Fan self-power-limiting intelligent identification method and related device
Through a two-stage screening method, combined with theoretical power generation and wind speed change slope analysis, the self-power limiting phenomenon of wind turbines can be accurately identified, solving the problem of high misjudgment rate in wind farms and improving the scientificity and accuracy of power generation efficiency monitoring.
Patent Information
- Application Number
- CN202511060665.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-30
- Publication Date
- 2025-09-12
AI Technical Summary
Existing technologies make it difficult to accurately distinguish between equipment failures and strategic power restrictions in wind farms, resulting in a high misjudgment rate and an inability to effectively identify the hidden causes of wind turbine self-power restrictions, affecting the scientific nature and accuracy of power generation efficiency monitoring.
A two-stage screening method is adopted. First, the first-level screening is performed based on the theoretical power generation, historical average actual power generation and wind speed. Then, the second-level screening is performed through the power-wind speed change slope of adjacent wind speed segments to eliminate erroneous data and lock the self-limiting power characteristic points.
It improves the scientificity and accuracy of wind farm power generation efficiency monitoring, reduces the misjudgment rate, and enhances the wind farm's power generation efficiency optimization and equipment health management capabilities.
Smart Images
Figure CN120626431A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of wind farm operation monitoring, and relates to a method for intelligently identifying wind turbine self-limiting power and a related device. Background Art
[0002] In the process of large-scale development of the wind power industry, the actual power generation capacity of wind farms is affected by multiple factors, including the status of the wind turbines themselves, environmental conditions, and scheduling strategies. When a wind turbine experiences self-limiting power (i.e., the equipment has the power generation capacity but is not operating at full capacity), traditional monitoring methods often rely on a single power threshold or empirical rules for judgment. This leads to high misjudgment rates, inability to distinguish between equipment failures and strategic power restrictions, and difficulty in locating the causes of hidden power restrictions. Therefore, it is urgent to build a set of intelligent self-limiting power identification methods that integrate multi-dimensional data, dynamic characteristic identification, and in-depth cause diagnosis to identify erroneous data and improve the scientificity and accuracy of wind farm power generation efficiency monitoring. Summary of the Invention
[0003] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method and related device for intelligently identifying wind turbine self-limiting power. This method and related device can detect erroneous data of wind turbine monitoring and improve the scientificity and accuracy of wind farm power generation efficiency monitoring.
[0004] To achieve the above object, the present invention discloses a method for intelligently identifying wind turbine self-limiting power, comprising:
[0005] Obtain wind turbine power generation data and corresponding wind speed;
[0006] Perform a first-level screening of wind turbine power generation data and corresponding wind speeds based on theoretical power generation, historical average actual power generation, and wind speed;
[0007] The wind turbine power generation data and the corresponding wind speed are screened at the second level according to the change slope of power-wind speed in adjacent wind speed segments.
[0008] Furthermore, the process of performing a first-level screening of the wind turbine power generation data and the corresponding wind speed according to the theoretical power generation, the historical average actual power generation and the wind speed is as follows:
[0009] Determine whether the wind turbine's power generation data and corresponding wind speed simultaneously meet the following conditions:
[0010] Actual power generation < theoretical power generation × 60%
[0011] Actual power generation < historical average actual power generation × 60%
[0012] Actual power generation>0
[0013] Wind speed>3m / s
[0014] When the power generation data and the corresponding wind speed of any wind turbine do not meet the above conditions, the power generation data and the corresponding wind speed of the wind turbine are deleted.
[0015] Furthermore, the process of performing secondary screening on the wind turbine power generation data and the corresponding wind speed according to the change slope of power-wind speed in adjacent wind speed segments is as follows:
[0016] Cluster the wind turbine power generation data according to each preset interval of wind speed to form a wind speed segment sequence;
[0017] Calculate the average wind speed and the corresponding average power of each wind speed segment in the wind speed segment sequence;
[0018] Calculate the slope of change of average power-average wind speed in adjacent wind speed segments;
[0019] When the slope is greater than or equal to a preset slope threshold, the power generation data of the wind turbine and the corresponding wind speed corresponding to the wind speed segment are deleted.
[0020] Furthermore, the slope threshold is 20.
[0021] The present invention discloses a wind turbine self-limiting intelligent identification system, comprising:
[0022] An acquisition module is used to obtain the power generation data of the wind turbine and the corresponding wind speed;
[0023] The first-level screening module is used to perform first-level screening on the wind turbine power generation data and the corresponding wind speed based on the theoretical power generation, the historical average actual power generation and the wind speed;
[0024] The secondary screening module is used to perform secondary screening on the wind turbine's power generation data and the corresponding wind speed according to the change slope of the power-wind speed in adjacent wind speed sections.
[0025] Furthermore, the process of performing a first-level screening of the wind turbine power generation data and the corresponding wind speed according to the theoretical power generation, the historical average actual power generation and the wind speed is as follows:
[0026] Determine whether the wind turbine's power generation data and corresponding wind speed simultaneously meet the following conditions:
[0027] Actual power generation < theoretical power generation × 60%
[0028] Actual power generation < historical average actual power generation × 60%
[0029] Actual power generation>0
[0030] Wind speed>3m / s
[0031] When the power generation data and the corresponding wind speed of any wind turbine do not meet the above conditions, the power generation data and the corresponding wind speed of the wind turbine are deleted.
[0032] Furthermore, the process of performing secondary screening on the wind turbine power generation data and the corresponding wind speed according to the change slope of power-wind speed in adjacent wind speed segments is as follows:
[0033] Cluster the wind turbine power generation data according to each preset interval of wind speed to form a wind speed segment sequence;
[0034] Calculate the average wind speed and the corresponding average power of each wind speed segment in the wind speed segment sequence;
[0035] Calculate the slope of change of average power-average wind speed in adjacent wind speed segments;
[0036] When the slope is greater than or equal to a preset slope threshold, the power generation data of the wind turbine and the corresponding wind speed corresponding to the wind speed segment are deleted.
[0037] Furthermore, the slope threshold is 20.
[0038] The present invention discloses a computer device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the wind turbine self-limiting intelligent identification method are implemented.
[0039] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the wind turbine self-limiting intelligent identification method are realized.
[0040] The present invention has the following beneficial effects:
[0041] During specific operation, the wind turbine self-limiting intelligent identification method and related devices described in the present invention perform a first-level screening of the wind turbine power generation data and the corresponding wind speed according to the theoretical power generation, the historical average actual power generation and the wind speed; and perform a second-level screening of the wind turbine power generation data and the corresponding wind speed according to the power-wind speed change slope of adjacent wind speed segments. Through the two-level screening, the erroneous data of the wind turbine monitoring can be eliminated, the scientificity and accuracy of the wind farm power generation efficiency monitoring can be improved, and the problem of judging the non-full power state caused by equipment abnormalities, control strategies or environmental coupling factors in the wind farm can be solved, providing core technical support for wind farm power prediction, equipment health management and power generation efficiency optimization. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:
[0043] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION
[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0045] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.
[0046] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.
[0047] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.
[0048] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.
[0049] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.
[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.
[0051] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.
[0052] Example 1
[0053] refer to Figure 1 The wind turbine self-limiting intelligent identification method of the present invention comprises the following steps:
[0054] 1) Basic conditions screening (coarse screening);
[0055] The present invention uses four constraints to eliminate operating data that is obviously not self-limited and retain highly suspicious samples. The technical logic and relevance of each condition are as follows:
[0056] 11) Actual power generation < theoretical power generation × 60%;
[0057] Theoretical power generation is calculated using the unit's factory power curve and real-time wind speed, reflecting the maximum power capacity under specific wind conditions. This threshold is based on industry statistics: the actual power fluctuation range of a normally operating unit is typically between 80% and 105% of the theoretical value, while this ratio is often below 60% under self-limiting conditions. This effectively distinguishes minor power losses from significant anomalies.
[0058] 12) Actual power generation < historical average actual power generation × 60%;
[0059] Incorporating the turbine's own historical data as a reference eliminates the impact of individual differences. The historical average takes the average power in the same wind speed range over the last 30 days of normal operation to avoid baseline drift caused by seasonal changes and turbine aging. When both conditions 1 and 2 are met, misjudgments caused by deviations from the theoretical power curve can be eliminated.
[0060] 13) Actual power generation > 0;
[0061] Excluding the complete shutdown state of the unit (such as maintenance shutdown or fault locking), the power loss in this state is an explicit fault, which is essentially different from the "implicit output limitation" feature of self-limiting power.
[0062] 14) Wind speed > 3m / s;
[0063] Based on aerodynamic characteristics: When wind speeds fall below 3m / s, most wind turbines are in a critical startup state, resulting in extremely low power output (typically less than 5% of rated power). This power shortage is a normal physical characteristic, not a case of self-limiting. This condition can filter out approximately 20% of invalid data.
[0064] The above four conditions must be met simultaneously, forming a "logical AND" relationship. The filtered data samples will enter the second stage of analysis, at which point the suspicion of self-limiting power in the sample group will increase to over 70%.
[0065] 2) Dynamic feature screening (fine screening);
[0066] In this phase, the power-wind speed segmentation characteristics are analyzed, normal power ramp data is eliminated, and the self-limiting characteristic point is locked. The core logic is that during normal operation, the wind turbine power increases with the wind speed in a gradient manner (stable slope), while the slope will suddenly change during self-limiting. The specific process is as follows:
[0067] 21) Data window and clustering processing;
[0068] Using a sliding time window technique, we selected three consecutive days of operating data (with a sampling frequency of 1 minute / time) to ensure coverage of the complete wind speed variation cycle (such as the difference in wind conditions between day and night). We clustered the wind speeds at intervals of 1 m / s to form a sequence of wind speed segments (such as 3-4 m / s, 4-5 m / s, etc.), with each cluster containing all power data points within that wind speed segment.
[0069] 22) Extraction of characteristic parameters;
[0070] Calculate the statistical characteristics of each wind speed segment:
[0071] Average wind speed: the average wind speed of all sampling points in the section, representing the typical wind conditions in the section; Average power: the average power of all sampling points in the section, eliminating the impact of instantaneous fluctuations;
[0072] Data volume within a segment: Ensure that each cluster contains at least 50 valid data points (approximately 4 hours of sampling) to avoid statistical bias due to small samples.
[0073] 23) Slope feature judgment
[0074] Calculate the power-wind speed change slope of adjacent wind speed segments, that is, the difference between the average power of the latter segment and the average power of the former segment, divided by the difference in wind speed segments.
[0075] During normal power ramping, the slope conforms to the power curve change rate designed for the unit. For example, the slope of a doubly fed wind turbine below the rated wind speed is usually stable between 15-30; however, in the self-limiting power state, the slope will show a significant jump due to limited output.
[0076] The present invention sets a slope threshold of ±20. When the slope of adjacent segments exceeds this range, the sampling point within that wind speed segment is determined to be a self-limiting point. This threshold is based on an analysis of the power curves of 10 mainstream wind turbine models and covers 95% of the normal slope fluctuation range.
[0077] This technology uses "two-stage screening". The first stage accurately locks potential power-limiting conditions (eliminating interference from normal environmental fluctuations). The second stage deeply filters non-power-limiting dynamic processes (such as ramping and short-term power regulation). This increases the accuracy of self-power-limiting identification by more than 30% compared with traditional methods, and reduces the missed detection rate to below 5%.
[0078] Example 2
[0079] The wind turbine self-limiting intelligent identification system of the present invention comprises:
[0080] An acquisition module is used to obtain the power generation data of the wind turbine and the corresponding wind speed;
[0081] The first-level screening module is used to perform first-level screening on the wind turbine power generation data and the corresponding wind speed based on the theoretical power generation, the historical average actual power generation and the wind speed;
[0082] The secondary screening module is used to perform secondary screening on the wind turbine's power generation data and the corresponding wind speed according to the change slope of the power-wind speed in adjacent wind speed sections.
[0083] In this embodiment, the process of performing a first-level screening of the wind turbine power generation data and the corresponding wind speed based on the theoretical power generation, the historical average actual power generation, and the wind speed is as follows:
[0084] Determine whether the wind turbine's power generation data and corresponding wind speed simultaneously meet the following conditions:
[0085] Actual power generation < theoretical power generation × 60%
[0086] Actual power generation < historical average actual power generation × 60%
[0087] Actual power generation>0
[0088] Wind speed>3m / s
[0089] When the power generation data and the corresponding wind speed of any wind turbine do not meet the above conditions, the power generation data and the corresponding wind speed of the wind turbine are deleted.
[0090] In this embodiment, the process of performing secondary screening on the wind turbine power generation data and the corresponding wind speed according to the slope of the power-wind speed change in adjacent wind speed segments is as follows:
[0091] Cluster the wind turbine power generation data according to each preset interval of wind speed to form a wind speed segment sequence;
[0092] Calculate the average wind speed and the corresponding average power of each wind speed segment in the wind speed segment sequence;
[0093] Calculate the slope of change of average power-average wind speed in adjacent wind speed segments;
[0094] When the slope is greater than or equal to a preset slope threshold, the power generation data of the wind turbine and the corresponding wind speed corresponding to the wind speed segment are deleted.
[0095] In this embodiment, the slope threshold is 20.
[0096] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.
[0097] Example 3
[0098] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for intelligently identifying wind turbine self-limiting power are implemented, including, for example: obtaining wind turbine power generation data and corresponding wind speed; performing a primary screening of the wind turbine power generation data and corresponding wind speed based on theoretical power generation, historical average actual power generation, and wind speed; and performing a secondary screening of the wind turbine power generation data and corresponding wind speed based on the power-wind speed change slope of adjacent wind speed segments. The memory may include internal memory, such as high-speed random access memory, or may also include non-volatile memory, such as at least one disk drive. The processor, network interface, and memory are interconnected via an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnect standard bus, an extended industrial standard architecture bus, etc. The bus may be classified as an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the programs may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.
[0099] Example 4
[0100] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the wind turbine self-limiting intelligent identification method, for example, including: obtaining the power generation data of the wind turbine and the corresponding wind speed; performing a first-level screening of the power generation data of the wind turbine and the corresponding wind speed according to the theoretical power generation, the historical average actual power generation and the wind speed; performing a second-level screening of the power generation data of the wind turbine and the corresponding wind speed according to the power-wind speed change slope of adjacent wind speed segments. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, etc.
[0101] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.
[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0103] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.
[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.
[0105] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.
[0106] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
[0107] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.
Claims
1. A method for intelligently identifying wind turbine self-limiting power, characterized in that: include: Obtain wind turbine power generation data and corresponding wind speed; Perform a first-level screening of wind turbine power generation data and corresponding wind speeds based on theoretical power generation, historical average actual power generation, and wind speed; The wind turbine power generation data and the corresponding wind speed are screened at the second level according to the change slope of power-wind speed in adjacent wind speed segments.
2. The method for intelligently identifying wind turbine self-limiting power according to claim 1, characterized in that: The process of performing a first-level screening of the wind turbine power generation data and the corresponding wind speed based on the theoretical power generation, the historical average actual power generation and the wind speed is as follows: Determine whether the wind turbine's power generation data and corresponding wind speed simultaneously meet the following conditions: Actual power generation < theoretical power generation × 60% Actual power generation < historical average actual power generation × 60% Actual power generation>0 Wind speed>3m / s When the power generation data and the corresponding wind speed of any wind turbine do not meet the above conditions, the power generation data and the corresponding wind speed of the wind turbine are deleted.
3. The method for intelligently identifying wind turbine self-limiting power according to claim 1, characterized in that: The process of performing secondary screening on the wind turbine power generation data and the corresponding wind speed according to the change slope of power-wind speed in adjacent wind speed segments is as follows: Cluster the wind turbine power generation data according to each preset interval of wind speed to form a wind speed segment sequence; Calculate the average wind speed and the corresponding average power of each wind speed segment in the wind speed segment sequence; Calculate the slope of change of average power-average wind speed in adjacent wind speed segments; When the slope is greater than or equal to a preset slope threshold, the power generation data of the wind turbine and the corresponding wind speed corresponding to the wind speed segment are deleted.
4. The method for intelligently identifying wind turbine self-limiting power according to claim 3, characterized in that: The slope threshold is 20.
5. A wind turbine self-limiting power intelligent identification system, characterized in that: include: An acquisition module is used to obtain the power generation data of the wind turbine and the corresponding wind speed; The first-level screening module is used to perform first-level screening on the wind turbine power generation data and the corresponding wind speed based on the theoretical power generation, the historical average actual power generation and the wind speed; The secondary screening module is used to perform secondary screening on the wind turbine's power generation data and the corresponding wind speed according to the change slope of the power-wind speed in adjacent wind speed sections.
6. The wind turbine self-limiting power intelligent identification system according to claim 5 is characterized in that: The process of performing a first-level screening of the wind turbine power generation data and the corresponding wind speed based on the theoretical power generation, the historical average actual power generation and the wind speed is as follows: Determine whether the wind turbine's power generation data and corresponding wind speed simultaneously meet the following conditions: Actual power generation < theoretical power generation × 60% Actual power generation < historical average actual power generation × 60% Actual power generation>0 Wind speed>3m / s When the power generation data and the corresponding wind speed of any wind turbine do not meet the above conditions, the power generation data and the corresponding wind speed of the wind turbine are deleted.
7. The wind turbine self-limiting power intelligent identification system according to claim 5 is characterized in that: The process of performing secondary screening on the wind turbine power generation data and the corresponding wind speed according to the change slope of power-wind speed in adjacent wind speed segments is as follows: Cluster the wind turbine power generation data according to each preset interval of wind speed to form a wind speed segment sequence; Calculate the average wind speed and the corresponding average power of each wind speed segment in the wind speed segment sequence; Calculate the slope of change of average power-average wind speed in adjacent wind speed segments; When the slope is greater than or equal to a preset slope threshold, the power generation data of the wind turbine and the corresponding wind speed corresponding to the wind speed segment are deleted.
8. The wind turbine self-limiting power intelligent identification system according to claim 7 is characterized in that: The slope threshold is 20.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the wind turbine self-limiting intelligent identification method according to any one of claims 1 to 4 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the wind turbine self-limiting intelligent identification method according to any one of claims 1 to 4 are implemented.