined power control command value of each wind turbine generator set is used to control the operation of the corresponding wind turbine generator set.

8. A wind farm noise prediction device, characterized in that: The device comprises: A data acquisition module, used for real-time acquisition of wind speed at a machine site of at least one wind turbine generator set in the wind farm that affects the noise at the noise detection point; Data calculation module, used for: Calculate the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed according to the noise sound power level database and the unit database; Calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed according to the wind farm noise propagation database; The total noise sound pressure level at the noise detection point is calculated using the noise sound power level and noise transmission loss value of each wind turbine generator set. The noise sound power level database includes wind speed, rotation speed and noise sound power level data, the unit database includes a first database including wind speed, maximum rotation speed and minimum rotation speed data, and the data calculation module is also used for: According to the first database and the collected wind speed, respectively calculating the maximum rotation speed of each wind turbine generator set at the corresponding wind speed by a linear interpolation method; According to the collected wind speed, the calculated maximum rotation speed and the noise sound power level database, the noise sound power level of each wind turbine generator set at the corresponding wind speed is calculated respectively by a linear interpolation method.

9. The device according to claim 8, characterized in that It also includes a database construction module for constructing a noise sound power level database and a unit database based on the noise sound power level data and output power data of wind turbine generator sets of the same type as each wind turbine generator set at different wind speeds and different rotation speeds, The unit database also includes a second database containing wind speed, rotation speed and output power data.

10. The device according to claim 8, characterized in that It also includes a database construction module, which is used to construct a wind farm noise propagation database according to the result of simulating the noise propagation process of the wind farm. Among them, the wind farm noise propagation database includes distance, angle and noise propagation loss value data.

11. The device according to claim 8, characterized in that The data acquisition module is also used to obtain the location information of each wind turbine generator set and the noise detection point and to collect the wind direction at the machine site of each wind turbine generator set in real time, and The data calculation module calculates the straight-line distance between each wind turbine generator set and the noise detection point according to the acquired position information, calculates the angle between each wind turbine generator set and the noise detection point according to the acquired position information and the acquisition direction, and calculates the noise propagation loss value of each wind turbine generator set at the corresponding wind speed by linear interpolation method according to the wind farm noise propagation database and the calculated straight-line distance and angle.

12. The device according to claim 8, characterized in that The data calculation module is also used to: Calculate the noise sound pressure level of each wind turbine generator set at the noise detection point by using the noise sound power level and the noise propagation loss value of each wind turbine generator set; The calculated noise pressure level of each wind turbine generator set at the noise detection point is superimposed to calculate the total noise sound pressure level at the noise detection point.

13. The device according to claim 8, characterized in that It also includes a control module for controlling the output power of each wind turbine generator set according to the calculated total noise pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise pressure level does not exceed the noise threshold.

14. The device according to claim 13, characterized in that The control module is used to compare the calculated total noise sound pressure level with the noise threshold of the noise detection point. Among them, when the total noise sound pressure level is greater than the noise threshold, the output power of each wind turbine generator set at the collected wind speed is calculated based on the unit database, and based on the calculated output power of each wind turbine generator set, the power control command value of each wind turbine generator set is determined by using an optimization algorithm when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum, and the determined power control command value of each wind turbine generator set is used to control the operation of the corresponding wind turbine generator set.

15. A wind farm noise prediction system, characterized in that: The system comprises: A memory, used to store a database of wind speed, noise sound power and a wind turbine generator set database collected in real time at a wind farm site that affects the noise at a noise detection point; The wind farm controller is configured to: Calculate the noise sound power level of each wind turbine generator set in the at least one wind turbine generator set at the collected wind speed according to the noise sound power level database and the unit database; Calculating the noise propagation loss value of each wind turbine generator set at the collected wind speed according to the wind farm noise propagation database; The total noise sound pressure level at the noise detection point is calculated using the noise sound power level and noise transmission loss value of each wind turbine generator set. The noise sound power level database includes wind speed, rotation speed and noise sound power level data, the unit database includes a first database including wind speed, maximum rotation speed and minimum rotation speed data, and the wind farm controller is further configured as follows: According to the first database and the collected wind speed, respectively calculating the maximum rotation speed of each wind turbine generator set at the corresponding wind speed by a linear interpolation method; According to the collected wind speed, the calculated maximum rotation speed and the noise sound power level database, the noise sound power level of each wind turbine generator set at the corresponding wind speed is calculated respectively by a linear interpolation method.

16. The system of claim 15, wherein: The unit database also includes a second database containing wind speed, rotation speed and output power data.

17. The system of claim 15, wherein: The wind farm noise propagation database is constructed based on the simulation results of the noise propagation process of the wind farm. Among them, the wind farm noise propagation database includes distance, angle and noise propagation loss value data.

18. The system of claim 15, wherein: The memory is used to store the location information of each wind turbine generator set and the noise detection point and the wind direction at the machine site of each wind turbine generator set collected in real time, and The wind farm controller calculates the straight-line distance between each wind turbine generator set and the noise detection point according to the acquired position information, calculates the angle between each wind turbine generator set and the noise detection point according to the acquired position information and the acquisition direction, and calculates the noise propagation loss value of each wind turbine generator set at the corresponding wind speed by linear interpolation method according to the wind farm noise propagation database and the calculated straight-line distance and angle.

19. The system of claim 15, wherein: The wind farm controller is also configured to: Calculate the noise sound pressure level of each wind turbine generator set at the noise detection point by using the noise sound power level and the noise propagation loss value of each wind turbine generator set; The calculated noise pressure level of each wind turbine generator set at the noise detection point is superimposed to calculate the total noise sound pressure level at the noise detection point.

20. The system of claim 15, wherein: The wind farm controller is also configured to: The output power of each wind turbine generator set is controlled according to the calculated total noise pressure level at the noise detection point, so that the output power of the entire wind farm is maximized when the total noise pressure level does not exceed the noise threshold.

21. The system of claim 20, wherein: The wind farm controller is further configured to compare the calculated total noise sound pressure level with a noise threshold value at the noise detection point, Among them, when the total noise sound pressure level is greater than the noise threshold, the output power of each wind turbine generator set at the collected wind speed is calculated based on the unit database, and based on the calculated output power of each wind turbine generator set, the power control command value of each wind turbine generator set is determined by using an optimization algorithm when the total noise sound pressure level is less than or equal to the noise threshold and the output power of the entire wind farm is maximum, and the determined power control command value of each wind turbine generator set is used to control the operation of the corresponding wind turbine generator set.

22. A computer-readable storage medium storing a program, characterized in that: The program includes instructions for executing the method according to any one of claims 1-7.

23. A computer, comprising a readable medium storing a computer program and a processor, characterized in that: When the processor runs the computer program, the method according to any one of claims 1 to 7 is performed.

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