Distributed wind power plant on-grid energy estimation method and system

Abstract

The invention provides a distributed wind power plant on-grid electric quantity estimation method and system, and belongs to the technical field of electric power systems, and the method specifically comprises the steps: determining an abnormal date group in similar date groups through employing an abnormal condition, and when the ratio of the number of dates in the abnormal date group does not meet a requirement, determining that the number of the dates in the abnormal date group does not meet a requirement; determining abnormal time periods of different fans according to abnormal conditions of historical output waveforms of different fans in the abnormal date groups in different time periods, determining power generation limiting fans in the abnormal date groups according to distribution data of the abnormal time periods, obtaining date number proportions of different abnormal date groups, and determining power generation limiting fans in the power generation limiting fans according to the date number proportions. And whether the on-grid electric quantity of the distributed wind power plant in the preset time period can be evaluated or not is determined by combining the installed capacity proportions of the power generation limiting motors in different abnormal date groups, so that the accuracy of an estimation processing result is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power systems, and in particular relates to a method and system for estimating the on-grid power of a distributed wind farm. Background Art

[0002] In order to estimate the amount of electricity on the grid of distributed wind farms, the invention patent application CN116169727A "Power Generation Assessment Method and System for Reconstruction Project of Old Wind Farms without Wind Measurement Data" calculates the power generation correction coefficient of mesoscale wind data based on the actual power generation of old wind farms, and calculates and corrects the power generation of reconstructed wind farms. However, the above technical solutions all have the following technical problems: When evaluating the amount of electricity connected to the grid, the existing technical solutions ignore the abnormal output waveforms of different wind turbines under different weather conditions, such as high harmonic content or current distortion. If the time periods with abnormal output waveforms cannot be excluded, the accuracy of the estimated results of the electricity connected to the grid cannot be guaranteed because the above-mentioned time periods cannot be normally connected to the grid.

[0003] In response to the above technical problems, the present application specifically provides a method and system for estimating the grid-connected power of a distributed wind farm. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In a first aspect, the present application provides a method for estimating the amount of electricity on-grid in a distributed wind farm, specifically comprising: S1 divides the dates within a preset time period into different similar date groups based on the weather data of the target area, and determines the abnormal conditions of the historical output waveforms of different wind turbines in the distributed wind farm at different time periods according to the weather data of different similar date groups at different time periods; S2 determines an abnormal date group in the similar date group using the abnormal situation, and when the proportion of the number of dates in the abnormal date group does not meet the requirement, determines the abnormal time period of different fans according to the abnormal situation of the historical output waveforms of different fans in the abnormal date group at different time periods; S3 determines the power generation restricted wind turbines in the abnormal date group based on the distribution data of the abnormal time period, obtains the proportion of the number of dates in different abnormal date groups, and determines whether it is possible to evaluate the online power consumption of the distributed wind farm within the preset time period in combination with the installed capacity proportion of the power generation restricted motors in different abnormal date groups.

[0005] The beneficial effects of the present invention are: The abnormal time periods of different wind turbines are determined based on the abnormal conditions of the historical output waveforms of different wind turbines in the abnormal date group at different time periods, which enables the screening of abnormal time periods based on the abnormal conditions of the historical output waveforms under similar weather conditions. This avoids the technical problem of inaccurate screening results for abnormal time periods caused by considering only the historical data of a certain day, improves the accuracy of the screening process for abnormal time periods, and lays the foundation for the differentiated determination of wind turbines with higher levels of power generation waveform abnormalities in abnormal groups.

[0006] Whether the online power generation of the distributed wind farm can be evaluated within the preset time period is determined by the proportion of the number of dates in different abnormal date groups and the proportion of the installed capacity of the power generation-restricted motors. This not only takes into account the impact of the estimated deviation of the online power generation of different abnormal date groups due to the difference in the proportion of the number of dates on the overall estimation result, but also takes into account the difference in the probability of the estimated deviation of the online power generation due to the difference in the proportion of the installed capacity of the power generation-restricted motors in different abnormal date groups, thereby ensuring the accuracy of the evaluation result of the online power generation.

[0007] A further technical solution is that the preset time period is one year, three years or five years.

[0008] A further technical solution is to divide the dates within a preset time period into different similar date groups, specifically including: Based on the deviations of wind speeds at different time periods on different dates, dates whose wind speed deviations at different time periods are within a preset wind speed deviation range are grouped into the same similar date group.

[0009] A further technical solution is that the abnormal conditions of the historical output waveform include 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.

[0010] A further technical solution is that the method for determining the abnormal date group in the similar date group is: Based on the abnormal situation, determining the number of time periods in which the power generation waveforms of different wind turbines under the weather data are abnormal; Determine abnormal wind turbines among the wind turbines by using the number of time periods in which abnormalities exist under the weather data; Whether the similar date group is an abnormal date group is determined according to the installed capacity proportion of the abnormal wind turbines.

[0011] A further technical solution is to determine whether it is possible to evaluate the grid-connected power of a distributed wind farm within a preset time period, specifically including: Based on the number of dates in different abnormal date groups, determining the proportion of the number of dates in different abnormal date groups within the preset time period, and using the proportion of the number of dates to determine the weight coefficients of different abnormal date groups; Determine the power generation abnormality coefficients of different abnormal date groups according to the installed capacity proportions of power generation limiting motors in different abnormal date groups; Based on the sum of the products of weight coefficients of different abnormal date groups and power generation abnormality coefficients, the output abnormality probability of the distributed wind farm is determined, and the output abnormality probability is used to determine whether the online power of the distributed wind farm can be evaluated within a preset time period.

[0012] A further technical solution is that, when the output abnormality probability is greater than a preset abnormality probability threshold, it is determined that the online power consumption of the distributed wind farm within a preset time period cannot be evaluated.

[0013] A further technical solution is that, when it is possible to evaluate the online power generation of a distributed wind farm within a preset time period, the online power generation of the distributed wind farm within the preset time period is evaluated based on the predicted power generation of different wind turbines in different similar date groups.

[0014] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for estimating the grid-connected power of a distributed wind farm when running the computer program.

[0015] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0018] Figure 1 It is a flow chart of a method for estimating the amount of electricity on the grid of a distributed wind farm; Figure 2 is a flow chart of a method for determining an abnormal date group in a similar date group; Figure 3 is a flow chart of a method for determining an abnormal period; Figure 4 It is a flow chart of a method for determining a power generation-limited wind turbine. DETAILED DESCRIPTION

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0020] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0021] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a method for estimating the amount of electricity on the grid of a distributed wind farm is provided, which specifically includes: S1 divides the dates within a preset time period into different similar date groups based on the weather data of the target area, and determines the abnormal conditions of the historical output waveforms of different wind turbines in the distributed wind farm at different time periods according to the weather data of different similar date groups at different time periods; Furthermore, the preset time period is one year, three years or five years.

[0022] Specifically, the dates within the preset time period are divided into different similar date groups, including: Based on the deviations of wind speeds at different time periods on different dates, dates whose wind speed deviations at different time periods are within a preset wind speed deviation range are grouped into the same similar date group.

[0023] It should be noted that the abnormal conditions of the historical output waveform include 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.

[0024] S2 determines an abnormal date group in the similar date group using the abnormal situation, and when the proportion of the number of dates in the abnormal date group does not meet the requirement, determines the abnormal time period of different fans according to the abnormal situation of the historical output waveforms of different fans in the abnormal date group at different time periods; Specifically, Figure 2As shown, the method for determining the abnormal date group in the similar date group is: Based on the abnormal situation, determining the number of time periods in which the power generation waveforms of different wind turbines under the weather data are abnormal; Determine abnormal wind turbines among the wind turbines by using the number of time periods in which abnormalities exist under the weather data; Whether the similar date group is an abnormal date group is determined according to the installed capacity proportion of the abnormal wind turbines.

[0025] Furthermore, the abnormal wind turbine is a wind turbine in which the number of time periods in which the abnormality exists under the weather data is greater than the number of preset time periods.

[0026] It can be understood that when the installed capacity proportion of the abnormal wind turbines is greater than the preset capacity proportion, the similar date group is determined to be an abnormal date group.

[0027] Optionally, the method for determining the abnormal date group in the similar date group is: Based on the abnormal situation, determining a time period when the power generation waveforms of different wind turbines under the weather data are abnormal; Determine an abnormal output period in the period according to the installed capacity proportion of wind turbines with abnormal power generation waveforms in different time periods; Whether the similar date group is an abnormal date group is determined according to the number ratio of the abnormal output time periods.

[0028] It should be noted that the abnormal output period in the time period is a period when the installed capacity of the wind turbines with abnormal power generation waveform does not meet the requirements.

[0029] Optionally, determining that the similar date group does not belong to the abnormal date group specifically includes: S11 determines, based on the abnormal situation, time periods when the power generation waveforms of different wind turbines are abnormal under the weather data, and determines wind turbine abnormality coefficients of different wind turbines using the proportion of the number of time periods when the power generation waveforms are abnormal; S12 determines waveform anomaly coefficients for different time periods according to the installed capacity proportion of wind turbines with abnormal power generation waveforms in different time periods; S13 determines a comprehensive abnormality coefficient based on the product of the average values ​​of the fan abnormality coefficients of different fans and the average values ​​of the waveform abnormality coefficients of different time periods, and uses the comprehensive abnormality coefficient to determine whether the similar date group is an abnormal date group.

[0030] Optionally, the above step S11 includes the following contents: S111 determines based on the abnormal situation that when different wind turbines do not have a period of time when the power generation waveform is abnormal under the weather data, it is determined that the similar date group does not belong to the abnormal date group. When there is a wind turbine in a period of time when the power generation waveform is abnormal under the weather data, the process proceeds to step S112; S112: obtaining the installed capacity of the wind turbine in the period when the power generation waveform is abnormal. When the proportion of the installed capacity of the wind turbine in the period when the abnormality exists is less than the preset capacity proportion threshold, it is determined that the similar date group does not belong to the abnormal date group. When the proportion of the installed capacity of the wind turbine in the period when the abnormality exists is not less than the preset capacity proportion threshold, the process proceeds to step S113. S113 determines the fan abnormality coefficients of different wind turbines using the proportion of time periods in which the power generation waveform is abnormal. When the fan abnormality coefficient does not meet the requirements and the installed capacity of the wind turbine is greater than the preset capacity threshold, it is determined that the similar date group belongs to the abnormal date group. When the fan abnormality coefficient does not meet the requirements and the installed capacity of the wind turbine is not greater than the preset capacity threshold, proceed to step S12.

[0031] Optionally, the above step S12 includes the following contents: S121 determines the number of time periods in which the abnormal wind turbines exist according to the installed capacity proportion of the wind turbines with abnormal power generation waveforms in different time periods. When the number of time periods in which the abnormal wind turbines exist is less than the number of preset time periods, it is determined that the similar date group does not belong to the abnormal date group. When the number of time periods in which the abnormal wind turbines exist is not less than the number of preset time periods, the process proceeds to step S122. S122 determines the waveform abnormality coefficients of different time periods according to the installed capacity proportion of wind turbines with abnormal power generation waveforms in different time periods. When the waveform abnormality coefficients of different time periods are all within the preset abnormality coefficient interval, the process proceeds to step S13. When there is a time period when the waveform abnormality coefficient is not within the preset abnormality coefficient interval, the process proceeds to step S123. S123 regards the time period when the waveform anomaly coefficient is not within the preset anomaly coefficient range as the waveform anomaly time period. When the number ratio of the waveform anomaly time periods does not meet the requirements, it is determined that the similar date group belongs to the abnormal date group. When the number ratio of the waveform anomaly time periods meets the requirements, proceed to step S13.

[0032] Further, it is determined that the proportion of the number of dates in the abnormal date group does not meet the requirements, specifically including: Determining the total number of dates in the different abnormal date groups based on the number of dates in the different abnormal date groups; Based on the proportion of the total number of dates to the number of dates in the preset time period, it is determined whether the proportion of the number of dates in the abnormal date group meets the requirement.

[0033] Specifically, when the proportion of the total number of dates in the preset time period is greater than the preset proportion of the number of dates, it is determined that the proportion of the number of dates in the abnormal date group does not meet the requirement.

[0034] It should be noted that when the proportion of the number of dates in the abnormal date group meets the requirements, the online power generation of the distributed wind farm within the preset time period is evaluated based on the predicted power generation of different wind turbines in different similar date groups.

[0035] It can be understood that the predicted power generation is determined based on the predicted results of the power generation of the wind turbine under different wind conditions.

[0036] Specifically, Figure 3 As shown, the method for determining the abnormal period is: Based on the abnormality of the historical output waveform of the wind turbine in the period, determining the date when the historical output waveform of the wind turbine in the period was abnormal; The date when the historical output waveform in the period is abnormal is used as the waveform abnormal date, and the date abnormality coefficients of different waveform abnormal dates are determined according to the proportion of the cumulative duration of the historical output waveform of different waveform abnormal dates. The screening abnormal dates in the waveform abnormal dates are determined according to the date abnormality coefficient, and the proportion of the screening abnormal dates in the abnormal date group is used to determine whether the time period is an abnormal time period of the wind turbine.

[0037] Further, when the proportion of the number of the screened abnormal dates in the abnormal date group does not meet the requirement, the time period is determined to be the abnormal time period of the wind turbine.

[0038] It is understandable that the abnormalities in the historical output waveform specifically include low voltage, current distortion and abnormal harmonic content.

[0039] In another possible embodiment, the method for determining the abnormal period is: When it is determined that the fan has no date with abnormal historical output waveform in the period based on the abnormal situation of the historical output waveform of the fan in the period, it is determined that the period does not belong to the abnormal period of the fan; When the wind turbine has a date with abnormal historical output waveform during the period: The date on which the historical output waveform in the period is abnormal is used as the waveform abnormal date, and the number ratio of the waveform abnormal dates in the abnormal date group is obtained; when the number ratio of the waveform abnormal dates of the wind turbine in the abnormal date group does not meet the requirement, it is determined that the period belongs to the abnormal period of the wind turbine; When the proportion of the number of abnormal waveform dates of the wind turbine in the abnormal date group meets the requirement: Obtaining the percentage of the cumulative duration of the abnormality of the historical output waveform on different waveform abnormality dates, and when there is a waveform abnormality date on which the cumulative duration of the abnormality of the historical output waveform does not meet the required waveform abnormality date, determining that the period belongs to the abnormal period of the wind turbine; When the percentage of the cumulative duration of abnormal historical output waveforms on different waveform abnormal dates meets the requirements: The date anomaly coefficients of different waveform anomaly dates are determined based on the proportion of the cumulative duration of the abnormality of the historical output waveform on different waveform anomaly dates and the number of time periods in which the historical output waveform is abnormal. When there is a waveform anomaly date whose date anomaly coefficient does not meet the requirements, it is determined that the time period belongs to the abnormal time period of the wind turbine. When there is no waveform abnormal date whose date abnormal coefficient does not meet the requirements: The waveform abnormal date whose date abnormality coefficient is within the preset date abnormality coefficient interval is used as the screening abnormal date, and when the number of the screening abnormal dates does not meet the requirement, it is determined that the period belongs to the abnormal period of the wind turbine to which it belongs; When the number of screening exception dates meets the requirement: The proportion of the waveform abnormality dates in the abnormal date group is obtained, and the time period abnormality coefficient is determined in combination with the average value of the date abnormality coefficients of different waveform abnormality dates, and the time period abnormality coefficient is used to determine whether the time period is an abnormal time period of the wind turbine.

[0040] Furthermore, the time period anomaly coefficient is the product of the proportion of the number of the waveform abnormal dates in the abnormal date group and the average value of the date anomaly coefficients of different waveform abnormal dates.

[0041] S3 determines the power generation restricted wind turbines in the abnormal date group based on the distribution data of the abnormal time period, obtains the proportion of the number of dates in different abnormal date groups, and determines whether it is possible to evaluate the online power consumption of the distributed wind farm within the preset time period in combination with the installed capacity proportion of the power generation restricted motors in different abnormal date groups.

[0042] Specifically, Figure 4 As shown, the method for determining the power generation limiting wind turbine is: Determine the proportion of the number of abnormal periods of the wind turbines on the date corresponding to the abnormal date group based on the distribution data of the abnormal period; Based on the distribution data, determining the number of interval time periods between different adjacent abnormal time periods, and using the number of interval time periods to determine the dispersed time periods in the abnormal time period; The abnormal output value of the wind turbine is determined according to the product of the number proportion of the abnormal time periods and the number proportion of the dispersed time periods in the abnormal time periods, and the abnormal output value is used to determine whether the wind turbine is a power generation-limited wind turbine in the abnormal date group.

[0043] Furthermore, the dispersed time period is an abnormal time period in which the number of interval time periods between any adjacent abnormal time period is greater than a preset number of time periods.

[0044] Optionally, the abnormal output value of the wind turbine ranges from 0 to 1, wherein when the abnormal output value of the wind turbine is greater than a preset abnormal threshold, it is determined that the wind turbine is a power generation-limited wind turbine in the abnormal date group.

[0045] Specifically, determining whether it is possible to evaluate the grid-connected power of a distributed wind farm within a preset time period includes: Based on the number of dates in different abnormal date groups, determining the proportion of the number of dates in different abnormal date groups within the preset time period, and using the proportion of the number of dates to determine the weight coefficients of different abnormal date groups; Determine the power generation abnormality coefficients of different abnormal date groups according to the installed capacity proportions of power generation limiting motors in different abnormal date groups; Based on the sum of the products of weight coefficients of different abnormal date groups and power generation abnormality coefficients, the output abnormality probability of the distributed wind farm is determined, and the output abnormality probability is used to determine whether the online power of the distributed wind farm can be evaluated within a preset time period.

[0046] Furthermore, when the output abnormality probability is greater than a preset abnormality probability threshold, it is determined that the online power consumption of the distributed wind farm within the preset time period cannot be evaluated.

[0047] Specifically, when the online power of a distributed wind farm in a preset time period can be evaluated, the online power of the distributed wind farm in the preset time period is evaluated based on the predicted power generation of different wind turbines in different similar date groups.

[0048] In another possible embodiment, determining whether it is possible to evaluate the grid-connected power of the distributed wind farm within a preset time period specifically includes: Obtaining the installed capacity proportion of the power generation-limited motors in different abnormal date groups, and when the installed capacity proportion of the power generation-limited motors in different abnormal date groups is less than a preset proportion threshold, determining that the online power consumption of the distributed wind farm in the preset time period can be evaluated; When there is an abnormal date group in which the installed capacity of the power generation-limited motor accounts for no less than the preset percentage threshold: When the average value of the installed capacity proportion of the power generation-limited motors in different abnormal date groups is greater than the preset proportion threshold: Obtaining the proportion of the number of dates of different abnormal date groups within a preset time period, and when the proportion of the number of dates is greater than a preset proportion limit value, determining that the online power consumption of the distributed wind farm within the preset time period cannot be evaluated; When the average value of the installed capacity proportion of the power generation limiting motors in different abnormal date groups is not greater than the preset proportion threshold or the proportion of the number of dates is not greater than the preset proportion limit value: The abnormal date group in which the installed capacity of the power generation limiting motor accounts for no less than the preset proportion threshold is used as the screening abnormal group. When the proportion of the number of dates of different screening abnormal groups within the preset time period does not meet the requirement, it is determined that the evaluation of the grid-connected power of the distributed wind farm within the preset time period cannot be performed; When the proportion of the number of dates in different screening anomaly groups within the preset period meets the requirements: Based on the number of dates in different abnormal date groups, determining the proportion of the number of dates in different abnormal date groups within the preset time period, and using the proportion of the number of dates to determine the weight coefficients of different abnormal date groups; Determine the power generation abnormality coefficients of different abnormal date groups according to the installed capacity proportions of power generation limiting motors in different abnormal date groups; Based on the sum of the products of weight coefficients of different abnormal date groups and power generation abnormality coefficients, the output abnormality probability of the distributed wind farm is determined, and the output abnormality probability is used to determine whether the online power of the distributed wind farm can be evaluated within a preset time period.

[0049] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for estimating the grid-connected power of a distributed wind farm when running the computer program.

[0050] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0051] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for estimating the amount of electricity connected to the grid in a distributed wind farm, characterized in that: Specifically include: Based on the weather data of the target area, the dates within the preset time period are divided into different similar date groups. According to the weather data of different similar date groups in different time periods, the abnormal conditions of the historical output waveforms of different wind turbines in the distributed wind farm in different time periods are determined; Determine an abnormal date group in the similar date group by using the abnormal situation, and when the proportion of the number of dates in the abnormal date group does not meet the requirement, determine the abnormal time periods of different fans by using the abnormal situations of the historical output waveforms of different fans in the abnormal date group at different time periods; The distribution data of the abnormal time period is used to determine the power generation restricted wind turbines in the abnormal date group, the date quantity proportion of different abnormal date groups is obtained, and combined with the installed capacity proportion of the power generation restricted motors in different abnormal date groups, it is determined whether the online power generation of the distributed wind farm within the preset time period can be evaluated.

2. The method for estimating the amount of electricity consumed by a distributed wind farm as claimed in claim 1, characterized in that: The preset time period is one year, three years or five years.

3. The method for estimating the amount of electricity consumed by a distributed wind farm as claimed in claim 1, characterized in that: Divide the dates within a preset time period into different similar date groups, including: Based on the deviations of wind speeds at different time periods on different dates, dates whose wind speed deviations at different time periods are within a preset wind speed deviation range are grouped into the same similar date group.

4. The method for estimating the amount of electricity connected to the grid in a distributed wind farm according to claim 1, characterized in that: The method for determining the abnormal date group in the similar date group is: Based on the abnormal situation, determining the number of time periods in which the power generation waveforms of different wind turbines under the weather data are abnormal; Determine abnormal wind turbines among the wind turbines by using the number of time periods in which abnormalities exist under the weather data; Whether the similar date group is an abnormal date group is determined according to the installed capacity proportion of the abnormal wind turbines.

5. The method for estimating the amount of electricity connected to the grid in a distributed wind farm according to claim 4, characterized in that: The abnormal wind turbine is a wind turbine in which the number of time periods in which the abnormality exists under the weather data is greater than the number of preset time periods.

6. The method for estimating the amount of electricity connected to the grid in a distributed wind farm according to claim 4, characterized in that: When the installed capacity proportion of the abnormal wind turbines is greater than the preset capacity proportion, the similar date group is determined to be an abnormal date group.

7. The method for estimating the amount of electricity connected to the grid in a distributed wind farm according to claim 1, characterized in that: Determine whether it is possible to evaluate the grid-connected power of the distributed wind farm within the preset time period, including: Based on the number of dates in different abnormal date groups, determining the proportion of the number of dates in different abnormal date groups within the preset time period, and using the proportion of the number of dates to determine the weight coefficients of different abnormal date groups; Determine the power generation abnormality coefficients of different abnormal date groups according to the installed capacity proportions of power generation limiting motors in different abnormal date groups; Based on the sum of the products of weight coefficients of different abnormal date groups and power generation abnormality coefficients, the output abnormality probability of the distributed wind farm is determined, and the output abnormality probability is used to determine whether the online power of the distributed wind farm can be evaluated within a preset time period.

8. The method for estimating the amount of electricity connected to the grid in a distributed wind farm according to claim 7, characterized in that: When the output abnormality probability is greater than a preset abnormality probability threshold, it is determined that the online power generation of the distributed wind farm within the preset time period cannot be evaluated.

9. The method for estimating the amount of electricity consumed by a distributed wind farm as claimed in claim 7, characterized in that: When the online power generation of the distributed wind farm within a preset time period can be evaluated, the online power generation of the distributed wind farm within the preset time period is evaluated based on the predicted power generation of different wind turbines in different similar date groups.

10. A computer system comprising: A memory and a processor that are communicatively connected, 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, a method for estimating the grid-connected power of a distributed wind farm as described in any one of claims 1-9 is executed.

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