Wind power plant wind power resource evaluation method and system

Abstract

The invention provides a wind power plant wind power resource evaluation method and system, relates to the technical field of wind power resource evaluation, and aims to realize higher-reliability and more comprehensive wind power resource evaluation. The method comprises the following steps: performing wind power data acquisition at a plurality of time points in the detection time of a wind power plant, obtaining a wind power initial data set; preprocessing the wind power initial data set; performing data analysis on the wind power initial data set to obtain a wind power characteristic data set which comprises regional wind speed probability distribution, regional wind direction probability distribution and wind energy density distribution at a plurality of time points; and performing wind power resource quality evaluation based on the wind power characteristic data set. The method has the advantage that the resource evaluation of the wind power of the wind power plant is more efficient, comprehensive and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of wind resource assessment, and in particular, to a method and system for evaluating wind resources in a wind farm. Background Art

[0002] Wind resources are the core foundation for the operation of a wind farm, as the power generation efficiency and economic benefits of a wind farm are closely related to the quality of the wind resources.

[0003] The quality of wind resources depends on parameters such as wind speed and wind direction in the region. Based on these characteristics, a scientific evaluation of wind resources is particularly important. Traditional wind resource evaluation methods mainly rely on processing the collected data in combination with historical experience data and statistical models. To a certain extent, this evaluation method can reflect the situation of wind resources, but the analysis dimensions and methods are too single, and it is difficult to form a reliable analysis result.

[0004] Therefore, there is an urgent need to improve the analysis method of wind resources to achieve a more reliable and comprehensive evaluation of wind resources. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for evaluating wind resources in a wind farm, which can achieve a more reliable and comprehensive evaluation of wind resources.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for evaluating wind resources in a wind farm, comprising the following steps:

[0008] Collect wind power data at multiple time points within the detection time of the wind farm. The data types include wind speed and wind direction, and an initial wind power data set is obtained;

[0009] Preprocess the initial wind power data set;

[0010] Perform data analysis on the initial wind power data set to obtain a wind power characteristic data set, including the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points;

[0011] Evaluate the quality of wind resources based on the wind power characteristic data set.

[0012] Preferably, the method for preprocessing the initial wind power data set includes:

[0013] Use a Kalman filter to supplement missing values and modify outliers;

[0014] Align the data of different data types in time.

[0015] Preferably, the method for analyzing the initial wind power data set includes:

[0016] Obtain the regional wind speed probability distribution:

[0017]

[0018] where v represents the wind speed, f(v) represents the probability of the wind speed being v, c represents the distribution scale characteristic value, and k represents the distribution shape characteristic;

[0019] Obtain the regional wind direction probability distribution:

[0020]

[0021] where d(wd i ) represents the probability of the wind direction being wd i , the total number of winds with the wind direction wd detected within the detection time, q i the total number of winds detected within the detection time, wd alli represents the i-th wind direction; i

[0022] Obtain the regional wind energy density distribution:

[0023]

[0024] where p(t) represents the wind energy density at time t, v(t) represents the wind speed at time t, and ρ is the air density.

[0025] Preferably, the method for obtaining the distribution scale characteristic value and the distribution shape characteristic value is:

[0026] Obtain the regional wind speed probability distribution of the national regional scope as the reference regional wind speed probability distribution f′(v):

[0027]

[0028] where f′(v) represents the probability of the wind speed being v in the reference regional wind speed probability distribution, c0 represents the reference distribution scale characteristic value, and k0 represents the reference distribution shape characteristic;

[0029] Obtain the distribution scale characteristic value:

[0030]

[0031] where v 0 max and v ′ min are respectively the maximum wind speed value and the minimum wind speed value of the reference regional wind speed probability distribution, v max ​and v min are respectively the maximum wind speed value and the minimum wind speed value in the initial wind power dataset, and α is a coefficient constant;

[0032] Obtain the distribution shape characteristic value:

[0033]

[0034] where var(v1) represents the variance of the wind speed in the initial wind power dataset, and var(v ref ) represents the variance of the wind speed in the wind speed probability distribution of the reference area.

[0035] Preferably, the value range of the coefficient constant is:

[0036] 2.2 ≤ α ≤ 2.5.

[0037] Preferably, the types of wind directions include east wind, west wind, south wind, north wind, northeast wind, southeast wind, northwest wind, and southwest wind.

[0038] Preferably, the method for evaluating the quality of wind energy resources based on the wind power characteristic dataset is:

[0039] Obtain the wind speed distribution evaluation value x, the wind direction distribution evaluation value y, and the wind energy distribution evaluation value z respectively based on the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points;

[0040] Obtain the comprehensive wind energy resource evaluation value Q according to the wind speed distribution evaluation value x, the wind direction distribution evaluation value y, and the wind energy distribution evaluation value z:

[0041] Q = β × x + γ × y + δ × z;

[0042] where β, γ, and δ represent weights respectively.

[0043] Preferably, the method for obtaining the wind speed distribution evaluation value x is:

[0044]

[0045] where v m is the set lower wind speed limit, v n is the set upper wind speed limit, x0 is the preset threshold of the wind speed distribution evaluation value, and f(v) is the probability that the regional wind speed probability distribution represents the wind speed of v;

[0046] The method for obtaining the wind direction distribution evaluation value y is:

[0047] y = var[d(wd i )] / y0;

[0048] where d(wdi ) is the probability that the representative wind direction of the wind direction probability distribution in the region is wd i , where wd i represents the i-th wind direction, var represents the function for calculating variance, and y0 is the preset threshold for evaluating the wind direction distribution;

[0049] The method for obtaining the wind energy distribution evaluation value z is as follows:

[0050]

[0051] where p(t) is the wind energy density representing time t in the wind energy density distribution in the region, t0 and t n respectively represent the starting time point and the ending time point for collecting the wind power data, N represents the total number of time points, and z0 is the preset threshold for evaluating the wind energy distribution.

[0052] The present invention also provides a wind farm wind power resource evaluation system, which is applied to the above-mentioned wind farm wind power resource evaluation method, and includes:

[0053] A data collection module, configured to collect wind power data at multiple time points within the detection time of the wind farm, and the data types include wind speed and wind direction, so as to obtain an initial wind power data set;

[0054] A preprocessing module, configured to preprocess the initial wind power data set;

[0055] A data analysis module, configured to perform data analysis on the initial wind power data set to obtain a wind power characteristic data set, including the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points;

[0056] An evaluation module, configured to perform wind power resource quality evaluation based on the wind power characteristic data set.

[0057] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0058] By continuously collecting and analyzing various data such as wind speed and wind direction, the present invention establishes a characteristic data set of regional wind power resources, covering various characteristics such as wind speed probability distribution, wind direction probability distribution, and wind energy density distribution, and can comprehensively reflect the wind power resource quality of the wind farm;

[0059] By preprocessing the initial wind power data set, the method of the present invention effectively filters out noise data and abnormal data, and improves the robustness and adaptability of the model;

[0060] When obtaining the wind speed probability distribution, the present invention adjusts the parameters of the model based on the data characteristics of the wind farm as the acquisition object on the basis of the traditional model, which can express the wind speed characteristics of the wind farm more objectively and accurately, and helps to improve the quality of wind resource evaluation;

[0061] The evaluation values of wind speed distribution, wind direction distribution and wind energy distribution obtained by the present invention based on the characteristic data set reflect the characteristics of the core indicators of wind resources more systematically from a multi-dimensional perspective, which helps to grasp the overall quality of regional wind resources from a global perspective;

[0062] The present invention constructs a comprehensive and reliable wind resource evaluation method, which significantly improves the evaluation efficiency and reliability, and provides important support for the efficient development and utilization of wind energy resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic flow chart of the wind farm wind resource evaluation method provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0065] Embodiment 1

[0066] This embodiment provides a wind farm wind resource evaluation method. Refer to Figure 1 , including the following steps:

[0067] Collect wind power data at multiple time points within the detection time of the wind farm. The data types include wind speed and wind direction to obtain an initial wind power data set;

[0068] Preprocess the initial wind power data set;

[0069] Perform data analysis on the initial wind power data set to obtain a wind power characteristic data set, including regional wind speed probability distribution, regional wind direction probability distribution and wind energy density distribution at multiple time points;

[0070] Perform wind resource quality assessment based on the wind power characteristic data set.

[0071] In this embodiment, continuous collection and in-depth analysis of various wind force data such as wind speed and wind direction are carried out. On this basis, a wind power resource characteristic data set that can reflect the wind force data characteristics of a wind farm is established. This data set contains information such as wind speed probability distribution, wind direction probability distribution, and wind energy density distribution, and can comprehensively reflect the quality status of the wind power resources of the wind farm from multiple dimensions. The solution of this embodiment improves the accuracy, comprehensiveness, and reliability of wind power resource evaluation.

[0072] In this embodiment, the method for preprocessing the initial wind force data set includes:

[0073] Using a Kalman filter to supplement missing values and modify outliers;

[0074] Aligning the data of different data types in time.

[0075] Based on the preprocessing scheme of this embodiment, noise data and abnormal data in the collection process are effectively filtered, making the processed data more reliable and standardized.

[0076] As a preferred scheme of this embodiment, the method for data analysis of the initial wind force data set includes:

[0077] Obtaining the wind speed probability distribution of the area:

[0078]

[0079] Where v represents wind speed, f(v) represents the probability of wind speed v, c represents the distribution scale characteristic value, and k represents the distribution shape characteristic;

[0080] Obtaining the wind direction probability distribution of the area:

[0081]

[0082] Where d(wd i ) represents the probability of wind direction wd i , The total number of winds with wind direction wd detected within the detection time, q i The total number of winds detected within the detection time, wd all represents the i-th wind direction; i represents the i-th wind direction;

[0083] Obtaining the wind energy density distribution of the area:

[0084]

[0085] Where p(t) represents the wind energy density at time t, v(t) represents the wind speed at time t, and ρ is the air density.

[0086] Furthermore, the method for obtaining the distribution scale eigenvalue and the distribution shape eigenvalue is as follows:

[0087] Obtain the regional wind speed probability distribution within the national regional scope as the reference regional wind speed probability distribution f′(v):

[0088]

[0089] where f′(v) represents the probability that the wind speed is v in the reference regional wind speed probability distribution, c0 represents the reference distribution scale eigenvalue, and k0 represents the reference distribution shape feature;

[0090] Obtain the distribution scale eigenvalue:

[0091]

[0092] where v′ max and v′ min are respectively the maximum wind speed value and the minimum wind speed value of the reference regional wind speed probability distribution, v max and v min are respectively the maximum wind speed value and the minimum wind speed value in the initial wind power dataset, and α is a coefficient constant;

[0093] Obtain the distribution shape eigenvalue:

[0094]

[0095] where var(v1) represents the variance of the wind speed in the initial wind power dataset, and var(v ref ) represents the variance of the wind speed in the reference regional wind speed probability distribution.

[0096] On this basis, the value range of the coefficient constant is:

[0097] 2.2 ≤ α ≤ 2.5.

[0098] In the above solution, the Weilbull distribution model is adopted to describe the regional wind speed probability distribution, and the distribution scale eigenvalue c and the distribution shape eigenvalue k therein can respectively control the range and steepness of the distribution. In this embodiment, a regional wind speed probability distribution within the national regional scope is used as the reference regional wind speed probability distribution to set the distribution scale eigenvalue v and the distribution shape eigenvalue k for a specific target wind farm. The reason for choosing the data within the national regional scope as the reference is that its wind speed distribution will be more comprehensive, with more extensive data in all dimensions. Using this as a benchmark to find parameters for local data has higher reliability. The ratio of the data of the wind farm to the data of the national scope used as the reference and It is denoted as the characteristic ratio. The characteristic ratio of the data coverage range of the wind farm needs to be closer to 1 compared to the reference data, c is closer to c0, k approaches k0, and the smaller the characteristic ratio, the smaller the distribution scale eigenvalue c and the distribution shape characteristic k. The coefficient constant α can adjust to what extent the data coverage range of the wind farm is close to the coverage range of the reference data before the regional wind speed probability distribution of the wind farm will be close to the reference regional wind speed probability distribution. For example, the smaller the value of α, the closer the characteristic ratio needs to be to 1 when c is closer to c0 and k approaches k0.

[0099] Meanwhile, the types of the wind direction preferably include east wind, west wind, south wind, north wind, northeast wind, southeast wind, northwest wind and southwest wind.

[0100] Next, the method for evaluating the quality of wind energy resources based on the wind power characteristic data set is as follows:

[0101] Based on the regional wind speed probability distribution, the regional wind direction probability distribution and the wind energy density distribution at multiple time points, the wind speed distribution evaluation value x, the wind direction distribution evaluation value y and the wind energy distribution evaluation value z are respectively obtained;

[0102] According to the wind speed distribution evaluation value x, the wind direction distribution evaluation value y and the wind energy distribution evaluation value z, the comprehensive evaluation value Q of the wind energy resources is obtained:

[0103] Q = β × x + γ × y + δ × z;

[0104] Among them, β, γ and δ respectively represent weights.

[0105] The above weights represent the importance degree of each parameter, and the sum of the three weights is 1. The specific values can be determined according to the requirements of the wind farm. By default, the values of β, γ and δ can be set to 1 / 3.

[0106] Specifically, the method for obtaining the wind speed distribution evaluation value x is as follows:

[0107]

[0108] Among them, v m is the set lower limit of the wind speed, v n is the set upper limit of the wind speed, x0 is the preset threshold of the wind speed distribution evaluation value, and f(v) is the probability that the regional wind speed probability distribution represents the wind speed of v;

[0109] The method for obtaining the wind direction distribution evaluation value y is as follows:

[0110] y = var[d(wd i )] / y0;

[0111] Among them, d(wd i ) is the probability that the regional wind direction probability distribution represents the wind direction of wdi probability, wd i represents the i-th wind direction, var represents the function for calculating variance, and y0 is the preset threshold for wind direction distribution evaluation value;

[0112] The method for obtaining the wind energy distribution evaluation value z is as follows:

[0113]

[0114] where p(t) is the wind energy density representing time t in the wind energy density distribution of the region, t0 and t n respectively represent the starting time point and the ending time point for collecting the wind power data, N represents the total number of time points, and z0 is the preset threshold for wind energy distribution evaluation value.

[0115] In the calculation of this embodiment, the lower wind speed limit v m and the upper wind speed limit v n are the ideal wind speed ranges for a wind farm. Therefore, the wind speed distribution evaluation value calculates the total probability of wind speed within an ideal wind speed section. The wind direction distribution evaluation value evaluates the non-randomness of the wind direction, while the wind energy distribution evaluation value evaluates the wind power persistence within the wind farm. Based on these parameters, this embodiment significantly improves the efficiency and reliability of wind power resource evaluation through comprehensive data collection and accurate feature extraction.

[0116] Embodiment 2

[0117] This embodiment provides a wind farm wind power resource evaluation system, which is applied to a wind farm wind power resource evaluation method in Embodiment 1 and includes:

[0118] A data collection module, configured to collect wind power data at multiple time points within the detection time of the wind farm. The data types include wind speed and wind direction, and an initial wind power data set is obtained;

[0119] A preprocessing module, configured to preprocess the initial wind power data set;

[0120] A data analysis module, configured to perform data analysis on the initial wind power data set to obtain a wind power feature data set, including the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points;

[0121] An evaluation module, configured to perform wind power resource quality evaluation based on the wind power feature data set.

[0122] The working principle and specific implementation details of this embodiment are the same as those of the wind farm wind power resource evaluation method in Embodiment 1.

[0123] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for evaluating wind power resources in a wind farm, characterized in that, It includes the following steps: Collect wind power data at multiple time points within the detection time of the wind farm. The data types include wind speed and wind direction, and an initial wind power dataset is obtained; Preprocess the initial wind power dataset; Perform data analysis on the initial wind power dataset to obtain a wind power feature dataset, including the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points; Conduct a wind power resource quality assessment based on the wind power feature dataset.

2. The method for evaluating wind power resources in a wind farm according to claim 1, wherein The method for preprocessing the initial wind power dataset includes: Use a Kalman filter to supplement missing values and modify outliers; Align the data of different data types in time.

3. The method for evaluating wind power resources in a wind farm according to claim 1, characterized in that The method for performing data analysis on the initial wind power dataset includes: Obtain the regional wind speed probability distribution: Where, v represents the wind speed, f(v) represents the probability of the wind speed being v, c represents the distribution scale characteristic value, and k represents the distribution shape characteristic; Obtain the regional wind direction probability distribution: where d(wd i ) represents the probability that the wind direction is wd i , the total number of winds detected with wind direction wd i during the detection time, q all the total number of winds detected during the detection time, wd i represents the i-th wind direction; Obtain the regional wind energy density distribution: Where, p(t) represents the wind energy density at time t, v(t) represents the wind speed at time t, and ρ is the air density.

4. The method for evaluating wind power resources in a wind farm according to claim 3, characterized in that, The method for obtaining the distribution scale characteristic value and the distribution shape characteristic value is: Obtain the regional wind speed probability distribution of the national regional scope as the reference regional wind speed probability distribution f′(v): Where, f′(v) represents the probability of the wind speed being v in the reference regional wind speed probability distribution, c0 represents the reference distribution scale characteristic value, and k0 represents the reference distribution shape characteristic; Obtain the distribution scale characteristic value: Among them, v 0 max and v ′ min are the maximum wind speed value and the minimum wind speed value of the wind speed probability distribution in the reference area respectively, v max and v min are the maximum wind speed value and the minimum wind speed value in the initial wind power dataset respectively, and α is a coefficient constant; Obtain the distribution shape characteristic value: Among them, var(v1) represents the variance of the wind speed of the initial wind power data set, and var(v ref ) represents the variance of the wind speed of the wind speed probability distribution in the reference area.

5. A method for evaluating wind power resources in a wind farm according to claim 4, characterized in that, The value range of the coefficient constant is: 2.2≤α≤2.5； Where, α is the coefficient constant.

6. The method for evaluating wind power resources in a wind farm according to claim 1, wherein The types of the wind direction include east wind, west wind, south wind, north wind, northeast wind, southeast wind, northwest wind, and southwest wind.

7. A method for evaluating wind power resources in a wind farm according to claim 1, characterized in that, The method for conducting a wind power resource quality assessment based on the wind power feature dataset is: Respectively obtain the wind speed distribution evaluation value x, the wind direction distribution evaluation value y, and the wind energy distribution evaluation value z based on the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points; Obtain the comprehensive wind power resource evaluation value Q according to the wind speed distribution evaluation value x, the wind direction distribution evaluation value y, and the wind energy distribution evaluation value z: Q = β×x + γ×y + δ×z; Where, β, γ, and δ respectively represent weights.

8. A method for evaluating wind power resources in a wind farm according to claim 7, characterized in that, The method for obtaining the wind speed distribution evaluation value x is: Among them, v m is the set lower limit of the wind speed, v n is the set upper limit of the wind speed, x0 is the preset threshold value of the wind speed distribution evaluation value, and f(v) is the probability that the representative wind speed of the wind speed probability distribution in the area is v; The method for obtaining the wind direction distribution evaluation value y is: y = var[d(wd i )] / y0; where d(wd i ) is the probability that the representative wind direction of the wind direction probability distribution in the region is wd i , wd i represents the i-th wind direction, var represents the function for calculating variance, and y0 is the preset threshold for evaluating the wind direction distribution; The method for obtaining the wind energy distribution evaluation value z is: where p(t) is the wind energy density representing the wind energy density at time t, t0 and t n represent the start time point and the end time point for collecting the wind power data respectively, N represents the total number of time points, and z0 is a preset threshold value for evaluating the wind energy distribution.

9. A wind farm wind power resource evaluation system, applied to a wind farm wind power resource evaluation method according to any one of claims 1-8, characterized in that, It includes: A data collection module, which is used to collect wind power data at multiple time points within the detection time of the wind farm. The data types include wind speed and wind direction, and an initial wind power dataset is obtained; A preprocessing module, which is used to preprocess the initial wind power dataset; A data analysis module, which is used to perform data analysis on the initial wind power dataset to obtain a wind power feature dataset, including the regional wind speed probability distribution, the regional wind direction probability distribution, and the wind energy density distribution at multiple time points; An evaluation module, which is used to conduct a wind power resource quality assessment based on the wind power feature dataset.

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