Wind power plant power adjustment method and device, equipment and storage medium
By generating wind speed time-series data using the Kemal model and Gaussian approximation, the power curve of the wind turbine was corrected, solving the problem of accurate prediction of wind turbine output power under turbulent conditions and improving the power generation efficiency of the wind turbine.
Patent Information
- Application Number
- CN202511775378.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-03
AI Technical Summary
Under turbulent conditions, existing technologies struggle to accurately predict the output power of wind turbine generators, resulting in significant nonlinear characteristics in the power curve.
The Kemal model is used to generate wind speed time series data. The conditional probability density function is obtained through Gaussian approximation. The known calibration power curve is then convolved to generate a corrected power curve under turbulent intensity, which is used to adjust the output power of the wind turbine.
It improves the accuracy and robustness of wind turbine output power adjustment under turbulent conditions, reduces power prediction errors, and optimizes the power generation of wind turbines.
Smart Images
Figure CN121602502A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of wind power generation technology, and in particular to a method, apparatus, equipment and storage medium for adjusting the power of a wind farm. Background Technology
[0002] In the performance evaluation and power prediction of wind turbine generators, the power curve is a core indicator reflecting the wind turbine's wind energy capture efficiency and grid-connected power characteristics. Under ideal conditions, there is a clear theoretical correlation between the power of a wind turbine generator and wind speed. However, in actual operation, wind turbine generators are affected by random fluctuations in wind speed caused by turbulence, resulting in significant nonlinear characteristics in the power curve. Therefore, accurately predicting the output power of wind turbine generators under turbulent conditions is a pressing technical problem that needs to be solved. Summary of the Invention
[0003] In view of this, the present disclosure provides a method, apparatus, device and storage medium for adjusting the power of a wind farm.
[0004] According to a first aspect of this disclosure, a wind farm power adjustment method is provided, the wind farm power adjustment method comprising: Based on the average wind speed, turbulence intensity, and scale parameters of the target operating condition, the Kaimal model is used to generate time-series wind speed data for the target operating condition. The conditional probability density function satisfied by the wind speed time series data is obtained by Gaussian approximation; Based on the conditional probability density function satisfied by the wind speed time series data, the known calibration power curve is convolved and corrected to obtain the corrected power curve under the turbulence intensity. The calibration power curve indicates the relationship between the wind turbine output power and wind speed when the turbulence intensity is 0, and the corrected power curve indicates the relationship between the wind turbine output power and wind speed under the turbulence intensity. The wind turbine output power under the turbulence intensity is adjusted based on the modified power curve and real-time wind speed.
[0005] In some embodiments of the first aspect of this disclosure, the wind speed time-series data of the target operating condition satisfies the following formula:
[0006]
[0007] in, This represents the average wind speed. Indicates turbulence intensity. This represents wind speed time series data. This represents the standard deviation of longitudinal pulsating wind speed.
[0008] In some embodiments of the first aspect of this disclosure, the conditional probability density function satisfied by the wind speed time series data is obtained through Gaussian approximation, including: The conditional probability density function is obtained based on the following formula:
[0009] in, This represents the average wind speed. Indicates turbulence intensity. This represents the instantaneous wind speed in the wind speed time series data. Let be the standard deviation parameter of the normal distribution. It is the conditional probability density function; Instantaneous wind speed Satisfy the following formula:
[0010] in, This represents the average wind speed. This indicates the intensity of turbulence.
[0011] In some embodiments of the first aspect of this disclosure, the step of convolving and correcting the known calibrated power curve based on the conditional probability density function satisfied by the wind speed time series data to obtain the corrected power curve under the turbulence intensity includes: Within the cut-in / cut-out limit range [ Convolution correction is performed within the data, the convolution correction including: discretizing the wind speed intervals of the wind speed time series data into... The corresponding calibration power curve For each instantaneous wind speed , Using the following formula The instantaneous wind speed is obtained by approximating the integral using several nearby wind speed points. ; output power;
[0012]
[0013]
[0014]
[0015] in, This represents the i-th instantaneous wind speed in the wind speed time series data. Indicates wind speed The output power of the wind turbine unit below Wind speed The neighborhood set, This represents a dynamic neighborhood that varies with turbulence intensity and instantaneous wind speed. This represents the neighborhood width coefficient, used to control the degree of smoothness. Indicates weight, Indicates the cut-in wind speed. This indicates the cut-out wind speed.
[0016] In some embodiments of the first aspect of this disclosure, the method further includes: For the instantaneous wind speeds in the wind speed time series data that are lower than the cut-in wind speed and higher than the cut-out wind speed, the output power of the wind turbine at the instantaneous wind speed is determined by the following formula;
[0017] in, This represents the instantaneous wind speed in the wind speed time series data. The output power of the wind turbine unit below Indicates the cut-in wind speed. This indicates the cut-out wind speed.
[0018] In some embodiments of the first aspect of this disclosure, the method further includes: for cases with multiple turbulence intensities, performing the following processing on each of the pre-set multiple turbulence intensities to obtain multiple corrected power curves: generating wind speed time-series data for the target operating condition under the turbulence intensities; obtaining the conditional probability density function satisfied by the wind speed time-series data under the turbulence intensities through Gaussian approximation; and performing convolution correction on the known calibration power curves based on the conditional probability density function satisfied by the wind speed time-series data under the turbulence intensities to obtain the corrected power curves under the turbulence intensities; wherein each of the multiple corrected power curves corresponds one-to-one with the multiple turbulence intensities.
[0019] According to a second aspect of this disclosure, a wind farm power regulation device is provided, the wind farm power regulation device comprising: The wind speed data generation unit is used to generate time-series wind speed data for the target operating condition based on the average wind speed, turbulence intensity, and scale parameters of the target operating condition using the Kaimal model. A Gaussian approximation unit is used to obtain the conditional probability density function satisfied by the wind speed time series data through Gaussian approximation. The convolution correction unit is used to perform convolution correction on the known calibration power curve based on the conditional probability density function satisfied by the wind speed time series data to obtain the corrected power curve under the turbulence intensity. The calibration power curve indicates the relationship between the wind turbine output power and the wind speed when the turbulence intensity is 0, and the corrected power curve indicates the relationship between the wind turbine output power and the wind speed under the turbulence intensity. A power adjustment unit is used to adjust the output power of the wind turbine under the turbulence intensity based on the modified power curve and the real-time wind speed.
[0020] In some embodiments of the second aspect of this disclosure, the convolution correction unit is specifically used for: within the cut-in / cut-out constraint range [ Convolution correction is performed within the data, the convolution correction including: discretizing the wind speed intervals of the wind speed time series data into... The corresponding calibration power curve For each instantaneous wind speed , Using the following formula The instantaneous wind speed is obtained by approximating the integral using several nearby wind speed points. ; output power;
[0021]
[0022]
[0023]
[0024] in, This represents the i-th instantaneous wind speed in the wind speed time series data. Indicates wind speed The output power of the wind turbine unit below Wind speed The neighborhood set, This represents a dynamic neighborhood that varies with turbulence intensity and instantaneous wind speed. This represents the neighborhood width coefficient, used to control the degree of smoothness. Indicates weight, Indicates the cut-in wind speed. This indicates the cut-out wind speed.
[0025] According to a third aspect of this disclosure, an electronic device is provided, the electronic device including a processor and a memory, the memory storing a computer program, the computer program causing the processor to perform the wind farm power adjustment method described above when executed by the processor.
[0026] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the wind farm power adjustment method described above.
[0027] The embodiments disclosed herein quantify the impact of turbulence intensity on power generation by correcting the power curve, thereby improving the accuracy of adjusting the output power of wind turbines under turbulent conditions. Attached Figure Description
[0028] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0029] Figure 1 A schematic flowchart of a wind farm power adjustment method provided in an embodiment of this disclosure; Figure 2 Examples of calibration power curves, weights, and corrected power curves involved in embodiments of this disclosure are shown in (a) a visualization of a known calibration power curve, and (b) a diagram of the weights. (c) is a visualization of the power curve after correction with a turbulence intensity of 10%. Figure 3 This is a schematic diagram of the structure of the wind farm power adjustment device provided in the embodiments of this disclosure; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation
[0030] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0031] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure. The singular forms “a,” “the,” and “the” as used in the embodiments of this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0032] Depending on the context, words such as "if," "when," etc., used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrases "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."
[0033] Figure 1 A schematic flowchart of a wind farm power adjustment method provided in an embodiment of this disclosure is shown. See also... Figure 1The wind farm power adjustment method of this disclosure embodiment may include the following steps: Step 101: Based on the average wind speed, turbulence intensity, and scale parameters of the target operating condition, generate the wind speed time series data of the target operating condition using the Kaimal model (The Kaimal Wind Velocity Spectrum Model); Step 102: Obtain the conditional probability density function satisfied by the wind speed time series data through Gaussian approximation; Step 103: Based on the conditional probability density function satisfied by the wind speed time series data, the known calibration power curve is convolved and corrected to obtain the corrected power curve under turbulence intensity. The calibration power curve indicates the relationship between the wind turbine output power and wind speed when the turbulence intensity is 0, and the corrected power curve indicates the relationship between the wind turbine output power and wind speed under turbulence intensity. Step 104: Adjust the wind turbine output power under turbulence intensity based on the modified power curve and real-time wind speed.
[0034] The Kaimal model is a wind speed spectrum model primarily used to describe the turbulent characteristics of the longitudinal wind speed component in the atmospheric boundary layer. Using the Kaimal spectrum, artificial wind speed time series that conform to the statistical characteristics of real atmospheric turbulence can be generated. The parameters of the Kaimal model include turbulence intensity (TI), mean wind speed, and scale parameters, including the turbulence integral scale.
[0035] The wind speed time series data obtained in step 101 satisfy the following equations (1) and (2).
[0036] (1) (2) in, This represents the average wind speed. Indicates turbulence intensity. This represents wind speed time series data. The standard deviation of longitudinal pulsating wind speed. It is an indicator that measures the degree to which wind speed fluctuates around its average value. The larger the value, the more drastic the instantaneous wind speed changes in the wind speed time series data.
[0037] For a given average wind speed The actual instantaneous wind speed fluctuates according to a Gaussian distribution in its vicinity. That is, the instantaneous wind speed... It follows a Gaussian distribution. Specifically, within a short timescale (e.g., 10 minutes), the instantaneous wind speed... Obey the average wind speed As the mean, with The variance follows a normal distribution. That is, within a short timescale, the instantaneous wind speed... It satisfies the following equation (3).
[0038] (3) in, This represents the instantaneous wind speed in the wind speed time series data obtained in step 101. This represents the average wind speed. This indicates the intensity of turbulence.
[0039] In step 102, the conditional probability density function is obtained by the Gaussian approximation based on the above equation (3) and the following equation (4).
[0040] (4) in, This represents the average wind speed. Indicates turbulence intensity. This represents the instantaneous wind speed in the wind speed time series data obtained in step 101. The standard deviation parameter is a normal distribution parameter, which measures the absolute fluctuation range of wind speed. Under the same turbulence intensity, the greater the average wind speed, the higher the wind speed. The larger the value, the greater the absolute fluctuation range of wind speed. Let be the conditional probability density function, representing the probability density function under a given average wind speed. Instantaneous wind speed under the condition The probability density.
[0041] Further, step 103 includes: within the cut-in / cut-out restriction range [ Perform convolution correction.
[0042] Under the condition of average wind speed U, the output power of the wind turbine satisfies the following equation (5): (5) in, With U as the mean, This represents the expected output power. Indicates the calibration power curve. Indicates instantaneous wind speed.
[0043] Therefore, within the cut-in and cut-out limits [ ] within, that is In this case, the convolution correction in step 103 may include: discretizing the wind speed range into... The corresponding calibration power curve For each , Using the following formulas (6) to (9) The output power at each instantaneous wind speed is obtained by approximating the integral at several nearby wind speed points.
[0044] (6) (7) (8) (9) in, This represents the i-th instantaneous wind speed in the wind speed time series data. Indicates wind speed The output power of the wind turbine unit below Wind speed The neighborhood set of contains indices that satisfy the conditions shown in equation (7). In other words, all wind speeds It is in the wind speed Data points within a nearby interval, which is composed of Decide. This represents a dynamic neighborhood that varies with turbulence intensity and instantaneous wind speed. This represents the neighborhood width coefficient, used to control the smoothness. A value of k = 5 indicates the use of ±5. A wide window. Indicates the weight.
[0045] As can be seen from the above, within the cut-in / cut-out limit [ ] within, that is At that time, within a sliding window determined by turbulence intensity and instantaneous wind speed, a weighted average is performed based on the proximity of the wind speed point to the instantaneous wind speed, thereby obtaining a smoothed corrected power curve.
[0046] Furthermore, step 103 may also include: for the instantaneous wind speed in the wind speed time series data that is lower than the cut-in wind speed and higher than the cut-out wind speed, the output power of the wind turbine under the instantaneous wind speed can be determined by the following formula (10).
[0047] (10) in, Instantaneous wind speed in wind speed time series data The output power of the wind turbine unit below Indicates the cut-in wind speed. This indicates the cut-out wind speed.
[0048] Furthermore, step 103 may further include: for cases with multiple turbulence intensities, performing the following processing on each of the pre-set multiple turbulence intensities to obtain multiple corrected power curves: generating wind speed time-series data for the target operating condition under the turbulence intensity; obtaining the conditional probability density function satisfied by the wind speed time-series data under the turbulence intensity through Gaussian approximation; performing convolution correction on the known calibration power curve based on the conditional probability density function satisfied by the wind speed time-series data under the turbulence intensity to obtain the corrected power curve under the turbulence intensity; wherein, each of the multiple corrected power curves corresponds one-to-one with the multiple turbulence intensities.
[0049] For example, for various turbulence intensities TI ranging from 3% to 30%, steps 101 to 103 can be repeated to obtain multiple corrected power curves. Each corrected power curve Corresponding to a certain turbulence intensity , The values are 3%, 5%, 10%, 15%, 20%, 25%, and 30%.
[0050] Figure 2 Example diagrams of the calibration power curve, Gaussian smoothing kernel, and corrected power curve related to embodiments of this disclosure are shown. See also Figure 2 (a) is the power curve with 0% turbulence intensity, which is also a visualization of the known calibration power curve; (b) is the weighted power curve. (c) is a visualization of the power curve after correction with a turbulence intensity of 10%.
[0051] For various turbulence intensities, when the turbulence intensity is low, the power curve correction is minimal, and the corrected power curve is close to the original power curve. When the turbulence intensity is high, the corrected power curve, compared to the calibrated power curve, shows a certain increase in power in the low wind speed range and a certain decrease in the high wind speed range (especially near the rated power, cut-off, etc.). This is consistent with the actual impact of turbulence intensity on the output power of wind turbines.
[0052] The actual output power data of a 5MW flexible blade wind turbine in a wind farm under various turbulence intensities of 3% to 21% (i.e., TI=3%, TI=9%, TI=12%, TI=21%) were compared with the corrected power curves obtained by the method of this embodiment of the present disclosure when the turbulence intensities are 3%-21%. It was found that the error decreased from an average of 8% to 3%. It can be seen that the processing by the method of this embodiment of the present disclosure can effectively improve the accuracy of the power curve representing the relationship between the output power of the wind turbine and the wind speed under turbulent conditions.
[0053] Furthermore, verification showed that when the turbulence intensity increased from 5% to 21%, the trend of the wind turbine output power indicated by the corrected power curve obtained by the method of this disclosure increasing in the low wind speed region and decreasing in the high wind speed region was consistent with the experimental results. It is evident that the processing by the method of this disclosure can effectively improve the robustness and reliability of the power curve representing the relationship between wind turbine output power and wind speed under turbulent conditions.
[0054] In step 104, the output power of the wind turbine can be adjusted under a specific turbulence intensity based on the corrected power curve obtained in step 103 and the real-time wind speed, so as to optimize the output performance of the wind turbine and increase its power generation.
[0055] This disclosure assumes that the turbulent wind speed distribution follows a Gaussian distribution to calculate the convolution response of the power curve under different turbulence intensities, thereby dynamically correcting the power curve according to the turbulence intensity, especially in the wind speed variation range (such as near the rated wind speed). Furthermore, this disclosure also proposes a power weighted average from the cut-in wind speed to the cut-out wind speed, which can take into account different environmental conditions and is applicable to various scenarios.
[0056] The embodiments disclosed herein can realize the prediction and adjustment of wind turbine output power under turbulent conditions, and can be applied to, but not limited to, wind farm power curve optimization prediction, wind turbine power fluctuation monitoring, and wind power output performance evaluation under turbulent conditions.
[0057] Figure 3 A schematic diagram of the wind farm power regulation device provided in an embodiment of this disclosure is shown. See also... Figure 3 The wind farm power adjustment device in this embodiment may include: The wind speed data generation unit 301 is used to generate time series wind speed data for the target operating condition based on the average wind speed, turbulence intensity and scale parameters of the target operating condition using the Kaimal model. Gaussian approximation unit 302 is used to obtain the conditional probability density function satisfied by the wind speed time series data through Gaussian approximation; The convolution correction unit 303 is used to perform convolution correction on the known calibration power curve based on the conditional probability density function satisfied by the wind speed time series data to obtain the corrected power curve under the turbulence intensity. The calibration power curve indicates the relationship between the wind turbine output power and the wind speed when the turbulence intensity is 0, and the corrected power curve indicates the relationship between the wind turbine output power and the wind speed under the turbulence intensity. The power adjustment unit 304 is used to adjust the output power of the wind turbine under the turbulence intensity based on the modified power curve and the real-time wind speed.
[0058] Furthermore, the convolution correction unit 303 is specifically used for: within the cut-in / cut-out constraint range [ Convolution correction is performed within the data, the convolution correction including: discretizing the wind speed intervals of the wind speed time series data into... The corresponding calibration power curve For each instantaneous wind speed , Using the above equations (6) to (9) The instantaneous wind speed is obtained by approximating the integral using several nearby wind speed points. . output power.
[0059] Furthermore, the wind speed data generation unit 301 is specifically used to obtain wind speed time series data that satisfy equations (1) to (2).
[0060] Furthermore, the Gaussian approximation unit 302 can be used to obtain the conditional probability density function satisfied by the wind speed time series data based on equations (3) to (4).
[0061] Furthermore, the convolution correction unit 303 can also be used to: determine the output power of the wind turbine at the instantaneous wind speed that is lower than the cut-in wind speed and higher than the cut-out wind speed in the wind speed time series data by formula (10).
[0062] The wind farm power adjustment device can also be used for various turbulence intensities. For each of the multiple turbulence intensities preset, multiple corrected power curves are obtained, and each of the multiple corrected power curves corresponds one-to-one with the turbulence intensities among the multiple turbulence intensities.
[0063] Specific technical details of each unit in the wind farm power regulation device can be found in the aforementioned method section and will not be repeated here. In practical applications, the wind farm power regulation device can be implemented as software, hardware, or a combination of both. In some examples, the wind farm power regulation device can be implemented as follows: Figure 4 The electronic device shown or the software deployed in the electronic device.
[0064] Figure 4 A schematic structural diagram of an electronic device provided according to an embodiment of this disclosure is shown. See also... Figure 4 The electronic device 400 provided in this embodiment may include a processor 401 and a memory 402. The memory 402 stores a computer program, which, when run by the processor 401, causes the processor 401 to execute the aforementioned wind farm power adjustment method.
[0065] The processor 401 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities.
[0066] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the methods of the various embodiments of this disclosure described above and / or other desired functions.
[0067] Depending on the specific application, the electronic device 400 may also include any other suitable components.
[0068] In addition to the methods and devices described above, embodiments of this disclosure may also be computer program products, including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the wind farm power adjustment method of the embodiments of this disclosure.
[0069] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of this disclosure. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0070] Furthermore, embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon, which, when run by a processor, causes the processor to perform the steps in the wind farm power adjustment method of embodiments of this disclosure.
[0071] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
[0072] The technical solutions provided in this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this disclosure. Furthermore, those skilled in the art will recognize that, based on the ideas of this disclosure, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this disclosure.
[0073] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications or equivalent substitutions made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A method for adjusting the power output of a wind farm, characterized in that, The method includes: Based on the average wind speed, turbulence intensity, and scale parameters of the target operating condition, the Kaimal model is used to generate time-series wind speed data for the target operating condition. The conditional probability density function satisfied by the wind speed time series data is obtained by Gaussian approximation; Based on the conditional probability density function satisfied by the wind speed time series data, the known calibration power curve is convolved and corrected to obtain the corrected power curve under the turbulence intensity. The calibration power curve indicates the relationship between the wind turbine output power and wind speed when the turbulence intensity is 0, and the corrected power curve indicates the relationship between the wind turbine output power and wind speed under the turbulence intensity. The wind turbine output power under the turbulence intensity is adjusted based on the modified power curve and real-time wind speed.
2. The method according to claim 1, characterized in that, The wind speed time series data for the target operating condition satisfy the following formula: in, This represents the average wind speed. Indicates turbulence intensity. This represents wind speed time series data. This represents the standard deviation of longitudinal pulsating wind speed.
3. The method according to claim 1, characterized in that, The conditional probability density function satisfied by the wind speed time series data is obtained through Gaussian approximation, including: The conditional probability density function is obtained based on the following formula: in, This represents the average wind speed. Indicates turbulence intensity. This represents the instantaneous wind speed in the wind speed time series data. Let be the standard deviation parameter of the normal distribution. It is the conditional probability density function; Instantaneous wind speed Satisfy the following formula: in, This represents the average wind speed. This indicates the intensity of turbulence.
4. The method according to claim 1, characterized in that, The step of convolving and correcting the known calibrated power curve based on the conditional probability density function satisfied by the wind speed time series data to obtain the corrected power curve under the turbulence intensity includes: Within the cut-in / cut-out limit range [ Convolution correction is performed within the data, the convolution correction including: discretizing the wind speed intervals of the wind speed time series data into... The corresponding calibration power curve For each instantaneous wind speed , Using the following formula The instantaneous wind speed is obtained by approximating the integral using several nearby wind speed points. ; output power; in, This represents the i-th instantaneous wind speed in the wind speed time series data. Indicates wind speed The output power of the wind turbine unit below Wind speed The neighborhood set, This represents a dynamic neighborhood that varies with turbulence intensity and instantaneous wind speed. This represents the neighborhood width coefficient, used to control the degree of smoothness. Indicates weight, Indicates the cut-in wind speed. This indicates the cut-out wind speed.
5. The method according to claim 1, characterized in that, The method further includes: For the instantaneous wind speeds in the wind speed time series data that are lower than the cut-in wind speed and higher than the cut-out wind speed, the output power of the wind turbine at the instantaneous wind speed is determined by the following formula; in, This represents the instantaneous wind speed in the wind speed time series data. The output power of the wind turbine unit below Indicates the cut-in wind speed. This indicates the cut-out wind speed.
6. The method according to claim 1, characterized in that, The method further includes: for multiple turbulence intensities, performing the following processing on each of the pre-set multiple turbulence intensities to obtain multiple corrected power curves: generating wind speed time-series data for the target operating condition under the turbulence intensities; obtaining the conditional probability density function satisfied by the wind speed time-series data under the turbulence intensities through Gaussian approximation; and performing convolution correction on the known calibrated power curves based on the conditional probability density function satisfied by the wind speed time-series data under the turbulence intensities to obtain the corrected power curves under the turbulence intensities. Each of the multiple corrected power curves corresponds one-to-one with one of the multiple turbulence intensities.
7. A wind farm power adjustment device, characterized in that, The wind farm power adjustment device includes: The wind speed data generation unit is used to generate time-series wind speed data for the target operating condition based on the average wind speed, turbulence intensity, and scale parameters of the target operating condition using the Kaimal model. A Gaussian approximation unit is used to obtain the conditional probability density function satisfied by the wind speed time series data through Gaussian approximation. The convolution correction unit is used to perform convolution correction on the known calibration power curve based on the conditional probability density function satisfied by the wind speed time series data to obtain the corrected power curve under the turbulence intensity. The calibration power curve indicates the relationship between the wind turbine output power and the wind speed when the turbulence intensity is 0, and the corrected power curve indicates the relationship between the wind turbine output power and the wind speed under the turbulence intensity. A power adjustment unit is used to adjust the output power of the wind turbine under the turbulence intensity based on the modified power curve and the real-time wind speed.
8. The apparatus according to claim 7, characterized in that, The convolution correction unit is specifically used for: within the cut-in / cut-out constraint range [ Convolution correction is performed within the data, the convolution correction including: discretizing the wind speed intervals of the wind speed time series data into... The corresponding calibration power curve For each instantaneous wind speed , Using the following formula The instantaneous wind speed is obtained by approximating the integral using several nearby wind speed points. ; output power; in, This represents the i-th instantaneous wind speed in the wind speed time series data. Indicates wind speed The output power of the wind turbine unit below Wind speed The neighborhood set, This represents a dynamic neighborhood that varies with turbulence intensity and instantaneous wind speed. This represents the neighborhood width coefficient, used to control the degree of smoothness. Indicates weight, Indicates the cut-in wind speed. This indicates the cut-out wind speed.
9. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, causes the processor to perform the method as described in any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the method as described in any one of claims 1 to 6.