Method and device for identifying operation risk of wind turbine generator system, electronic equipment, storage medium and program product

Abstract

The present disclosure provides a wind turbine generator set operation risk identification method, device, electronic equipment, storage medium and program product. The operation risk identification method comprises: determining a first turbulence statistical result based on operation data samples of a target unit when the target unit does not perform sector management, wherein the first turbulence statistical result is obtained by statistically analyzing the turbulence conditions of each wind speed section when the target unit does not perform sector management; determining whether the target unit has a sector that needs to perform sector management but does not actually perform sector management based on the first turbulence statistical result; and if there is a sector that needs to perform sector management but does not actually perform sector management, determining that the target unit has an operation risk.

Description

Technical Field

[0001] This disclosure generally relates to the field of wind power generation technology, and more specifically, to a method, apparatus, electronic equipment, storage medium, and program product for identifying operational risks of wind turbine generator sets. Background Technology

[0002] With the rapid development of the wind power industry, the design cost of wind turbine generators (hereinafter referred to as generators) has been greatly reduced, and the design boundaries of generators have been significantly lowered. Due to the complex wind environment, some projects require generators to operate in complex environmental areas, which has led to the development of sector management and control technology for generators to reduce the operating risks of generators under extreme conditions.

[0003] In recent years, the number of wind farm projects under sector management has gradually increased, and the problems encountered in implementation have also become increasingly prominent. Because a large amount of data is required to set configuration parameters before the sector management scheme is implemented, insufficient information transmission and the lack of closed-loop error prevention measures for manual operations may lead to operational risks for the turbines. Summary of the Invention

[0004] Exemplary embodiments of this disclosure provide a method, apparatus, electronic device, storage medium, and program product for identifying operational risks of wind turbine generator sets, which can effectively identify whether there are operational risks in the generator set.

[0005] According to a first aspect of the present disclosure, a method for identifying operational risks of a wind turbine generator set is provided, comprising: determining a first turbulence statistical result based on operational data samples of the target generator set when sector management is not performed, wherein the first turbulence statistical result is obtained by statistically analyzing the turbulence conditions at various wind speed ranges when sector management is not performed; determining, based on the first turbulence statistical result, whether the target generator set has sectors that require sector management but are not actually managed; and determining that the target generator set has operational risks if sectors that require sector management but are not actually managed exist.

[0006] Optionally, the step of determining whether the target unit has sectors that require sector management but are not actually managed based on the first turbulence statistics results includes: determining the first wind speed standard deviation for each wind speed segment based on the first turbulence statistics results; determining the first reference turbulence value for the target unit under extreme turbulence conditions for each wind speed segment based on the first wind speed standard deviation for each wind speed segment; and determining that the target unit has sectors that require sector management but are not actually managed based on the first reference turbulence value for each wind speed segment, if the number of wind speed segments with the corresponding first reference turbulence value higher than the first preset threshold exceeds the first number.

[0007] Optionally, it also includes: determining a second turbulence statistical result based on the operating data sample of the target unit when performing sector management, wherein the second turbulence statistical result is obtained by statistically analyzing the turbulence situation under each wind speed segment when the target unit performs sector management; and determining, based on the second turbulence statistical result, whether there are sectors of the target unit that do not need to perform sector management but actually perform sector management.

[0008] Optionally, the step of determining whether the target unit has sectors that do not require sector management but actually perform sector management based on the second turbulence statistics results includes: determining the second wind speed standard deviation for each wind speed segment based on the second turbulence statistics results; determining the second reference turbulence value for the target unit under extreme turbulence conditions for each wind speed segment based on the second wind speed standard deviation for each wind speed segment; if the number of wind speed segments with the corresponding second reference turbulence value lower than the second preset threshold exceeds the second number, then it is determined that the target unit has sectors that do not require sector management but actually perform sector management.

[0009] Optionally, it also includes: determining, from the operating data samples of the target unit when sector management is not performed, the operating data samples belonging to each wind speed segment under each wind direction interval; based on the operating data samples belonging to each wind speed segment under each wind direction interval, determining whether there are risky sectors when the target unit does not perform sector management; if there are risky sectors when the target unit does not perform sector management, then determining that the target unit has an operational risk.

[0010] Optionally, the step of determining whether there are risk sectors when the target unit does not perform sector management based on the operational data samples of each wind speed segment under each wind direction interval includes: determining the representative turbulence value of each wind speed segment under each wind direction interval based on the operational data samples of each wind speed segment under each wind direction interval; determining the reference turbulence value of the target unit under normal turbulence conditions in each wind direction interval based on the representative turbulence value of each wind speed segment under each wind direction interval; and determining the wind direction intervals where the corresponding reference turbulence value is higher than a third preset threshold and the distribution of the corresponding operational data samples meets preset conditions as risk sectors when the target unit does not perform sector management.

[0011] Optionally, it also includes: determining the maximum representative turbulence value of the target unit under each wind speed segment within the rated wind speed range based on the operating data sample when sector management is not performed; determining the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range based on the maximum representative turbulence value under each wind speed segment within the rated wind speed range; judging whether the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range is abnormal by referring to the reference turbulence values ​​of other units in the wind farm under extreme turbulence conditions within the rated wind speed range; if the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range is abnormal, then determining that the target unit has an operational risk.

[0012] Optionally, the step of determining the maximum representative turbulence value of the target unit in each wind speed segment within the rated wind speed range based on the operating data sample of the target unit when sector management is not performed includes: determining the representative turbulence value of the target unit in each wind direction interval within each wind speed segment within the rated wind speed range based on the operating data sample of the target unit when sector management is not performed; and taking the maximum value among the representative turbulence values ​​corresponding to each wind speed segment as the maximum representative turbulence value in that wind speed segment.

[0013] Optionally, it also includes: based on the operating data sample of the target unit when performing sector management, statistically analyzing the wind speed segment and sector in which the target unit actually performs sector management; determining whether the wind speed segment and sector in which the target unit actually performs sector management matches the sector management-related configuration parameters of the target unit; if they do not match the sector management-related configuration parameters of the target unit, then determining that the target unit has an operational risk; wherein, the wind speed segment and sector in which the target unit actually performs sector management includes: the sector to which the wind direction belongs and the wind speed segment to which the wind speed belongs when the power is limited due to sector management; and the sector to which the wind direction belongs and the wind speed segment to which the wind speed belongs when the unit is shut down due to sector management.

[0014] Optionally, it also includes: counting the number of wind direction intervals with the highest wind frequency for the target unit; determining whether the prevailing wind direction of the target unit, as assessed during the design of the wind farm, belongs to the number of wind direction intervals; if it does not belong to the number of wind direction intervals, then determining that the prevailing wind direction assessed during the design period for the target unit is inconsistent with the prevailing wind direction assessed during the operation period.

[0015] Optionally, it also includes: obtaining a calculated nacelle setting deviation based on the operating data of the target unit and the meteorological data of the wind farm, wherein the nacelle setting deviation is the angular difference between the initial installation position of the nacelle and the due north direction of the geographical orientation; determining the angle difference between the calculated nacelle setting deviation and the manually calibrated nacelle setting deviation; if the angle difference reaches a fourth preset threshold, then it is determined that the target unit has an operational risk.

[0016] Optionally, it also includes: determining the wind rose diagram of the target unit based on the operating data of the target unit; determining the wind rose diagram of the wind farm based on the meteorological data of the wind farm; determining whether the yaw position information and / or manually calibrated nacelle setting deviation of the target unit are accurate by comparing the differences between the wind rose diagram of the target unit and the wind rose diagram of the wind farm; if the yaw position information and / or manually calibrated nacelle setting deviation of the target unit are inaccurate, it is determined that there is an operational risk to the target unit; wherein, the nacelle setting deviation is the angular difference between the initial installation position of the nacelle and the due north direction of the geographical azimuth.

[0017] According to a second aspect of the present disclosure, a wind turbine generator set operation risk identification device is provided, comprising: a statistics unit configured to determine a first turbulence statistics result based on operation data samples of the target generator set when sector management is not performed, wherein the first turbulence statistics result is obtained by statistically analyzing the turbulence conditions at various wind speed ranges when the target generator set does not perform sector management; a determination unit configured to determine, based on the first turbulence statistics result, whether the target generator set has sectors that require sector management but are not actually managed; and a risk judgment unit configured to determine that the target generator set has an operation risk if sectors that require sector management but are not actually managed exist.

[0018] According to a third aspect of the present disclosure, a computer-readable storage medium storing a computer program is provided, which, when executed by a processor, causes the processor to perform the wind turbine generator operation risk identification method as described above.

[0019] According to a fourth aspect of the present disclosure, an electronic device is provided, the electronic device comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, it causes the processor to perform the wind turbine generator operation risk identification method as described above.

[0020] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the wind turbine generator operation risk identification method as described above.

[0021] The wind turbine generator operation risk identification method, apparatus, electronic device, storage medium, and program product according to the exemplary embodiments of this disclosure can effectively identify whether the generator set has operation risks.

[0022] In the following description, some aspects and / or advantages of the general concept of this disclosure will be set forth, and other aspects and / or advantages will become apparent from the following description or from practice of the general concept of this disclosure. Attached Figure Description

[0023] These and / or other aspects and advantages of this application will become clearer and more readily understood from the following detailed description of embodiments of this application taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a first exemplary embodiment of the present disclosure;

[0025] Figure 2 A flowchart illustrating a method for identifying operational risks of a wind turbine generator according to a second exemplary embodiment of the present disclosure;

[0026] Figure 3 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a third exemplary embodiment of the present disclosure is provided.

[0027] Figure 4 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a fourth exemplary embodiment of the present disclosure is provided.

[0028] Figure 5 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a fifth exemplary embodiment of the present disclosure is provided.

[0029] Figure 6 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a sixth exemplary embodiment of the present disclosure is provided.

[0030] Figure 7 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a seventh exemplary embodiment of the present disclosure is provided.

[0031] Figure 8 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to an eighth exemplary embodiment of the present disclosure;

[0032] Figure 9 A structural block diagram of an operation risk identification device for a wind turbine generator set according to an exemplary embodiment of the present disclosure is shown. Detailed Implementation

[0033] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings, examples of which are illustrated in the drawings, wherein the same reference numerals always refer to the same parts. The embodiments will now be described with reference to the accompanying drawings in order to explain this disclosure.

[0034] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0035] It should be noted that the phrase "at least one of several items" in this disclosure refers to three parallel cases: "any one of the several items", "a combination of any number of the several items", and "all of the several items". For example, "including at least one of A and B" includes the following three parallel cases: (1) including A; (2) including B; (3) including A and B. As another example, "performing at least one of step one and step two" indicates the following three parallel cases: (1) performing step one; (2) performing step two; (3) performing both step one and step two.

[0036] As an example, the wind turbine generator operation risk identification method according to the exemplary embodiments of this disclosure can be executed by the generator controller or the wind farm's field-level controller, or by an electronic device with data processing capabilities, such as a terminal (e.g., a personal laptop, desktop computer, etc.) or a server (e.g., a standalone server, server cluster, cloud platform, etc.). This disclosure does not impose any limitations on this.

[0037] In wind farm projects with sector management, some or all of the turbines within the wind farm require sector management. For turbines requiring sector management, sector management is implemented when the incoming wind direction belongs to a specific sector (i.e., the sector under sector management) and the wind speed meets certain conditions (e.g., shutdown or power-limited operation). Otherwise, sector management is not implemented. To implement sector management for the turbines, sector management settings need to be configured. As an example, the relevant configuration parameters for sector management can include, but are not limited to: which sectors and wind speed ranges to implement power-limited operation, and which sectors and wind speed ranges to implement shutdown.

[0038] To avoid errors and situations such as incorrect true north calibration, incorrect sector management scope, and significant deviations between on-site wind parameters and those assessed during the wind farm design phase, this disclosure proposes a method for identifying unit operational risks through risk control indicators (especially sector risk control indicators related to sector management). The following section will combine these methods with... Figures 1-9 Please provide a detailed explanation.

[0039] Figure 1 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a first exemplary embodiment of the present disclosure is shown.

[0040] Reference Figure 1 In step S101, the first turbulence statistics result is determined based on the operating data sample of the target unit when sector management is not performed.

[0041] The first turbulence statistics were obtained by statistically analyzing the turbulence at various wind speed ranges when sector management was not implemented for the target unit.

[0042] As an example, firstly, multiple operational data samples of the target unit can be obtained over a period of time (each operational data sample corresponds to a different sampling time period); then, operational data samples with a data integrity higher than a preset percentage (e.g., 60% or 80%) are selected; next, the selected operational data samples are divided into operational data samples when sector management is not performed and operational data samples when sector management is performed, based on the flag bit of sector management execution.

[0043] Each operational data sample may include wind speed, wind direction, unit status information (e.g., unit status word variables), status cause information (e.g., power limitation cause variables, shutdown mode word variables), etc., at multiple sampling time points within a preset duration (e.g., 10 minutes).

[0044] As an example, the operating data samples of the target unit when sector management is not performed can first be divided into wind speed compartments. Then, the statistical value (e.g., 95th percentile) of the turbulence value (turbulence value = wind speed standard deviation / wind speed) of all operating data samples in each compartment is calculated as the turbulence value under the corresponding wind speed range for that compartment, thus obtaining the first turbulence statistical result. Here, a compartment is considered valid only when the total number of all operating data samples in a single compartment exceeds a certain number (e.g., 5).

[0045] As an example, wind speeds can be divided into compartments according to (2,3],(3,4]..., that is, compartments can be divided at 1 m / s intervals. The first turbulence statistics of each unit can be shown in Table 1.

[0046] Table 1 – Turbulence values ​​at various wind speed ranges when sector management is not implemented for the unit

[0047] wind speed 620407001 620407002 620407003 620407004 620407005 2.5 0.211 0.214 0.214 0.223 0.211 3.5 0.218 0.232 0.227 0.238 0.226 … … … … … … 18.5 0.080 0.086 0.099 0.099 0.091

[0048] In step S102, based on the first turbulence statistics, it is determined whether there are sectors in the target unit that require sector management but are not actually managed.

[0049] As an example, step S102 may include: steps S1021-S1023.

[0050] In step S1021, based on the first turbulence statistics, the first wind speed standard deviation is determined for each wind speed segment.

[0051] As an example, the first standard deviation of wind speed in each wind speed segment is the product of the wind speed value and the turbulence value in that wind speed segment. For example, the wind speed value in wind speed segment (2,3) is 2.5 m / s.

[0052] In step S1022, based on the first wind speed standard deviation under each wind speed segment, the first reference turbulence value of the target unit for extreme turbulence conditions under each wind speed segment is determined.

[0053] As an example, the first reference turbulence value I for extreme turbulence conditions at each wind speed range can be determined by the following formula (Extreme Turbulence Model ETM). ref1 :

[0054]

[0055] Where σ1 represents the first standard deviation of wind speed in this wind speed range, V ave V represents the average annual wind speed. hub This indicates the wind speed value for that wind speed range, c = 2 m / s.

[0056] In step S1023, if the number of wind speed segments with corresponding first reference turbulence values ​​higher than the first preset threshold exceeds the first number, it is determined that the target unit has sectors that require sector management but have not actually performed sector management.

[0057] As an example, it is possible to count only the number of wind speed segments within the wind speed range with the largest load (e.g., 7.5m to 14.5m) where the corresponding first reference turbulence value is higher than the first preset threshold to see if it exceeds the first number.

[0058] As an example, the specific values ​​of the first preset threshold and the first quantity can be set according to the actual situation and specific needs. For example, the first preset threshold can be set to 0.16 and the first quantity can be set to 3.

[0059] In step S103, if there are sectors that require sector management but are not actually managed, then the target unit is determined to have operational risks.

[0060] In addition, as an example, if the target unit has sectors that require sector management but are not actually managed, the maximum value of the first reference turbulence value corresponding to each wind speed range from 7.5m to 14.5m can be output as a reference for troubleshooting sector management issues of the target unit.

[0061] Figure 2 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a second exemplary embodiment of the present disclosure is shown.

[0062] Reference Figure 2 In step S201, the second turbulence statistics result is determined based on the operating data sample when the target unit performs sector management.

[0063] The second turbulence statistics were obtained by statistically analyzing the turbulence at various wind speed ranges when the target unit was under sector management.

[0064] As an example, the operational data samples of the target unit during sector management can first be divided into wind speed compartments. Then, the statistical value (e.g., 95th percentile) of the turbulence value (turbulence value = wind speed standard deviation / wind speed) of all operational data samples in each compartment is calculated as the turbulence value for the corresponding wind speed range of that compartment, thus obtaining the second turbulence statistical result. Here, a compartment is considered valid only when the total number of all operational data samples in a single compartment exceeds a certain number (e.g., 5).

[0065] In step S202, based on the second turbulence statistics, it is determined whether the target unit has sectors that do not require sector management but actually perform sector management.

[0066] As an example, step S202 may include: steps S2021-S2023.

[0067] In step S2021, based on the second turbulence statistics, the second wind speed standard deviation is determined for each wind speed segment.

[0068] As an example, the second standard deviation of wind speed in each wind speed segment is the product of the wind speed value and the turbulence value in that wind speed segment.

[0069] In step S2022, based on the second wind speed standard deviation under each wind speed segment, the second reference turbulence value for the target unit under extreme turbulence conditions under each wind speed segment is determined.

[0070] As an example, the second reference turbulence value I for extreme turbulence conditions at each wind speed range can be determined using the following formula (Extreme Turbulence Model ETM). ref2 :

[0071]

[0072] Where σ2 represents the second standard deviation of wind speed in this wind speed range, V ave V represents the average annual wind speed. hub This indicates the wind speed value for that wind speed range, c = 2 m / s.

[0073] In step S2023, if the number of wind speed segments with corresponding second reference turbulence values ​​lower than the second preset threshold exceeds the second number, it is determined that the target unit has sectors that do not require sector management but have actually performed sector management.

[0074] As an example, it is possible to count only the number of wind speed segments within the wind speed range with the largest load (e.g., 7.5m to 14.5m) where the corresponding second reference turbulence value is lower than the second preset threshold to see if it exceeds the second number.

[0075] As an example, the specific values ​​of the second preset threshold and the second quantity can be set according to the actual situation and specific needs. For example, the second preset threshold can be set to 0.14 and the first quantity can be set to 3.

[0076] In addition, as an example, if the target unit has sectors that do not require sector management but actually perform sector management, the minimum value of the second reference turbulence value corresponding to each wind speed range of 7.5m to 14.5m can be output as a reference for troubleshooting sector management problems of the target unit.

[0077] It should be understood that each turbine in the wind farm can be considered as a target turbine to determine whether there are sectors for each turbine that require sector management but are not actually managed, and whether there are sectors that do not require sector management but are actually managed. The statistical results, as shown in Table 2, can be output. For example, "0.18" for turbine "620407001" indicates that this turbine has sectors that require sector management but are not actually managed, and these sectors are within the range of 7.5m to 14.5m. The maximum value among the first reference turbulence values ​​corresponding to each wind speed segment is 0.18. For example, the " / " for unit "620407003" indicates that the unit does not have any sectors that require sector management but are not actually managed. For example, the "0.13" for unit "620407007" indicates that the unit has sectors that do not require sector management but are actually managed. The minimum value among the second reference turbulence values ​​corresponding to each wind speed segment from 7.5m to 14.5m is 0.18.

[0078] Table 2 – Unit Numbers Exceeding Design ETM

[0079] Serial Number Unit number <![CDATA[I of non-sector management data ref1 Maximum value]]> <![CDATA[I of sector management data ref2 Minimum value]]> 1 620407001 0.18 / 2 620407003 / 0.12 3 620407007 0.165 0.13

[0080] Figure 3 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a third exemplary embodiment of the present disclosure is shown.

[0081] Reference Figure 3In step S301, the operating data samples belonging to each wind speed segment under each wind direction interval are determined from the operating data samples when the target unit does not perform sector management.

[0082] As an example, the operating data samples of the target unit when sector management is not performed can be divided into wind direction and wind speed compartments to determine the operating data samples belonging to each wind speed segment under each wind direction interval.

[0083] For example, wind speed can be divided into compartments according to (2,3],(3,4]..., that is, compartments can be divided at intervals of 1m / s; wind direction can be divided into compartments according to (348.75,11.25],(11.25,33.75]..., with the unit being degrees, which can be divided into 16 wind direction intervals (that is, sectors).

[0084] Here, a sample warehouse is considered valid only when the total number of all running data samples in a single sample warehouse exceeds a certain number (e.g., 5).

[0085] In step S302, based on the operating data samples of each wind speed segment belonging to each wind direction interval, it is determined whether there are risky sectors when the target unit does not perform sector management.

[0086] As an example, step S302 may include: steps S3021-S3023.

[0087] In step S3021, based on the operational data samples of each wind speed segment belonging to each wind direction interval, the representative turbulence value of each wind speed segment under each wind direction interval is determined.

[0088] As an example, a representative turbulence value for a wind speed segment within a given wind direction interval can be determined based on the turbulence mean and turbulence standard deviation of all operational data samples belonging to each wind speed segment within each wind direction interval. For example, the representative turbulence value = turbulence mean + 1.28 times the turbulence standard deviation.

[0089] In step S3022, based on the representative turbulence values ​​of each wind speed segment under each wind direction interval, the reference turbulence value of the target unit under normal turbulence conditions is determined for each wind direction interval.

[0090] As an example, we can first determine the standard deviation σ of the wind speed for each wind speed segment within each wind direction range based on the representative turbulence value of each wind speed segment. The standard deviation σ is the wind speed value V for that wind speed segment. hub Multiply by the value representing turbulence; then, for each wind direction interval, calculate the wind speed standard deviation σ and wind speed value V based on each wind speed segment (preferably, each wind speed segment within the range of 6.5-14.5 m / s) within that wind direction interval. hub(For example, using the least squares method) the following equation (Normal Turbulence Model NTM) is fitted to calculate the reference turbulence value I for normal turbulence conditions in this wind direction range. ref :

[0091] σ=I ref (0.75V hub +b)

[0092] Where b = 5.6 m / s.

[0093] In step S3023, the wind direction range where the corresponding reference turbulence value is higher than the third preset threshold and the distribution of the corresponding operating data sample meets the preset conditions is determined as the risk sector when the target unit does not perform sector management.

[0094] As an example, the specific value of the third preset threshold can be set according to the actual situation and specific needs. For example, the third preset threshold can be set to 0.16.

[0095] As an example, preset conditions may include, but are not limited to: the maximum value of the wind speed corresponding to all valid sample cells in the wind direction interval is greater than a preset wind speed threshold (e.g., 8 m / s), and the wind frequency (total number of sector samples / total number of samples) in the wind direction interval exceeds a preset wind frequency threshold (e.g., 5%).

[0096] In step S303, if there are risky sectors when the target unit does not perform sector management, then it is determined that the target unit has operational risks.

[0097] In addition, as an example, each turbine in the wind farm can be taken as the target turbine, and the risk sectors when sector management is not performed for each turbine can be determined. The statistical results shown in Table 3 are output. "None" means that there are no risk sectors when the turbine does not perform sector management, and "0" means that there are risk sectors when the turbine does not perform sector management. The risk sector with the largest corresponding reference turbulence value is (348.75, 11.25).

[0098] Table 3 – Unit Numbers Exceeding Design NTM

[0099] Unit number Risk sector with the highest reference turbulence value 620407001 22.5 620407002 0 620407003 none 620407004 0

[0100] Figure 4 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a fourth exemplary embodiment of the present disclosure is shown.

[0101] Reference Figure 4 In step S401, based on the operating data sample of the target unit when sector management is not performed, the maximum representative turbulence value of the target unit in each wind speed segment within the rated wind speed range is determined.

[0102] As an example, the rated wind speed range can be from (rated wind speed - 2 m / s) to (rated wind speed + 2 m / s).

[0103] As an example, step S401 may include: step S4011-step S4012.

[0104] In step S4011, based on the operating data sample of the target unit when sector management is not performed, the representative turbulence values ​​of the target unit in each wind direction interval under each wind speed segment within the rated wind speed range are determined.

[0105] As an example, the operating data samples of the target unit when sector management is not performed can be divided into wind direction and wind speed compartments to determine the operating data samples belonging to each wind direction interval under each wind speed segment.

[0106] For example, wind speed can be divided into compartments according to (2,3],(3,4]..., that is, compartments can be divided at intervals of 1m / s; wind direction can be divided into compartments according to (348.75,11.25],(11.25,33.75]..., with the unit being degrees, which can be divided into 16 wind direction intervals (that is, sectors).

[0107] Here, a sample warehouse is considered valid only when the total number of all running data samples in a single sample warehouse exceeds a certain number (e.g., 5).

[0108] As an example, representative turbulence values ​​for each wind direction interval within each wind speed segment can be determined based on operational data samples belonging to each wind direction interval within each wind speed segment. For instance, the representative turbulence value for that wind direction interval within a wind speed segment can be determined based on the turbulence mean and turbulence standard deviation of all operational data samples belonging to each wind direction interval within each wind speed segment. For example, representative turbulence value = turbulence mean + 1.28 times turbulence standard deviation.

[0109] In step S4012, the maximum value among the representative turbulence values ​​corresponding to each wind speed segment is taken as the maximum representative turbulence value under that wind speed segment.

[0110] As an example, the maximum value among the representative turbulence values ​​of all wind direction intervals under each wind speed range can be used as the maximum representative turbulence value under that wind speed range.

[0111] As an example, the maximum representative turbulence values ​​of each turbine in the wind farm at various wind speed ranges are shown in Table 4.

[0112] Table 4 – Group ETM Table When Sector Management is Not Performed

[0113] wind speed 620407001 620407002 620407003 620407004 620407005 2.5 0.211 0.214 0.214 0.223 0.211 3.5 0.218 0.232 0.227 0.238 0.226 … … … … … … 18.5 0.080 0.086 0.099 0.099 0.091

[0114] In step S402, a reference turbulence value for the target unit under extreme turbulence conditions is determined based on the maximum representative turbulence value at each wind speed segment within the rated wind speed range.

[0115] As an example, the standard deviation σ of the wind speed in each wind speed range can be determined first based on the maximum representative turbulence value within the rated wind speed range. ETM Wind speed standard deviation σ ETM This is the product of the wind speed value for that wind speed range and the maximum representative turbulence value; then, it is based on the standard deviation σ of the wind speed in each wind speed range within the rated wind speed range. ETM and wind speed value V hub (For example, using the least squares method) the following equation (Extreme Turbulence Model, ETM) is fitted to calculate the reference turbulence value I for the target unit under extreme turbulence conditions within the rated wind speed range. ref(ETM) :

[0116]

[0117] Among them, V ave The annual average wind speed is represented by c = 2 m / s.

[0118] In step S403, the reference turbulence value of the target unit under extreme turbulence conditions under the rated wind speed range is determined by referring to the reference turbulence value of other units in the wind farm under extreme turbulence conditions within the rated wind speed range.

[0119] That is, by making a horizontal comparison with other units in the wind farm, it is determined whether the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range is abnormal (e.g., whether it is significantly higher).

[0120] In step S404, if the reference turbulence value of the target unit under extreme turbulence conditions is abnormal within the rated wind speed range, it is determined that the target unit has an operational risk.

[0121] As an example, the reference turbulence value I for each turbine in the wind farm under extreme turbulence conditions within the rated wind speed range. ref(ETM) As shown in Table 5.

[0122] Table 5 – All Units I ref(ETM) surface

[0123] Unit number <![CDATA[I ref( ETM ) <!-- 9 -->]]> 620407001 0.14 620407002 0.15 620407003 0.16 620407004 0.14 620407005 0.15 620407006 0.16

[0124] Figure 5 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a fifth exemplary embodiment of the present disclosure is shown.

[0125] Reference Figure 5In step S501, based on the operating data sample of the target unit when performing sector management, the wind speed segment and sector where the target unit actually performs sector management are statistically analyzed (that is, the wind speed segment and sector where sector management is actually performed).

[0126] The wind speed range and sector in which the target unit is actually located when sector management is implemented include: the sector to which the wind direction belongs and the wind speed range to which the wind speed belongs when the power is limited due to sector management; and the sector to which the wind direction belongs and the wind speed range to which the wind speed belongs when the unit is shut down due to sector management.

[0127] As an example, firstly, multiple operational data samples of the target unit can be obtained over a period of time (each operational data sample corresponds to a different sampling time period); then, operational data samples with a data integrity higher than a preset percentage (e.g., 60% or 80%) are selected; next, the selected operational data samples are divided into operational data samples when sector management is not performed and operational data samples when sector management is performed, based on the flag bit of sector management execution.

[0128] As an example, the operating data samples when performing sector management can be further divided into: operating data samples when power is limited due to sector management and operating data samples when the system is shut down due to sector management. Specifically, the operating data samples that meet the power-limited operation state and whose power-limiting reason is sector management and whose actual power is less than 110% of the power limit setting value are taken as the operating data samples when power is limited due to sector management; and the operating data samples that meet the shutdown state and whose shutdown reason is sector management and whose actual power is less than 110% of the power limit setting value are taken as the operating data samples when the system is shut down due to sector management.

[0129] As an example, the selected operational data samples can be divided into wind speed and wind direction categories, and then the actual execution of sector management in each wind direction interval within each wind speed segment can be statistically analyzed. For example, the statistical results can be shown in Table 6. If any sample category includes operational data samples of power-limited operation due to sector management, then the sample category is marked as 2, indicating that the wind speed segment and wind direction interval corresponding to the sample category belong to the wind speed segment and wind direction of the operation under power limitation due to sector management. If any sample category includes operational data samples of shutdown due to sector management, then the sample category is marked as 0, indicating that the wind speed segment and wind direction interval corresponding to the sample category belong to the wind speed segment and wind direction of the operation under shutdown due to sector management. If any sample category includes operational data samples of operation without sector management, then the sample category is marked as 1. Sample categories with no operational data samples are marked as empty "NA".

[0130] Table 6 – Actual Execution Switch Table

[0131] Wind speed / sector 0 22.5 45 …… 315 337.5 2.5 1 1 1 …… 1 1 3.5 1 1 1 …… 1 1 …… …… …… …… …… …… …… 18.5 1 1 1 …… 0 1 19.5 NA 1 1 …… 0 1 20.5 NA 1 1 …… 0 1

[0132] In step S502, the wind speed segment and sector in which sector management is actually performed are determined, and whether they match the sector management configuration parameters of the target unit.

[0133] In step S503, if the sector management configuration parameters of the target unit do not match, it is determined that the target unit has an operational risk.

[0134] Specifically, the sector management configuration parameters of the target unit specify which sectors and wind speed ranges will be subject to power-limited operation, and which sectors and wind speed ranges will be subject to shutdown. Since the target unit's controller manages sectors according to these configuration parameters, the wind speed ranges and sectors actually managed should normally match these parameters. If they do not match, it indicates an anomaly in sector management and a potential operational risk to the target unit.

[0135] Figure 6 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a sixth exemplary embodiment of the present disclosure is shown.

[0136] During on-site hoisting, due to terrain conditions and hoisting capacity limitations, the initial installation position of the nacelle on the tower could not be guaranteed to face due north. During operation, the wind direction data collected by the anemometer on the nacelle is relative to the nacelle's position. This makes it difficult to accurately determine the wind direction relative to the ground during unit sector management and control, causing difficulties for operational control and subsequent analysis.

[0137] To address the aforementioned issues, the existing solution involves manually calibrating the nacelle setup deviation. The nacelle setup deviation is the angular difference between the initial installation position of the nacelle and true north, also known as the true northerly correction value for the generator set. For example, the generator set can first be manually yawed to its initial 0-degree position. Then, using a compass, maintenance personnel can measure the angle between the generator set's nose and true north at the top of the nacelle, which is used as the manually calibrated nacelle setup deviation on-site.

[0138] Reference Figure 6 In step S601, the calculated nacelle setting deviation is obtained based on the operating data of the target unit and the meteorological data of the wind farm.

[0139] Here, meteorological data is a set of data that reflects the weather. For example, meteorological data may include, but is not limited to, wind speed and wind direction.

[0140] As an example, meteorological data for a wind farm can be data obtained from the wind measurement equipment at the wind farm, or it can be mesoscale data obtained using a mesoscale meteorological numerical model. For example, wind measurement equipment may include, but is not limited to: wind measurement towers, lidar, power prediction towers, and wind vanes. For example, mesoscale data can be obtained using a 3-Tier mesoscale meteorological model, and data sources may include, but are not limited to: NNRP, ERAI, MERRA, and MEERA2.

[0141] It should be understood that the target unit's operating data is operating data contemporaneous with the wind farm's meteorological data. As an example, operating data may include, but is not limited to, wind speed, nacelle yaw position, and windward angle (e.g., recorded by the nacelle wind vane).

[0142] As an example, step S601 may include: calculating the relative wind direction of the cabin based on the yaw position and the wind angle; and using the wind direction deviation between the relative wind direction and the wind direction in the meteorological data as the calculated cabin setup deviation. For example, relative wind direction = mod(-yaw position + wind angle, 360). For example, the mode of the wind direction deviation (i.e., the difference between the relative wind direction and the meteorological data wind direction) that meets certain conditions (e.g., small wind speed fluctuations) can be used as the calculated cabin setup deviation based on the relative wind direction time series and the wind direction time series of meteorological data during the same period.

[0143] In step S602, the angle between the calculated cabin setup deviation and the manually calibrated cabin setup deviation is determined.

[0144] As an example, the difference in angle = mod(manually calibrated cabin setup deviation - calculated cabin setup deviation, 360).

[0145] In step S603, if the angle difference reaches the fourth preset threshold, it is determined that the target unit has an operational risk.

[0146] Specifically, if the angle difference reaches the fourth preset threshold, it is determined that the manually calibrated cabin settings deviation of the target unit is unreliable and may have problems, which will pose an operational risk to the target unit.

[0147] In addition, as an example, each turbine in the wind farm can be taken as the target turbine, and the angle between the calculated nacelle setting deviation of each turbine and the manually calibrated nacelle setting deviation can be obtained. The specific situation of each turbine can be output in the form of Table 7.

[0148] Table 7 – Deviation of True North Calibration Value

[0149] Unit number Manually calibrated cabin setup deviations Calculated cabin setup deviation The difference in angle 620407001 37.21 36.21 1 620407002 -89.49 -39.49 50 620407003 29.15 19.15 10

[0150] Figure 7A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to a seventh exemplary embodiment of the present disclosure is shown.

[0151] Reference Figure 7 In step S701, the number of wind direction intervals with the highest wind frequency of the target unit is counted.

[0152] As an example, wind direction intervals can be divided according to (348.75, 11.25], (11.25, 33.75]... in degrees, which can be divided into 16 wind direction intervals (i.e., sectors).

[0153] As an example, the true northerly wind direction at the target unit can be obtained over a period of time, and then the number of preset wind direction intervals (e.g., 2) with the highest wind frequency at the target unit can be counted.

[0154] It should be understood that the true north wind direction used here is a reliable and accurate true north wind direction. For example, it could be the true north wind direction obtained from meteorological data of the wind farm; or, it could be the true north direction calculated based on nacelle setup deviations, yaw positions, and wind angles.

[0155] In addition, as an example, each turbine in the wind farm can be taken as the target turbine, and the sectors in which each turbine ranks first or second in wind frequency among the 16 sectors can be obtained. The specific information of each turbine can be output in the form of Table 8.

[0156] Table 8 – Top two sectors by wind direction frequency

[0157] Unit number Sector 1 Sector 1 percentage Sector 2 Sector 2 percentage 620407001 202.5 0.17459 247.5 0.169994 620407002 180 0.239495 202.5 0.168343 620407003 180 0.234041 202.5 0.19565

[0158] In step S702, it is determined whether the main wind energy direction of the target unit evaluated when designing the wind farm belongs to the previously preset number of wind direction intervals.

[0159] In step S703, if the wind direction does not belong to the previously preset number of wind direction intervals, it is determined that the prevailing wind direction assessed during the design period for the target unit is inconsistent with the prevailing wind direction assessed during the operation period.

[0160] Figure 8 A flowchart illustrating a method for identifying operational risks of a wind turbine generator set according to an eighth exemplary embodiment of the present disclosure is shown.

[0161] Reference Figure 8 In step S801, the wind frequency rose diagram of the target unit is determined based on the operating data of the target unit.

[0162] In step S802, the wind frequency rose diagram of the wind farm is determined based on the meteorological data of the wind farm.

[0163] In step S803, by comparing the differences between the wind frequency rose diagram of the target unit and the wind frequency rose diagram of the wind farm, it is determined whether the yaw position information of the target unit and / or the manually calibrated nacelle setting deviation are accurate.

[0164] As an example, if there is a first type of difference between the wind frequency rose diagram of the target turbine and the wind frequency rose diagram of the wind farm (e.g., similar shapes, one with a certain rotation angle relative to the other), it can be determined that the difference is due to inaccurate nacelle setting deviations in manual calibration; if there is a second type of difference between the wind frequency rose diagram of the target turbine and the wind frequency rose diagram of the wind farm (e.g., significant shape differences), it can be determined that the difference is due to inaccurate yaw position information of the target turbine.

[0165] As an example, inaccurate yaw position information may be caused by a problem with the yaw encoder or rotary encoder.

[0166] In step S804, if the yaw position information and / or manually calibrated cabin setting deviation of the target unit are inaccurate, it is determined that there is an operational risk to the target unit.

[0167] The recognition results obtained through the above exemplary embodiments can be summarized as shown in Table 9.

[0168] Table 9 – Summary of Conclusions for All Units

[0169]

[0170] According to exemplary embodiments of this disclosure, wind farm operational risks are identified through unit operation data, wind farm design phase data, and wind farm meteorological data. This includes assessing the accuracy of true north calibration results, whether the actual sector management range matches the design, the rationality of the sector management range, whether the actual sector management range matches the risk sectors, whether the prevailing wind direction at the site matches the prevailing wind direction assessed during the design phase, whether extreme turbulence conditions at the site exceed the customized ETM when sector management is not implemented, whether the turbulence at the site exceeds the turbulence assessed during the design phase when sector management is not implemented, and whether there are deviations in yaw position information. The identification results based on the above exemplary embodiments can be used to identify risky wind farm locations, correct problems, and resolve them. For example, if the risk is due to an unreasonable sector management range design, the sector management range needs to be redesigned; if the actual sector management range at the site differs from the designed sector management range, the issue of execution deviation needs to be addressed.

[0171] Figure 9 A structural block diagram of an operation risk identification device for a wind turbine generator set according to an exemplary embodiment of the present disclosure is shown.

[0172] like Figure 9As shown, the wind turbine generator set operation risk identification device according to an exemplary embodiment of the present disclosure includes: a statistics unit 100, a determination unit 200, and a risk judgment unit 300.

[0173] The statistical unit 100 is configured to determine a first turbulence statistical result based on the operating data sample of the target unit when sector management is not performed. The first turbulence statistical result is obtained by statistically analyzing the turbulence situation under each wind speed range when the target unit is not performing sector management.

[0174] The determination unit 200 is configured to determine, based on the first turbulence statistics, whether there are sectors in the target unit that require sector management but are not actually managed.

[0175] The risk assessment unit 300 is configured to determine that the target unit has an operational risk if there are sectors that require sector management but have not actually been implemented.

[0176] As an example, the determining unit 200 can be configured to: determine the first wind speed standard deviation under each wind speed segment based on the first turbulence statistics; determine the first reference turbulence value of the target unit under each wind speed segment for extreme turbulence conditions based on the first wind speed standard deviation under each wind speed segment; if the number of wind speed segments with the corresponding first reference turbulence value higher than the first preset threshold exceeds the first number, then it is determined that the target unit has sectors that need to be managed by sector management but have not actually been managed.

[0177] As an exemplary embodiment, the statistics unit 100 may also be configured to: determine a second turbulence statistics result based on the operating data sample of the target unit when performing sector management, wherein the second turbulence statistics result is obtained by statistically analyzing the turbulence situation under each wind speed segment when the target unit performs sector management; the determination unit 200 may also be configured to: determine, based on the second turbulence statistics result, whether there are sectors in the target unit that do not need to perform sector management but actually perform sector management.

[0178] As an example, the determination unit 200 can be configured to: determine whether the target unit has sectors that do not require sector management but actually perform sector management based on the second turbulence statistics results, including: determining the second wind speed standard deviation for each wind speed segment based on the second turbulence statistics results; determining the second reference turbulence value for the target unit under extreme turbulence conditions for each wind speed segment based on the second wind speed standard deviation for each wind speed segment; if the number of wind speed segments with the corresponding second reference turbulence value lower than the second preset threshold exceeds the second number, then it is determined that the target unit has sectors that do not require sector management but actually perform sector management.

[0179] As an exemplary embodiment, the statistics unit 100 may also be configured to: determine, from the operating data samples of the target unit when sector management is not performed, the operating data samples belonging to each wind speed segment under each wind direction interval; the determination unit 200 may also be configured to: determine, based on the operating data samples belonging to each wind speed segment under each wind direction interval, whether there are risky sectors when the target unit does not perform sector management; the risk judgment unit 300 may also be configured to: if there are risky sectors when the target unit does not perform sector management, then determine that the target unit has an operational risk.

[0180] As an example, the determining unit 200 can be configured to: determine the representative turbulence value of each wind speed segment in each wind direction interval based on the operating data samples belonging to each wind speed segment in each wind direction interval; determine the reference turbulence value of the target unit for normal turbulence conditions in each wind direction interval based on the representative turbulence value of each wind speed segment in each wind direction interval; and determine the wind direction intervals whose corresponding reference turbulence value is higher than a third preset threshold and whose distribution of the corresponding operating data samples meets preset conditions as risk sectors when the target unit does not perform sector management.

[0181] As an exemplary embodiment, the statistics unit 100 may further be configured to: determine the maximum representative turbulence value of the target unit under each wind speed segment within the rated wind speed range based on the operating data sample of the target unit when sector management is not performed; and determine the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range based on the maximum representative turbulence value under each wind speed segment within the rated wind speed range. The determination unit 200 may further be configured to: determine whether the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range is abnormal by referring to the reference turbulence values ​​of other units in the wind farm under extreme turbulence conditions within the rated wind speed range. The risk judgment unit 300 may further be configured to: determine that the target unit has an operational risk if the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range is abnormal.

[0182] As an example, the statistics unit 100 can be configured to: determine the representative turbulence values ​​of each wind direction interval under each wind speed segment within the rated wind speed range of the target unit based on the operating data sample when the target unit does not perform sector management; and take the maximum value of the representative turbulence values ​​corresponding to each wind speed segment as the maximum representative turbulence value under that wind speed segment.

[0183] As an exemplary embodiment, the statistics unit 100 may further be configured to: based on the operating data sample of the target unit when performing sector management, count the wind speed segment and sector in which the target unit actually performs sector management; the determination unit 200 may further be configured to: determine whether the wind speed segment and sector in which the target unit actually performs sector management matches the sector management-related configuration parameters of the target unit; the risk judgment unit 300 may further be configured to: if it does not match the sector management-related configuration parameters of the target unit, determine that the target unit has an operational risk; wherein, the wind speed segment and sector in which the target unit actually performs sector management includes: the sector to which the wind direction belongs and the wind speed segment to which the wind speed belongs when the power is limited due to sector management; and the sector to which the wind direction belongs and the wind speed segment to which the wind speed belongs when the unit is shut down due to sector management.

[0184] As an exemplary embodiment, the statistics unit 100 may also be configured to: count the number of wind direction intervals with the highest wind frequency of the target unit; the determination unit 200 may also be configured to: determine whether the main wind energy direction of the target unit evaluated during the design of the wind farm belongs to the number of wind direction intervals; if it does not belong to the number of wind direction intervals, then determine that the main wind energy direction evaluated during the design period for the target unit is inconsistent with the main wind energy direction evaluated during the operation period.

[0185] As an exemplary embodiment, the statistics unit 100 may also be configured to: obtain a calculated nacelle setup deviation based on the operating data of the target unit and the meteorological data of the wind farm, wherein the nacelle setup deviation is the angular difference between the initial installation position of the nacelle and the due north direction of the geographical orientation; the determination unit 200 may also be configured to: determine the angle difference between the calculated nacelle setup deviation and the manually calibrated nacelle setup deviation; the risk judgment unit 300 may also be configured to: determine that the target unit has an operational risk if the angle difference reaches a fourth preset threshold.

[0186] As an exemplary embodiment, the statistics unit 100 may further be configured to: determine the wind rose diagram of the target unit based on the operating data of the target unit; determine the wind rose diagram of the wind farm based on the meteorological data of the wind farm; the determination unit 200 may further be configured to: determine whether the yaw position information and / or manually calibrated nacelle setting deviation of the target unit are accurate by comparing the difference between the wind rose diagram of the target unit and the wind rose diagram of the wind farm; the risk judgment unit 300 may further be configured to: determine that there is an operational risk to the target unit if the yaw position information and / or manually calibrated nacelle setting deviation of the target unit are inaccurate; wherein, the nacelle setting deviation is the angular difference between the initial installation position of the nacelle and the due north direction of the geographical orientation.

[0187] It should be understood that the specific processing performed by the wind turbine generator operation risk identification device according to the exemplary embodiments of this disclosure has been referenced. Figures 1 to 8 A detailed description has been provided, and the relevant details will not be repeated here.

[0188] It should be understood that the various units in the wind turbine generator operation risk identification device according to the exemplary embodiments of this disclosure can be implemented as hardware components and / or software components. Those skilled in the art can implement the various units, for example, using field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), based on the processes performed by each defined unit.

[0189] An electronic device according to an exemplary embodiment of the present disclosure includes a processor (not shown) and a memory (not shown), wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the wind turbine generator operation risk identification method as described in the exemplary embodiment above.

[0190] As an example, the electronic device may be an electronic device with data processing capabilities. For example, the electronic device may be a unit controller or a wind farm-level controller, a terminal (such as a personal laptop, desktop computer, etc.), or a server (such as a standalone server, server cluster, cloud platform, etc.). This disclosure does not limit this aspect.

[0191] According to exemplary embodiments of this disclosure, a computer-readable storage medium storing instructions may also be provided, wherein when the instructions are executed by at least one processor, they cause at least one processor to perform the wind turbine generator operation risk identification method as described in the exemplary embodiments above. Examples of computer-readable storage media herein include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disc storage, hard disk drive (HDD), solid-state drive (SSD), card storage (such as multimedia cards, secure digital (SD) cards, or ultra-fast digital (XD) cards), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the aforementioned computer-readable storage medium can run in an environment deployed in computer devices such as clients, hosts, agent devices, servers, etc. Furthermore, in one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system, such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner through one or more processors or computers.

[0192] According to exemplary embodiments of the present disclosure, a computer program product may also be provided, wherein the instructions in the computer program product are executable by at least one processor to perform the wind turbine generator operation risk identification method as described in the exemplary embodiments above.

[0193] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0194] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for identifying operational risks of wind turbine generator sets, characterized in that, include: Based on the operating data sample of the target unit when sector management is not performed, the first turbulence statistical result is determined. The first turbulence statistical result is obtained by statistically analyzing the turbulence situation under each wind speed range when the target unit is not performing sector management. Based on the first turbulence statistics, determine whether there are sectors in the target unit that require sector management but are not actually managed. If there are sectors that require sector management but are not actually managed, then the target unit is identified as having operational risks.

2. The operational risk identification method according to claim 1, characterized in that, Based on the first turbulence statistics, the steps to determine whether the target unit has sectors that require sector management but are not actually managed include: Based on the first turbulence statistics, the first wind speed standard deviation is determined for each wind speed range; Based on the first wind speed standard deviation at each wind speed range, the first reference turbulence value for the target unit under extreme turbulence conditions at each wind speed range is determined. If the number of wind speed segments with corresponding first reference turbulence values ​​higher than the first preset threshold exceeds the first number, it is determined that the target unit has sectors that require sector management but have not actually performed sector management.

3. The operational risk identification method according to claim 1, characterized in that, Also includes: Based on the operational data samples of the target unit when performing sector management, the second turbulence statistical result is determined. The second turbulence statistical result is obtained by statistically analyzing the turbulence situation under each wind speed range when the target unit performs sector management. Based on the second turbulence statistics, determine whether there are sectors in the target unit that do not require sector management but actually perform sector management.

4. The operational risk identification method according to claim 3, characterized in that, Based on the second turbulence statistics, the steps to determine whether the target unit has sectors that do not require sector management but are actually subject to it include: Based on the second turbulence statistics, the second wind speed standard deviation is determined for each wind speed range; Based on the second wind speed standard deviation at each wind speed range, the second reference turbulence value for the target unit under extreme turbulence conditions at each wind speed range is determined. If the number of wind speed segments with corresponding second reference turbulence values ​​lower than the second preset threshold exceeds the second number, it is determined that the target unit has sectors that do not require sector management but have actually performed sector management.

5. The operational risk identification method according to claim 1, characterized in that, Also includes: From the operational data samples of the target unit when sector management was not performed, determine the operational data samples belonging to each wind speed segment under each wind direction interval; Based on the operational data samples of each wind speed segment under each wind direction interval, determine whether there are risky sectors when the target unit does not perform sector management; If risky sectors exist in the target unit when sector management is not implemented, then the target unit is determined to have operational risks.

6. The operational risk identification method according to claim 5, characterized in that, Based on operational data samples belonging to each wind speed segment within each wind direction interval, the steps to determine whether there are risky sectors when the target unit is not implementing sector management include: Based on the operational data samples of each wind speed segment under each wind direction interval, the representative turbulence value of each wind speed segment under each wind direction interval is determined. Based on the representative turbulence values ​​of each wind speed range under each wind direction range, the reference turbulence values ​​of the target unit under normal turbulence conditions are determined for each wind direction range. The wind direction range where the corresponding reference turbulence value is higher than the third preset threshold and the distribution of the corresponding operating data sample meets the preset conditions is identified as the risk sector when the target unit does not perform sector management.

7. The operational risk identification method according to claim 1, characterized in that, Also includes: Based on the operating data sample of the target unit when sector management is not performed, the maximum representative turbulence value of the target unit in each wind speed segment within the rated wind speed range is determined; Based on the maximum representative turbulence value at each wind speed range within the rated wind speed range, the reference turbulence value for the target unit under extreme turbulence conditions within the rated wind speed range is determined. By referring to the reference turbulence values ​​of other units in the wind farm under extreme turbulence conditions within the rated wind speed range, it can be determined whether the reference turbulence value of the target unit under extreme turbulence conditions within the rated wind speed range is abnormal. If the reference turbulence value of the target unit under extreme turbulence conditions is abnormal within the rated wind speed range, then the target unit is determined to have an operational risk.

8. The operational risk identification method according to claim 7, characterized in that, Based on the operating data sample of the target unit when sector management is not implemented, the steps to determine the maximum representative turbulence value of the target unit in each wind speed range within the rated wind speed range include: Based on the operating data sample of the target unit when sector management is not performed, the representative turbulence values ​​of the target unit in each wind direction range under each wind speed segment within the rated wind speed range are determined. The maximum value among the representative turbulence values ​​corresponding to each wind speed range is taken as the maximum representative turbulence value under that wind speed range.

9. The operational risk identification method according to claim 1, characterized in that, Also includes: Based on the operational data samples of the target unit when performing sector management, the wind speed range and sector in which the target unit actually performed sector management are statistically analyzed; Determine whether the wind speed range and sector in which sector management is actually performed match the relevant configuration parameters for sector management of the target unit; If the sector management configuration parameters of the target unit do not match, it is determined that the target unit has an operational risk. The wind speed segment and sector in which the target unit is actually located when sector management is implemented include: the sector to which the wind direction belongs and the wind speed segment to which the wind speed belongs when the power is limited due to sector management; and the sector to which the wind direction belongs and the wind speed segment to which the wind speed belongs when the unit is shut down due to sector management.

10. The operational risk identification method according to claim 1, characterized in that, Also includes: The number of wind direction intervals with the highest wind frequency for the target unit is pre-set; Determine whether the prevailing wind direction of the target turbines evaluated during the design of the wind farm belongs to the pre-preset number of wind direction intervals; If it does not belong to the previously preset number of wind direction intervals, then it is determined that the prevailing wind direction assessed during the design period for the target unit is inconsistent with the prevailing wind direction assessed during the operation period.

11. The operational risk identification method according to claim 1, characterized in that, Also includes: Based on the operating data of the target unit and the meteorological data of the wind farm, the calculated nacelle setting deviation is obtained, where the nacelle setting deviation is the angular difference between the initial installation position of the nacelle and the due north direction of the geographical orientation. Determine the angle difference between the calculated cabin setup deviation and the manually calibrated cabin setup deviation; If the angle difference reaches the fourth preset threshold, it is determined that the target unit has an operational risk.

12. The operational risk identification method according to claim 1, characterized in that, Also includes: Based on the operating data of the target unit, determine the wind frequency rose diagram of the target unit; Based on meteorological data from the wind farm, the wind frequency rose diagram of the wind farm is determined; By comparing the differences between the wind frequency rose diagram of the target unit and the wind frequency rose diagram of the wind farm, it is determined whether the yaw position information of the target unit and / or the manually calibrated nacelle setting deviation are accurate. If the yaw position information and / or manually calibrated cabin setting deviations of the target unit are inaccurate, then the target unit is determined to have an operational risk. Among them, the cabin setup deviation is the angular difference between the initial installation position of the cabin and the due north direction of the geographical orientation.

13. A device for identifying operational risks of a wind turbine generator set, characterized in that, include: The statistical unit is configured to determine a first turbulence statistical result based on the operating data sample of the target unit when sector management is not performed. The first turbulence statistical result is obtained by statistically analyzing the turbulence situation under each wind speed range when the target unit is not performing sector management. The determination unit is configured to determine, based on the first turbulence statistics, whether there are sectors in the target unit that require sector management but are not actually managed. The risk assessment unit is configured to determine that the target unit has an operational risk if there are sectors that require sector management but have not actually been managed.

14. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it causes the processor to perform the wind turbine generator operation risk identification method as described in any one of claims 1 to 12.

15. An electronic device, characterized in that, The electronic device includes: processor; A memory storing a computer program that, when executed by a processor, causes the processor to perform the wind turbine generator operation risk identification method as described in any one of claims 1 to 12.

16. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the wind turbine generator operation risk identification method as described in any one of claims 1 to 12.

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