Alpha stable distribution estimation method for underwater noise distance characteristics of wind power plant

Abstract

The invention belongs to the technical field of wind power plant noise, and particularly relates to an alpha stable distribution estimation method for underwater noise distance characteristics of a wind power plant. The method comprises the following steps: firstly, preprocessing wind power plant noise to obtain wind power plant noise time domain oscillogram under different distances; thirdly, determining value ranges of stable distribution a and gamma according to parameter characteristics of alpha stable distribution, and selecting a value range of omega0 in combination with a range of simulation data; in order to solve the problem that gamma parameter estimation errors are large, a new estimation method based on semi-probability scale parameters is combined, and time domain waveform data of wind power plant noise at different distances are estimated through a new parameter estimation formula. According to the alpha stable distribution estimation method for the distance characteristics of the underwater noise of the wind power plant, the characteristics of the underwater noise of the wind power plant changing along with the distance can be described more accurately, and an effective means is provided for evaluation and management of the underwater noise of the wind power plant.

Description

Technical Field

[0001] The invention belongs to the technical field of wind farm noise, and in particular relates to a method for describing distance characteristics of underwater noise of a wind farm based on alpha stable distribution. Background Art

[0002] With the continuous advancement of global offshore renewable energy technology, the offshore wind farm industry is experiencing a booming development to meet the urgent needs of international energy conservation and emission reduction. However, the noise problems generated during its construction and operation phases have attracted widespread attention. Noise problems exist significantly throughout the life cycle of offshore wind farms.

[0003] During the operation phase, the operation of the wind turbine and the mechanical vibration in the nacelle continue to generate noise. This process will continue until the wind farm stops operating. The noise can be transmitted to places several kilometers away from the sound source. In addition, the mechanical vibration noise during the operation phase lasts the longest and has the most significant impact on underwater noise, and the α-stable distribution can well describe this non-Gaussian noise. However, the α-stable distribution does not have a closed characteristic function, and the existing estimation methods cannot meet the accuracy requirements for underwater noise of wind farms. Summary of the invention

[0004] The purpose of the present invention is to provide a method for describing the distance characteristics of underwater noise in a wind farm based on α stable distribution, and to complete the γ estimation combined with semi-probability to solve the problem of low accuracy in underwater noise estimation in the interval α∈(1,2].

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A method for estimating the α-stable distribution of underwater noise distance characteristics of a wind farm comprises the following steps:

[0007] S1. Read wind farm noise data, perform wavelet denoising on the original data, and obtain time domain waveform data of underwater noise of wind farms at different distances;

[0008] S2, determine the effective time-frequency waveform data within the target time period, and calculate the mathematical expectation of the noise data at different distances;

[0009] S3. Select the value range of ω0 in combination with the simulation data range; it is necessary to determine whether the standard deviation of different ω0 is the smallest, and discard the value range that does not meet the conditions;

[0010] S4. After selecting the range of ω0 values, the parameter estimation optimization is performed in combination with the semi-probability formula to give a new scale estimation algorithm;

[0011] S5. Through a new estimation algorithm, the distance characteristics of the underwater noise of the wind farm are given, and the relationship between the underwater noise of the wind farm and the α stable distribution parameters at different distances is obtained.

[0012] Furthermore, step S1 specifically includes the following steps:

[0013] S11, extracting the original data of wind farm noise and performing wavelet denoising to obtain original waveform data, wherein the time domain data includes underwater noise generated during the operation period of the wind farm;

[0014] S12. Split the time domain data to keep the noise data lengths at different distances consistent, and obtain the time domain waveform data of the underwater noise of the wind farm at different distances.

[0015] Furthermore, step S3 specifically includes the following steps:

[0016] S31. Use the characteristic function of a stable random variable to describe its distribution characteristics and corresponding parameter estimation, expressed by the following formula;

[0017]

[0018] In the formula, ω=1 and ω=ω0 represent two characteristic function points, and sign(·) represents the sign function. α is the characteristic index, and its value range is 0<α≤2. β is the skew parameter, which determines the degree of skewness of the distribution, and its value range is -1≤β≤1. γ is the scale parameter, also known as the dispersion coefficient and deviation, which describes the degree to which the stable distribution random variable deviates from the mean or median. γ can be any positive number, similar to the variance in the Gaussian distribution. δ is the location parameter, which determines the location of the peak of the probability density function of the α stable distribution, and its value range is -∞<δ<∞.

[0019] S32, estimating the expressions of α characteristic parameter and γ scale parameter, such as formula;

[0020]

[0021] Where N is the length of the observation sample X, X = {x1, x2, x3, … x i ,…,x N}; ω0 is closely related to the accuracy of parameter estimation in the characteristic function of stable distribution, and its value range is 0<ω0<0.5 and 1<ω0<3. The estimation accuracy of the statistical characteristics of underwater noise in wind farms with different value ranges of ω0 is analyzed; if α∈(0,2] is set, the key parameters of the input stable distribution data are simulated.

[0022] S33. The simulation scale parameter γ value range is set in the interval (0.0001, 0.001).

[0023] Furthermore, step S4 specifically includes the following steps:

[0024] S41, set α∈(0,2], γ∈(0.0001,0.001) and estimate based on the method based on semi-probability scale parameters;

[0025] S42. Combined with the simulation data for comparison, the new parameter estimation algorithm is as follows:

[0026]

[0027] H(·) is a piecewise function. When α∈(1,2], the function value is 0. At this time, x i :P(X|>x i )=0.5; when α∈(0,1], the function value is 1.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention uses improved α-stable distribution to describe the noise characteristics. The statistical analysis shows that with the increase of distance, the time domain waveform pulse characteristics of underwater noise gradually weaken, and the maximum values ​​of the scale parameter γ in two experiments are 0.00094 and 0.00087 respectively, which decreases with the increase of distance; γ measures the degree of deviation of random variables from mathematical expectations in the time domain waveform, which implies that the amplitude of the time domain waveform is more stable as the distance increases; the law of sound propagation is consistent with the overall trend of the scale parameter; the present invention improves the distance characteristic estimation algorithm for describing the underwater noise of a wind farm with α-stable distribution, so that the complexity of estimation is lower and the accuracy of estimation is higher; the method can accurately estimate the distance characteristics of underwater noise of a wind farm, which is helpful to more comprehensively understand the underwater noise characteristics and environmental impact of offshore wind farms, and provides an important basis for optimizing engineering design and operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 It is a flow chart of the steps of the method for estimating the α-stable distribution of the underwater noise distance characteristics of a wind farm according to the present invention;

[0032] Figure 2a-2b Comparison chart of improved estimation methods. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0034] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0035] like Figure 1 As shown, a method for estimating the α-stable distribution of the distance characteristics of underwater noise in a wind farm comprises the following steps:

[0036] S1. Obtain wind farm noise signals within a target time period, select time-frequency signals with the same time length according to different distances, read wind farm noise data, perform wavelet denoising on the original data, and obtain wind farm underwater noise time domain waveform data at different distances. Specifically, the following steps are included:

[0037] S11, extracting the original data of wind farm noise and performing wavelet denoising to obtain original waveform data, wherein the time domain data includes underwater noise generated during the operation period of the wind farm;

[0038] S12. Split the time domain data to keep the noise data lengths at different distances consistent, and obtain the time domain waveform data of the underwater noise of the wind farm at different distances.

[0039] S2. Determine the effective time-frequency waveform data within the target time period, and calculate the mathematical expectation of the noise data at different distances.

[0040] S3. Select the value range of ω0 in combination with the simulation data range. It is necessary to determine whether the standard deviation of different ω0 is the smallest, and discard the value range that does not meet the conditions. Specifically, the following steps are included:

[0041] S31. Use the characteristic function of a stable random variable to describe its distribution characteristics and corresponding parameter estimation, expressed by the following formula;

[0042]

[0043] In the formula, ω=1 and ω=ω0 represent two characteristic function points, and sign(·) represents the sign function. α is the strength of the pulse characteristic of the stable distribution and the tail thickness of the probability density function, and its value range is 0<α≤2. β is the skew parameter, which determines the degree of skewness of the distribution, and its value range is -1≤β≤1. γ is the scale parameter, which describes the degree to which the stable distribution random variable deviates from the mean or median. γ is an arbitrary positive number, similar to the variance in the Gaussian distribution. δ is the location parameter, which determines the location of the peak of the probability density function of the α stable distribution, and its value range is -∞<δ<∞.

[0044] S32, estimating the expressions of α characteristic parameter and γ scale parameter, such as formula;

[0045]

[0046]

[0047] Where N is the length of the observation sample X, X = {x1, x2, x3, … x i ,…,x N}. ω0 is closely related to the accuracy of parameter estimation in the characteristic function of stable distribution, and its value range is 0<ω0<0.5 and 1<ω0<3. The estimation accuracy of the statistical characteristics of underwater noise in wind farms with different value ranges of ω0 is analyzed. If α∈(0,2] is set, the key parameters of the input stable distribution data are simulated.

[0048] S33, the simulation sets the scale parameter γ value range in the interval (0.0001, 0.001). The error of the scale parameter γ estimated by the original method in this interval is large. In order to solve the problem of large error in the estimation of the scale parameter of wind farm noise, a new scale estimation method is given in combination with the estimation method based on semi-probability scale parameter.

[0049] like Figure 2a As shown, blue is the true α value, green and red are the results estimated by the formula for 0<ω0<0.5 and 1<ω0<3 respectively. The horizontal axis is the value of α∈(0,2], and the vertical axis is the standard deviation of α. When the range of ω0 is 1<ω0<3, the estimated standard deviation of α is small.

[0050] S4. After selecting the range of ω0 values, the parameter estimation optimization is performed in combination with the semi-probability formula to give a new scale estimation algorithm. Specifically, the following steps are included:

[0051] S41, set α∈(0,2], γ∈(0.0001,0.001) and estimate based on the method based on semi-probability scale parameters;

[0052] S32. Combined with the simulation data for comparison, the new parameter estimation method is as follows:

[0053]

[0054] H(·) is a piecewise function. When α∈(1,2], the function value is 0. At this time, x i :P(X|>x i )=0.5; when α∈(0,1], the function value is 1.

[0055] Figure 2b In the figure, the gray line is the value of the scale parameter in (0.0001, 0.001), and the green line is the estimated value of the improved formula. It can be seen that the error of the estimated scale parameter in this interval is large. The red line is the estimated comparison within the range of α∈(1,2], γ∈(0.0001, 0.001). It can be seen that within the entire effective range of α, the estimated value error of γ is the smallest.

[0056] S5. Through a new estimation algorithm, the distance characteristics of the underwater noise of the wind farm are given, and the relationship between the underwater noise of the wind farm and the α stable distribution parameters at different distances is obtained.

[0057] In summary, the statistical analysis of noise characteristics described by the improved α-stable distribution shows that this method can accurately estimate the distance characteristics of underwater noise in wind farms, which helps to more comprehensively understand the underwater noise characteristics of offshore wind farms and their environmental impacts, and provides an important basis for optimizing engineering design and operation management.

[0058] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for estimating the α-stable distribution of underwater noise distance characteristics of a wind farm, characterized in that: The following steps are involved: S1. Read wind farm noise data, perform wavelet denoising on the original data, and obtain time domain waveform data of underwater noise of wind farms at different distances; S2, determine the effective time-frequency waveform data within the target time period, and calculate the mathematical expectation of the noise data at different distances; S3. Select the value range of ω0 in combination with the simulation data range; it is necessary to determine whether the standard deviation of different ω0 is the smallest, and discard the value range that does not meet the conditions; S4. After selecting the range of ω0 values, the parameter estimation optimization is performed in combination with the semi-probability formula to give a new scale estimation algorithm; S5. Through a new estimation algorithm, the distance characteristics of the underwater noise of the wind farm are given, and the relationship between the underwater noise of the wind farm and the α stable distribution parameters at different distances is obtained.

2. The method for estimating the α-stable distribution of the distance characteristics of underwater noise in a wind farm according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11, extracting the original data of wind farm noise and performing wavelet denoising to obtain original waveform data, wherein the time domain data includes underwater noise generated during the operation period of the wind farm; S12. Split the time domain data to keep the noise data lengths at different distances consistent, and obtain the time domain waveform data of the underwater noise of the wind farm at different distances.

3. The method for estimating the α-stable distribution of the distance characteristics of underwater noise in a wind farm according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. Use the characteristic function of a stable random variable to describe its distribution characteristics and corresponding parameter estimation, expressed by the following formula; In the formula, ω=1 and ω=ω0 represent two characteristic function points, and sign(·) represents the sign function. α is the characteristic index, and its value range is 0<α≤2. β is the skew parameter, which determines the degree of skewness of the distribution, and its value range is -1≤β≤1. γ is the scale parameter, also known as the dispersion coefficient and deviation, which describes the degree to which the stable distribution random variable deviates from the mean or median. γ can be any positive number, similar to the variance in the Gaussian distribution. δ is the location parameter, which determines the location of the peak of the probability density function of the α stable distribution, and its value range is -∞<δ<∞. S32, estimating the expressions of α characteristic parameter and γ scale parameter, such as formula; Where N is the length of the observation sample X, X = {x1, x2, x3, … x i ,…,x N }; ω0 is closely related to the accuracy of parameter estimation in the characteristic function of stable distribution, and its value range is 0<ω0<0.5 and 1<ω0<3. The estimation accuracy of the statistical characteristics of underwater noise in wind farms with different value ranges of ω0 is analyzed; if α∈(0,2] is set, the key parameters of the input stable distribution data are simulated. S33. The simulation scale parameter γ value range is set in the interval (0.0001, 0.001).

4. The method for estimating the α-stable distribution of the distance characteristics of underwater noise in a wind farm according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41, set α∈(0,2], γ∈(0.0001,0.001) and estimate based on the method based on semi-probability scale parameters; S42. Combined with the simulation data for comparison, the new parameter estimation algorithm is as follows: H(·) is a piecewise function. When α∈(1,2], the function value is 0. At this time, x i :P(X|>x i )=0.5; when α∈(0,1], the function value is 1.

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