A method and system for estimating on-grid power of a distributed wind farm

Abstract

The application provides a distributed wind farm on-grid power estimation method and system, and belongs to the technical field of power systems, and specifically comprises: determining an abnormal date group in a similar date group by using abnormal conditions; when the number of dates in the abnormal date group does not meet the requirement, determining abnormal time periods of different wind turbines by using abnormal conditions of historical output waveforms of different wind turbines in different time periods in the abnormal date group; determining power generation limiting wind turbines in the abnormal date group by using distributed data of the abnormal time periods; obtaining the number of dates of different abnormal date groups; and determining whether the distributed wind farm can be evaluated in a preset time period in combination with the installed capacity proportion of the power generation limiting wind turbines in different abnormal date groups, so that the accuracy of the estimation processing result is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a distributed wind farm on-grid power estimation method and system. BACKGROUND

[0002] In order to realize the estimation of the on-grid power of the distributed wind farm, the patent application CN116169727A "Old wind farm reconstruction project power generation capacity evaluation method and system without wind data" calculates the power generation capacity correction coefficient of the mesoscale wind data based on the actual power generation capacity of the old wind farm, and corrects the power generation capacity of the reconstructed wind farm, but the above technical solutions have the following technical problems:

[0003] In the evaluation of the on-grid power, the existing technical solutions ignore the abnormal conditions of the output waveform of different wind turbines under different weather conditions, such as high harmonic content or current distortion. If the time period of the abnormal output waveform is not excluded, the on-grid processing of the above time period cannot be normally processed, and thus the accuracy of the estimation result of the on-grid power cannot be guaranteed.

[0004] In view of the above technical problems, the present application provides a distributed wind farm on-grid power estimation method and system. SUMMARY

[0005] To achieve the purpose of the present application, the present application adopts the following technical solutions:

[0006] In the first aspect, the present application provides a distributed wind farm on-grid power estimation method, which specifically comprises:

[0007] S1, based on the weather data of a target area, divides the dates in a preset time period into different similar date groups, and determines the abnormal conditions of the historical output waveform of different wind turbines in the distributed wind farm under different time periods according to the weather data of different similar date groups in different time periods;

[0008] S2, using the abnormal conditions, determines an abnormal date group in the similar date groups, and when the date quantity proportion of the abnormal date group does not meet the requirements, determines the abnormal time period of different wind turbines according to the abnormal conditions of the historical output waveform of different wind turbines in different time periods in the abnormal date group;

[0009] S3, using the distribution data of the abnormal time period to determine the power generation limiting wind turbine in the abnormal date group, obtaining the date quantity proportion of different abnormal date groups, and combining the installed capacity proportion of the power generation limiting wind turbine in different abnormal date groups to determine whether the on-grid power estimation of the distributed wind farm in the preset time period can be performed.

[0010] The present application has the following advantages:

[0011] The abnormal time period of different wind turbines is determined according to the abnormal situation of the historical output waveform of different wind turbines in different time periods in the abnormal date group, the abnormal time period is screened from the abnormal situation of the historical output waveform under similar weather conditions, the technical problem of inaccurate screening result of the abnormal time period caused by single consideration of historical data of a day is avoided, the accuracy of the screening processing of the abnormal time period is improved, and a foundation is laid for differentiating the determination of the power generation limiting wind turbine with high abnormal degree of power generation waveform in the abnormal group.

[0012] Whether the distributed wind power plant can evaluate the on-grid power in the preset time period is determined by the date quantity proportion of different abnormal date groups and the installed capacity proportion of the power generation limiting motor, which not only considers the influence of the estimation deviation of the on-grid power of different abnormal date groups on the overall estimation result caused by the difference in the date quantity proportion, but also considers the difference in the probability of the estimation deviation of the on-grid power caused by the difference in the installed capacity proportion of the power generation limiting motor in different abnormal date groups, so as to ensure the accuracy of the evaluation result of the on-grid power.

[0013] Further, the preset time period is one year, three years or five years.

[0014] Further, the dates in the preset time period are divided into different similar date groups, which specifically includes:

[0015] The dates with the deviation amount of wind speed in different time periods within the preset wind speed deviation amount range are divided into the same similar date group based on the deviation amount of wind speed in different time periods of different dates.

[0016] Further, the abnormal situation of the historical output waveform includes the number of abnormal times of the power generation waveform of the wind turbine under the weather data and the duration of different abnormal times.

[0017] Further, the determination method of the abnormal date group in the similar date group is:

[0018] Based on the abnormal situation, the number of time periods in which the power generation waveform of different wind turbines is abnormal under the weather data is determined.

[0019] The abnormal wind turbine in the wind turbine is determined by the number of time periods in which the power generation waveform is abnormal under the weather data.

[0020] Whether the similar date group is an abnormal date group is determined according to the installed capacity proportion of the abnormal wind turbine.

[0021] The further technical scheme is characterized in that whether the distributed wind farm can perform the evaluation of the on-grid power in the preset time period is determined, and the evaluation specifically includes:

[0022] The proportion of the number of dates in the different abnormal date groups in the number of dates in the preset time period is determined based on the number of dates in the different abnormal date groups, and the weight coefficient of the different abnormal date groups is determined by using the proportion of the number of dates.

[0023] The power generation abnormality coefficient of the different abnormal date groups is determined according to the proportion of the installed capacity of the power generation limiting motor in the different abnormal date groups.

[0024] The output abnormality probability of the distributed wind farm is determined based on the sum of the products of the weight coefficients and the power generation abnormality coefficients of the different abnormal date groups, and whether the distributed wind farm can perform the evaluation of the on-grid power in the preset time period is determined by using the output abnormality probability.

[0025] The further technical scheme is characterized in that when the output abnormality probability is greater than a preset abnormality probability threshold, it is determined that the distributed wind farm cannot perform the evaluation of the on-grid power in the preset time period.

[0026] The further technical scheme is characterized in that when the distributed wind farm can perform the evaluation of the on-grid power in the preset time period, the evaluation of the on-grid power of the distributed wind farm in the preset time period is performed based on the power generation prediction of different wind turbines in different similar date groups.

[0027] In a second aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to perform the distributed wind farm on-grid power estimation method.

[0028] Other features and advantages will be set forth in the accompanying description, and in part will be apparent from the description, or can be learned by practice of the application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described in detail below, and the accompanying drawings are referred to. BRIEF DESCRIPTION OF DRAWINGS

[0030] The above and other features and advantages of the present application will become more apparent by describing in detail example embodiments thereof with reference to the attached drawings.

[0031] Figure 1 is a flowchart of a distributed wind farm on-grid power estimation method;

[0032] Figure 2 is a flowchart of a method of determining an abnormal date group in a similar date group;

[0033] Figure 3 is a flowchart of a method of determining an abnormal time period;

[0034] Figure 4 is a flowchart of a method of determining a power generation limiting wind turbine. DETAILED DESCRIPTION

[0035] Example embodiments now will be described more fully hereinafter with reference to the accompanying drawings. Example embodiments, may, however, be implemented in many different forms and should not be construed as limited to the implementations set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the example embodiments to those skilled in the art. Like reference numerals refer to like elements throughout the figures, and thus description of the same will be omitted.

[0036] The terms "one", "a", "an", "the", and "said" are used to mean one or more, unless expressly specified otherwise; the terms "includes", "including", and "have" are used to mean "including, but not limited to".

[0037] Example 1

[0038] To solve the above problems, according to one aspect of the present application, as shown in the accompanying drawings, a distributed wind farm on-grid power estimation method is provided according to one aspect of the present application, specifically comprising: Figure 1 S1, based on weather data of a target area, divides dates in a preset time period into different similar date groups, and determines abnormal conditions of historical output waveforms of different wind turbines in the distributed wind farm in different time periods according to weather data of different similar date groups in different time periods;

[0039] Further, the preset time period is one year, three years, or five years.

[0040] Specifically, the dates in the preset time period are divided into different similar date groups, specifically comprising:

[0041] Based on the deviation amount of wind speed of different dates in different time periods, dates in which the deviation amount of wind speed of different time periods is within a preset wind speed deviation amount range are divided into the same similar date group.

[0042]

[0043] ​It should be noted that the abnormal situation of the historical output waveform includes the number of abnormal times of the power generation waveform of the fan under the weather data and the duration of different abnormal times.

[0044] S2 determines an abnormal date group in the similar date group according to the abnormal situation, and determines an abnormal time period of different fans according to the abnormal situation of the historical output waveform of different fans in different time periods in the abnormal date group when the proportion of the number of dates in the abnormal date group does not meet the requirement.

[0045] Specifically, as shown in the figure, Figure 2 The method for determining the abnormal date group in the similar date group is:

[0046] Based on the abnormal situation, the number of time periods in which the power generation waveform of different fans is abnormal under the weather data is determined.

[0047] The abnormal fan in the fan is determined according to the number of time periods in which the power generation waveform is abnormal under the weather data.

[0048] According to the proportion of the installed capacity of the abnormal fan, it is determined whether the similar date group is an abnormal date group.

[0049] Further, the abnormal fan is a fan whose number of abnormal time periods under the weather data is greater than a preset number of time periods.

[0050] It can be understood that when the proportion of the installed capacity of the abnormal fan is greater than a preset capacity proportion, the similar date group is determined to be an abnormal date group.

[0051] Optionally, the method for determining the abnormal date group in the similar date group is:

[0052] Based on the abnormal situation, the number of time periods in which the power generation waveform of different fans is abnormal under the weather data is determined.

[0053] According to the proportion of the installed capacity of the fan whose power generation waveform is abnormal in different time periods, an abnormal output time period in the time period is determined.

[0054] According to the proportion of the number of abnormal output time periods, it is determined whether the similar date group is an abnormal date group.

[0055] It should be noted that the abnormal output time period in the time period is a time period in which the proportion of the installed capacity of the fan whose power generation waveform is abnormal does not meet the requirement.

[0056] Optionally, it is determined that the similar date group does not belong to an abnormal date group, and specifically includes:

[0057] S11, based on the abnormal situation, determining time periods in which power generation waveforms of different wind turbines are abnormal under the weather data, and determining wind turbine abnormality coefficients of different wind turbines by using a proportion of the number of time periods in which power generation waveforms are abnormal;

[0058] S12, determining waveform abnormality coefficients of different time periods according to proportions of installed capacities of wind turbines in which power generation waveforms are abnormal in different time periods;

[0059] S13, determining a comprehensive abnormality coefficient based on a product of a mean value of the wind turbine abnormality coefficients of different wind turbines and a mean value of the waveform abnormality coefficients of different time periods, and determining whether the similar date group is an abnormal date group by using the comprehensive abnormality coefficient.

[0060] Optionally, the step S11 includes the following content:

[0061] S111, based on the abnormal situation, determining that the similar date group does not belong to an abnormal date group when there is no time period in which power generation waveforms of different wind turbines are abnormal under the weather data, and turning to step S112 when there is a wind turbine in which power generation waveforms are abnormal under the weather data;

[0062] S112, obtaining installed capacities of wind turbines in which power generation waveforms are abnormal, and determining that the similar date group does not belong to an abnormal date group when a proportion of installed capacities of wind turbines in which power generation waveforms are abnormal is less than a preset capacity proportion threshold, and turning to step S113 when the proportion of installed capacities of wind turbines in which power generation waveforms are abnormal is not less than the preset capacity proportion threshold;

[0063] S113, determining wind turbine abnormality coefficients of different wind turbines by using a proportion of the number of time periods in which power generation waveforms are abnormal, and determining that the similar date group belongs to an abnormal date group when the wind turbine abnormality coefficients do not meet a requirement that a required installed capacity of a wind turbine is greater than a preset capacity threshold, and turning to step S12 when the wind turbine abnormality coefficients do not meet a requirement that the required installed capacity of a wind turbine is not greater than the preset capacity threshold.

[0064] Optionally, the step S12 includes the following content:

[0065] S121, determining the number of time periods in which wind turbines are abnormal according to proportions of installed capacities of wind turbines in which power generation waveforms are abnormal in different time periods, and determining that the similar date group does not belong to an abnormal date group when the number of time periods in which wind turbines are abnormal is less than a preset number of time periods, and turning to step S122 when the number of time periods in which wind turbines are abnormal is not less than the preset number of time periods;

[0066] S122 determines the waveform abnormality coefficient for different time periods based on the installed capacity ratio of wind turbines with abnormal power generation waveforms in different time periods. When the waveform abnormality coefficients for different time periods are all within the preset abnormality coefficient range, proceed to step S13. When there are time periods with waveform abnormality coefficients that are not within the preset abnormality coefficient range, proceed to step S123.

[0067] S123 defines the time period when the waveform abnormality coefficient is not within the preset abnormality coefficient range as the waveform abnormal time period. When the proportion of the waveform abnormal time period does not meet the requirements, the similar date group is determined to belong to the abnormal date group. When the proportion of the waveform abnormal time period meets the requirements, the process proceeds to step S13.

[0068] Furthermore, determining whether the percentage of dates in the abnormal date group does not meet the requirements specifically includes:

[0069] Based on the number of dates in different abnormal date groups, determine the total number of dates in different abnormal date groups;

[0070] Based on the proportion of the total number of dates in the preset time period, determine whether the proportion of dates in the abnormal date group meets the requirements.

[0071] Specifically, when the total number of dates accounts for a greater percentage of the total number of dates within the preset time period than the preset percentage of the total number of dates, it is determined that the percentage of dates in the abnormal date group does not meet the requirements.

[0072] It should be noted that when the proportion of dates in the abnormal date group meets the requirements, the on-grid power of the distributed wind farm within a preset time period is evaluated based on the predicted power generation of different wind turbines in different similar date groups.

[0073] It is understood that the power generation forecast is determined based on the forecast results of the power generation of the wind turbine under different wind conditions.

[0074] Specifically, such as Figure 3 As shown, the method for determining the abnormal time period is as follows:

[0075] Based on the abnormalities in the historical output waveform of the wind turbine during the specified time period, determine the dates during the specified time period when the wind turbine had abnormal historical output waveforms.

[0076] The dates in the time period where the historical output waveform was abnormal are taken as waveform abnormal dates. Based on the percentage of the cumulative duration of the historical output waveform being abnormal on different waveform abnormal dates, the date abnormality coefficients for different waveform abnormal dates are determined.

[0077] According to the date anomaly coefficient, a screening abnormal date in the waveform abnormal date is determined, and a proportion of the screening abnormal date in the abnormal date group is used to determine whether the time period is an abnormal time period of the fan.

[0078] Further, when the proportion of the screening abnormal date in the abnormal date group does not meet the requirement, it is determined that the time period is an abnormal time period of the fan.

[0079] It can be understood that the abnormality of the historical output waveform specifically includes low voltage, current distortion, and harmonic content abnormality.

[0080] In another possible embodiment, the method for determining the abnormal time period is as follows:

[0081] When the historical output waveform of the fan in the time period does not have a date with abnormality, it is determined that the time period does not belong to the abnormal time period of the fan;

[0082] When the historical output waveform of the fan in the time period has a date with abnormality:

[0083] The date with abnormality in the historical output waveform in the time period is taken as a waveform abnormal date, a proportion of the waveform abnormal date in the abnormal date group is obtained, and when the proportion of the waveform abnormal date of the fan in the abnormal date group does not meet the requirement, it is determined that the time period belongs to the abnormal time period of the fan;

[0084] When the proportion of the waveform abnormal date of the fan in the abnormal date group meets the requirement:

[0085] An accumulated time length proportion of the historical output waveform with abnormality in different waveform abnormal dates is obtained, and when there is a waveform abnormal date with an accumulated time length proportion of the historical output waveform with abnormality that does not meet the requirement, it is determined that the time period belongs to the abnormal time period of the fan;

[0086] When the accumulated time length proportion of the historical output waveform with abnormality in different waveform abnormal dates all meets the requirement:

[0087] The accumulated time length proportion of the historical output waveform with abnormality in different waveform abnormal dates is used to determine a date anomaly coefficient of the different waveform abnormal dates in combination with the number of time periods with abnormality of the historical output waveform, and when there is a waveform abnormal date with a date anomaly coefficient that does not meet the requirement, it is determined that the time period belongs to the abnormal time period of the fan;

[0088] When there is no waveform abnormal date with a date anomaly coefficient that does not meet the requirement:

[0089] The waveform abnormal date in the preset date abnormal coefficient interval is taken as a screening abnormal date, and when the number of the screening abnormal dates does not meet the requirement, it is determined that the time period belongs to an abnormal time period of the fan;

[0090] When the number of the screening abnormal dates meets the requirement:

[0091] The number proportion of the waveform abnormal dates in the abnormal date group is obtained, and the time period abnormal coefficient is determined in combination with the average value of the date abnormal coefficients of different waveform abnormal dates, and the time period abnormal coefficient is used to determine whether the time period is an abnormal time period of the fan.

[0092] Further, the time period abnormal coefficient is the product of the number proportion of the waveform abnormal dates in the abnormal date group and the average value of the date abnormal coefficients of different waveform abnormal dates.

[0093] S3 determines the power generation restriction fan in the abnormal date group according to the distribution data of the abnormal time period, obtains the date number proportion of different abnormal date groups, and determines whether the evaluation of the on-grid power of the distributed wind power plant in the preset time period can be performed in combination with the installed capacity proportion of the power generation restriction fan in different abnormal date groups.

[0094] Specifically, as shown in Figure 4 The method for determining the power generation restriction fan is:

[0095] The number proportion of the abnormal time periods of the fan in the abnormal date group corresponding to the date is determined according to the distribution data of the abnormal time period.

[0096] Based on the distribution data, the interval time period number between adjacent abnormal time periods is determined, and the interval time period number is used to determine the dispersed time period in the abnormal time period.

[0097] The output abnormal value of the fan is determined according to the product of the number proportion of the abnormal time period and the number proportion of the dispersed time period in the abnormal time period, and the output abnormal value is used to determine whether the fan is a power generation restriction fan in the abnormal date group.

[0098] Further, the dispersed time period is an abnormal time period with an interval time period number greater than a preset time period number between any adjacent abnormal time period.

[0099] Optionally, the output abnormal value of the fan ranges from 0 to 1, and when the output abnormal value of the fan is greater than a preset abnormal threshold value, it is determined that the fan belongs to a power generation restriction fan in the abnormal date group.

[0100] Specifically, determining whether the distributed wind farm can perform the evaluation of the on-grid power in the preset time period includes:

[0101] On the basis of the number of dates in different abnormal date groups, the proportion of the number of dates in the preset time period is determined, and the weight coefficient of different abnormal date groups is determined by using the proportion of the number of dates.

[0102] According to the proportion of the installed capacity of the power generation limiting motor in different abnormal date groups, the power generation abnormality coefficient of different abnormal date groups is determined.

[0103] Based on the sum of the product of the weight coefficient and the power generation abnormality coefficient of different abnormal date groups, the output abnormality probability of the distributed wind farm is determined, and the evaluation of the on-grid power of the distributed wind farm in the preset time period is determined by using the output abnormality probability.

[0104] Further, when the output abnormality probability is greater than the preset abnormality probability threshold, it is determined that the evaluation of the on-grid power of the distributed wind farm in the preset time period cannot be performed.

[0105] Specifically, when the evaluation of the on-grid power of the distributed wind farm in the preset time period can be performed, the evaluation of the on-grid power of the distributed wind farm in the preset time period is performed based on the power generation prediction of different wind turbines in different similar date groups.

[0106] In another possible embodiment, determining whether the distributed wind farm can perform the evaluation of the on-grid power in the preset time period includes:

[0107] The proportion of the installed capacity of the power generation limiting motor in different abnormal date groups is obtained, and when the proportion of the installed capacity of the power generation limiting motor in different abnormal date groups is less than the preset proportion threshold, it is determined that the evaluation of the on-grid power of the distributed wind farm in the preset time period can be performed.

[0108] When there is an abnormal date group whose proportion of the installed capacity of the power generation limiting motor is not less than the preset proportion threshold:

[0109] When the average of the proportion of the installed capacity of the power generation limiting motor in different abnormal date groups is greater than the preset proportion threshold:

[0110] The proportion of the number of dates in the preset time period of different abnormal date groups is obtained, and when the proportion of the number of dates is greater than the preset proportion limit value, it is determined that the evaluation of the on-grid power of the distributed wind farm in the preset time period cannot be performed.

[0111] When the average of the installed capacity proportion of the power generation limiting motor of different abnormal date groups is not greater than a preset proportion threshold value or the date quantity proportion is not greater than a preset proportion limit value:

[0112] The abnormal date group with the installed capacity proportion of the power generation limiting motor not less than the preset proportion threshold value is taken as a screening abnormal group, and when the date quantity proportion of different screening abnormal groups in a preset time period does not meet the requirement, it is determined that the evaluation of the on-grid power of the distributed wind power plant in the preset time period cannot be performed.

[0113] When the date quantity proportion of different screening abnormal groups in a preset time period meets the requirement:

[0114] On the basis of the date quantity of different abnormal date groups, the date quantity proportion of the date quantity of different abnormal date groups in the preset time period is determined, and the date quantity proportion is used to determine the weight coefficient of different abnormal date groups.

[0115] According to the installed capacity proportion of the power generation limiting motor in different abnormal date groups, the power generation abnormality coefficient of different abnormal date groups is determined.

[0116] Based on the sum of the product of the weight coefficient and the power generation abnormality coefficient of different abnormal date groups, the output abnormality probability of the distributed wind power plant is determined, and the output abnormality probability is used to determine whether the evaluation of the on-grid power of the distributed wind power plant in a preset time period can be performed.

[0117] Embodiment 2

[0118] In a second aspect, the present application provides a computer system, comprising a memory and a processor connected in communication, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to perform the above-mentioned distributed wind power plant on-grid power estimation method.

[0119] Each embodiment in the specification is described in a progressive manner, and the same and similar parts of each embodiment can be referred to each other. Each embodiment mainly describes the difference from other embodiments. Especially, the device, equipment and non-volatile computer storage medium embodiments are basically similar to the method embodiments, so the description is relatively simple, and the related parts can be referred to the part of the method embodiment.

[0120] The above-described embodiments of the application have special structure and can achieve the desired results. Other embodiments can have different structures and achieve the same results. The purpose of the above-described embodiments is to illustrate the principles of the application and not to limit the scope of the application. The scope of the application is defined by the claims and their equivalents. Other embodiments are within the scope of the claims.

[0121] The above description is merely illustrative of the embodiments of the present application and is not intended to limit the scope of the present application. Various modifications can be made by those skilled in the art based upon the teachings disclosed herein. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present application shall fall within the scope of the claims of the present application.

Claims

1. A method for estimating the grid-connected electricity of a distributed wind farm, characterized in that, Specifically, it includes: Based on the weather data of the target area, the dates within the preset time period are divided into different similar date groups. Based on the weather data of different similar date groups in different time periods, the abnormalities of the historical output waveforms of different wind turbines in the distributed wind farm at different time periods are determined. The abnormal date groups in the similar date groups are determined by the abnormal situation. When the proportion of the number of dates in the abnormal date groups does not meet the requirements, the abnormal time periods of different wind turbines are determined by the abnormal situation of the historical output waveforms of different wind turbines in different time periods in the abnormal date groups. The distribution data of the abnormal period is used to determine the power generation restriction wind turbines in the abnormal date group, obtain the date quantity ratio of different abnormal date groups, and combine the installed capacity ratio of power generation restriction motors in different abnormal date groups to determine whether the grid-connected electricity of the distributed wind farm can be evaluated within the preset time period. Determining that the similar date group does not belong to the abnormal date group specifically includes: Based on the aforementioned anomalies, the periods in which the power generation waveforms of different wind turbines exhibited anomalies under the aforementioned weather data were determined, and the wind turbine anomaly coefficient for different wind turbines was determined by the proportion of the number of periods in which the power generation waveforms exhibited anomalies. Based on the installed capacity percentage of wind turbines with abnormal power generation waveforms in different time periods, the waveform abnormality coefficient for different time periods is determined. A comprehensive anomaly coefficient is determined by multiplying the average of the wind turbine anomaly coefficients for different wind turbines and the average of the waveform anomaly coefficients for different time periods. The comprehensive anomaly coefficient is then used to determine whether the similar date group is an abnormal date group. The method for determining the power generation limitation wind turbine is as follows: The proportion of the number of wind turbines in the abnormal period of the date corresponding to the abnormal date group is determined based on the distribution data of the abnormal period. Based on the distribution data, the number of interval periods between different adjacent abnormal periods is determined, and the number of interval periods is used to determine the scattered periods in the abnormal periods. The abnormal output value of the wind turbine is determined by multiplying the proportion of abnormal periods by the proportion of scattered periods within the abnormal periods, and the abnormal output value is used to determine whether the wind turbine is a power generation restricted wind turbine in the abnormal date group. Determine whether it is possible to assess the grid-connected electricity generated by distributed wind farms within a preset time period, specifically including: Based on the number of dates in different abnormal date groups, determine the proportion of the number of dates in different abnormal date groups within the preset time period, and use the proportion of the number of dates to determine the weighting coefficient of different abnormal date groups; Based on the proportion of installed capacity of generators subject to power generation restrictions in different abnormal date groups, the power generation abnormality coefficient for each abnormal date group is determined. The power output anomaly probability of the distributed wind farm is determined by summing the products of the weighting coefficients of different abnormal date groups and the power generation anomaly coefficients, and the power output anomaly probability is used to determine whether the grid-connected power of the distributed wind farm can be evaluated within a preset time period. The preset time period is one year, three years, or five years.

2. The method for estimating the grid-connected power of distributed wind farms as described in claim 1, characterized in that, Divide dates within a preset time period into different similar date groups, specifically including: Based on the deviation of wind speed at different times on different dates, dates whose wind speed deviation at different times is within the preset wind speed deviation range are grouped into the same similar date group.

3. The method for estimating the grid-connected power of distributed wind farms as described in claim 1, characterized in that, When the power output anomaly probability is greater than the preset anomaly probability threshold, it is determined that the grid-connected power of the distributed wind farm within the preset time period cannot be evaluated.

4. The method for estimating the grid-connected power of distributed wind farms as described in claim 1, characterized in that, When it is possible to assess the grid-connected power generation of a distributed wind farm within a preset time period, the assessment is performed based on the predicted power generation of different wind turbines in different similar date groups.

5. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a method for estimating the grid-connected power of a distributed wind farm as described in any one of claims 1-4.

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