Wind turbine generator blade icing identification method, system and equipment and storage medium

Abstract

The invention belongs to the technical field of wind power generation, and relates to a wind turbine generator blade icing identification method, system and device and a storage medium. Comprising the following steps that operation data of a wind turbine generator are obtained, and the operation data comprise the wind speed, the generator power and the environment temperature; calculating an actual power coefficient of the wind turbine generator according to the wind speed and the power of the generator; obtaining a unit theoretical power coefficient corresponding to the wind speed; calculating a deviation value between the actual power coefficient and the theoretical power coefficient; and when the deviation value exceeds a first preset threshold value and the environment temperature is lower than a second preset threshold value, it is judged that the blades of the wind turbine generator set are frozen. According to the invention, accurate and reliable identification of the icing state of the blade is realized.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation technology and relates to a method, system, device and storage medium for identifying icing on wind turbine blades. Background Technology

[0002] As wind farm construction expands to colder and more humid regions with more complex environmental conditions, such as higher latitudes and altitudes, wind turbine generators face a severe problem of blade icing during operation. Under low temperature and high humidity conditions, ice layers easily accumulate on the surface of wind turbine blades, causing significant changes in their original aerodynamic shape. This not only reduces wind energy capture efficiency and turbine output power but also triggers a series of operational risks, such as increased turbine vibration and dynamic load imbalance, seriously threatening the structural safety and long-term stable operation of the equipment.

[0003] Currently, conventional monitoring technologies for blade icing mainly rely on external sensors or visual inspection systems. These methods often have limitations such as high cost, complex installation and maintenance, and insufficient reliability in severe weather, which restrict their large-scale application and practical effectiveness. Summary of the Invention

[0004] To address the problems in the prior art, this invention provides a method, system, device, and storage medium for identifying icing on wind turbine blades, achieving accurate and reliable identification of the icing state of the blades.

[0005] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, the present invention provides a method for identifying icing on wind turbine blades, comprising the following steps: Acquire operating data of the wind turbine, including wind speed, generator power, and ambient temperature; Calculate the actual power coefficient of the wind turbine based on the wind speed and the generator power. Obtain the theoretical power coefficient of the unit corresponding to the wind speed; Calculate the deviation between the actual power coefficient and the theoretical power coefficient; When the deviation value exceeds the first preset threshold and the ambient temperature is lower than the second preset threshold, it is determined that the wind turbine blades are icing.

[0006] Preferably, the formula for calculating the actual power coefficient is:

[0007] in, This is the actual power factor; This refers to the generator power. air density; The swept area of ​​the wind turbine unit; This refers to wind speed.

[0008] Preferably, obtaining the theoretical power coefficient of the unit corresponding to the wind speed includes: Based on the model and performance parameters of the wind turbine, the standard power performance curve of the wind turbine under normal operating conditions without icing is obtained; Based on the wind speed, the theoretical power coefficient of the unit corresponding to the wind speed is obtained by querying the standard power performance curve.

[0009] Preferably, the formula for calculating the deviation between the actual power coefficient and the theoretical power coefficient is as follows:

[0010]

[0011] In the formula, This is the deviation value; , These are the weighting coefficients; This represents the average value of the instantaneous power coefficient deviation at each sampling time within a preset time window; The standard deviation of the instantaneous power coefficient deviation value at each sampling time within the preset time window; The formula for calculating the instantaneous power coefficient deviation at each sampling moment within the preset time window is as follows:

[0012] In the formula, To be within the preset time window The instantaneous deviation at each sampling moment; To be within the preset time window The actual power coefficient at each sampling time; To be within the preset time window The theoretical power coefficient at each sampling time.

[0013] Preferably, the method for determining the first preset threshold includes: Acquire historical operating data of the wind turbine under normal operating conditions without icing; the historical operating data includes at least wind speed and generator power; The corresponding normal power coefficient is calculated based on the wind speed and generator power in each set of historical operating data. For each of the normal power coefficients, the deviation between the normal power coefficient and the theoretical power coefficient of the unit under the same wind speed is calculated to obtain multiple normal deviation values; Based on the statistical analysis results of multiple normal deviation values, the first preset threshold is set.

[0014] Preferably, the method for determining the second preset threshold includes: Obtain the historical ambient temperature and historical icing periods of the wind turbine; Extract the historical ambient temperature corresponding to the historical time period, and determine the data distribution characteristics of the historical ambient temperature; Based on the data distribution characteristics, a second preset threshold is set.

[0015] Preferably, after determining that the wind turbine blades are icing, the process further includes: Continuously monitor the deviation value and the ambient temperature within a preset time period; If the deviation value continues to exceed the first preset threshold and the ambient temperature continues to be lower than the second preset threshold within the preset time period, then the icing determination result is confirmed to be valid.

[0016] Secondly, the present invention provides a wind turbine blade icing identification system, comprising: Data acquisition module: used to acquire the operating data of the wind turbine, including wind speed, generator power and ambient temperature; Actual power factor calculation module: used to calculate the actual power factor of the wind turbine based on the wind speed and the generator power; Theoretical power coefficient acquisition module: used to acquire the theoretical power coefficient of the unit corresponding to the wind speed; Deviation calculation module: used to calculate the deviation between the actual power coefficient and the theoretical power coefficient; Icing determination module: used to determine that the wind turbine blades are icing when the deviation value exceeds a first preset threshold and the ambient temperature is lower than a second preset threshold.

[0017] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a wind turbine blade icing identification method.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a method for identifying icing on wind turbine blades.

[0019] Compared with the prior art, the present invention has the following beneficial effects: By dynamically calculating the actual power coefficient reflecting the current energy conversion efficiency using wind speed and generator power, the single influence of wind speed fluctuations on power output is effectively eliminated. By comparing the actual power coefficient with the theoretical power coefficient that characterizes the optimal aerodynamic performance of the unit's clean blades, the generated deviation value can directly and sensitively capture the essence of performance degradation caused by changes in aerodynamic shape due to icing. Secondly, this invention introduces an auxiliary criterion that the ambient temperature is below a preset threshold, which, together with the performance deviation criterion, constitutes a dual verification, distinguishing between performance degradation caused by icing and performance anomalies caused by other non-low temperature factors (such as blade contamination, sensor failure, etc.), thereby significantly improving the specificity and reliability of the discrimination. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0024] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0025] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0026] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0027] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0028] The present invention will now be described in further detail with reference to the accompanying drawings: The first objective of this invention is to provide a method for identifying icing on wind turbine blades, such as... Figure 1 As shown, it includes the following steps: Acquire operating data of the wind turbine, including wind speed, generator power, and ambient temperature; Calculate the actual power coefficient of the wind turbine based on the wind speed and the generator power. Obtain the theoretical power coefficient of the unit corresponding to the wind speed; Calculate the deviation between the actual power coefficient and the theoretical power coefficient; When the deviation value exceeds the first preset threshold and the ambient temperature is lower than the second preset threshold, it is determined that the wind turbine blades are icing.

[0029] This invention provides crucial inputs for icing identification by acquiring three operational data points: wind speed, generator power, and ambient temperature. Wind speed and generator power together determine the energy capture and conversion efficiency of the wind turbine at a specific moment, serving as direct evidence for evaluating its aerodynamic performance. Ambient temperature provides an indispensable meteorological background for determining the physical conditions under which icing occurs. Subsequently, the actual power coefficient calculated based on wind speed and generator power accurately quantifies the true efficiency level of the unit under the current operating condition. By comparing this with the theoretical power coefficient determined by the unit's inherent performance curve at the same wind speed, the obtained deviation value can sensitively capture performance degradation caused by changes in blade aerodynamic shape (such as icing), thus eliminating the influence of wind speed fluctuations on power output and making performance degradation monitoring more targeted and accurate. Finally, logically linking and comprehensively judging the two conditions—the deviation value representing the degree of performance degradation exceeding a preset threshold and the ambient temperature being below the freezing point threshold—significantly improves the reliability of judgments based solely on performance degradation and effectively eliminates false alarms that may be caused by non-low-temperature factors (such as blade contamination, sensor drift, etc.).

[0030] For example, the formula for calculating the actual power factor is:

[0031] in, This is the actual power factor; This refers to the generator power. air density; The swept area of ​​the wind turbine unit; This refers to wind speed.

[0032] Among them, generator power Real-time measurements directly from the wind turbine monitoring system; air density Accurate calculations can be performed by collecting ambient temperature and atmospheric pressure data and applying the ideal gas law, or by using the long-term statistical average of the wind farm area as a reliable approximation; swept area These are fixed geometric parameters that are only related to the blade length and are determined by the turbine model; wind speed This is the measured value at the hub height.

[0033] For example, obtaining the theoretical power coefficient of the unit corresponding to the wind speed includes: Based on the model and performance parameters of the wind turbine, the standard power performance curve of the wind turbine under normal operating conditions without icing is obtained; Based on the wind speed, the theoretical power coefficient of the unit corresponding to the wind speed is obtained by querying the standard power performance curve.

[0034] The standard power performance curve is a characteristic curve showing how the power coefficient (or output power) of a wind turbine changes with wind speed under ideal aerodynamic conditions with clean, defect-free blades. This curve is provided by the manufacturer based on the wind turbine design parameters, or obtained by fitting on-site power curve test data conducted during the acceptance and commissioning phase in the non-icing season. It serves as a benchmark for characterizing the optimal performance of this type of wind turbine.

[0035] During implementation, the standard curve is pre-stored in the form of a data table or function model. In the actual identification process, the system efficiently and accurately obtains the theoretical power coefficient corresponding to the current wind speed by looking up a table or performing interpolation based on the real-time collected wind speed values. This invention compares the actual power coefficient calculated in real-time with this theoretical benchmark; the difference directly and purely reflects the performance deviation caused by abnormal factors such as icing, effectively eliminating the influence of the wind turbine's own design characteristics and normal wind speed fluctuations.

[0036] For example, the formula for calculating the deviation between the actual power coefficient and the theoretical power coefficient is as follows:

[0037]

[0038] In the formula, This is the deviation value; , These are the weighting coefficients; This represents the average value of the instantaneous power coefficient deviation at each sampling time within a preset time window; The standard deviation of the instantaneous power coefficient deviation value at each sampling time within the preset time window; The formula for calculating the instantaneous power coefficient deviation at each sampling moment within the preset time window is as follows:

[0039] In the formula, To be within the preset time window The instantaneous deviation at each sampling moment; To be within the preset time window The actual power coefficient at each sampling time; To be within the preset time window The theoretical power coefficient at each sampling time.

[0040] Specifically, the present invention continuously calculates the instantaneous deviation at each sampling moment within a preset sliding time window. This value directly reflects the instantaneous deviation of real-time performance from the theoretical benchmark. Based on this, for all [performance within the window]... Perform statistical analysis and calculate its average value. with standard deviation :average value This characterizes the overall level of performance degradation and the stability offset during this time period, while the standard deviation... This quantifies the severity of performance fluctuations. The final deviation value... yes and The weighted sum, weight coefficients and The settings can be optimized based on historical operating data to meet the specific balance requirements of different units or wind farms for trend sensitivity and fluctuation sensitivity.

[0041] For example, the method for determining the first preset threshold includes: Acquire historical operating data of the wind turbine under normal operating conditions without icing; the historical operating data includes at least wind speed and generator power; The corresponding normal power coefficient is calculated based on the wind speed and generator power in each set of historical operating data. For each of the normal power coefficients, the deviation between the normal power coefficient and the theoretical power coefficient of the unit under the same wind speed is calculated to obtain multiple normal deviation values; Based on the statistical analysis results of multiple normal deviation values, the first preset threshold is set.

[0042] The first preset threshold is not a fixed or empirical value, but a data-driven method based on the historical operating characteristics of the wind turbine itself. In practice, long-term historical operating data of the wind turbine under known non-icing conditions (such as warm seasons or confirmed ice-free periods) need to be selected to calculate a sample of normal deviation values ​​reflecting normal fluctuations. By performing statistical analysis on this sample set (for example, calculating its probability distribution, determining its mean and standard deviation, and selecting specific high percentiles, such as the 95th or 99th percentile), the first preset threshold can be scientifically set.

[0043] This method fully considers the individual differences of specific generating units, the micro-meteorological conditions of the installation location, and the normal performance scattering range introduced by the measurement characteristics of the sensors themselves, thus making the set threshold personalized and adaptive. The threshold established in this way can strictly distinguish between normal random fluctuations and abnormal performance degradation caused by icing. While minimizing false alarms caused by normal fluctuations of the generating unit itself, it ensures sensitive detection of real icing events, improving the reliability and applicability of the identification system.

[0044] For example, the method for determining the second preset threshold includes: Obtain the historical ambient temperature and historical icing periods of the wind turbine; Extract the historical ambient temperature corresponding to the historical time period, and determine the data distribution characteristics of the historical ambient temperature; Based on the data distribution characteristics, a second preset threshold is set.

[0045] The second preset threshold is set in close conjunction with the specific climatic conditions and historical icing experience of the wind turbine site. Specifically, by collecting long-term historical ambient temperature data for the turbine or the same wind farm and correlating it with historically confirmed periods of blade icing, a set of ambient temperature samples corresponding to the occurrence of icing events can be extracted. Statistical analysis of this set (e.g., analyzing its minimum, maximum, central tendency, and probability distribution) can clarify the actual temperature range and critical conditions for icing.

[0046] Based on this data distribution characteristic, the second preset threshold can be set as a conservative critical value, such as slightly higher than the lower limit of the temperature distribution or a lower percentile (e.g., the 10th percentile), to ensure that the environmental criteria can be triggered under the low-temperature conditions where freezing is most likely to occur, while appropriately excluding some very rare atypical low-temperature ice-free situations. This method makes the temperature threshold no longer a generalized theoretical freezing point, but a personalized parameter that fits the local climate and actual observations, effectively compensating for the deviations that may be caused by complex factors such as humidity and radiation when relying solely on the physical freezing point (0°C).

[0047] For example, after determining that the wind turbine blades are icing, the process further includes: Continuously monitor the deviation value and the ambient temperature within a preset time period; If the deviation value continues to exceed the first preset threshold and the ambient temperature continues to be lower than the second preset threshold within the preset time period, then the icing determination result is confirmed to be valid.

[0048] Specifically, when the system first simultaneously meets both performance deviation and ambient temperature criteria and triggers a preliminary icing alarm, it does not immediately output a final conclusion. Instead, it continuously confirms the deviation value within a preset period of time (e.g., 10 to 30 minutes). Continuous monitoring of ambient temperature is conducted. Performance deviations will only be considered if they occur within the entire validation period. Only when the ambient temperature T remains consistently and stably above the first preset threshold, and also consistently and stably below the second preset threshold, does the system finally confirm the validity of the icing determination and issue a formal alarm. This invention, by introducing a continuous time dimension requirement, can reliably identify and eliminate false alarms caused by isolated anomalies resulting from random factors such as instantaneous strong gusts, short-term power fluctuations, occasional interference from temperature sensors, or transient noise in data acquisition.

[0049] A second objective of this invention is to provide a wind turbine blade icing identification system, comprising: Data acquisition module: used to acquire the operating data of the wind turbine, including wind speed, generator power and ambient temperature; Actual power factor calculation module: used to calculate the actual power factor of the wind turbine based on the wind speed and the generator power; Theoretical power coefficient acquisition module: used to acquire the theoretical power coefficient of the unit corresponding to the wind speed; Deviation calculation module: used to calculate the deviation between the actual power coefficient and the theoretical power coefficient; Icing determination module: used to determine that the wind turbine blades are icing when the deviation value exceeds a first preset threshold and the ambient temperature is lower than a second preset threshold.

[0050] The system can operate 24 hours a day without interruption, and monitors the risk of icing online, in real time and proactively. It provides wind farm operators with a powerful decision support tool and significantly improves the safety and economy of the unit's operation in cold climates.

[0051] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a wind turbine blade icing identification method.

[0052] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the wind turbine blade icing identification method in the above embodiments.

[0053] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A wind turbine blade icing identification method, characterized in that, The method comprises the following steps: obtaining operation data of a wind turbine, the operation data comprising wind speed, generator power and ambient temperature; calculating an actual power coefficient of the wind turbine according to the wind speed and the generator power; obtaining a theoretical power coefficient of the wind turbine corresponding to the wind speed; calculating a deviation value of the actual power coefficient and the theoretical power coefficient; when the deviation value exceeds a first preset threshold value and the ambient temperature is lower than a second preset threshold value, determining that the wind turbine blade is iced.

2. A wind turbine blade icing identification method according to claim 1, characterized in that, The calculation formula of the actual power coefficient is: wherein, is the actual power coefficient; is the generator power; is the air density; is the swept area of the wind turbine; is the wind speed.

3. A method of ice detection on a wind turbine blade according to claim 1, wherein, The method for obtaining the theoretical power coefficient of the wind turbine corresponding to the wind speed comprises: obtaining a standard power performance curve of the wind turbine in a normal operation state without icing based on the model and performance parameters of the wind turbine; querying the standard power performance curve according to the wind speed to obtain the theoretical power coefficient of the wind turbine corresponding to the wind speed.

4. The method of claim 1, wherein, The calculation formula of the deviation value of the actual power coefficient and the theoretical power coefficient is: In the formula, is a deviation value; , is a weight coefficient; is an average value of the instantaneous power coefficient deviation values corresponding to each sampling time within a preset time window; is a standard deviation of the instantaneous power coefficient deviation values corresponding to each sampling time within a preset time window; wherein, the calculation formula of the instantaneous power coefficient deviation value corresponding to each sampling time in a preset time window is: In the formula, is the instantaneous deviation at the th sampling moment within the preset time window; is the actual power coefficient at the th sampling moment within the preset time window; is the theoretical power coefficient at the th sampling moment within the preset time window.

5. A method of ice detection on a wind turbine blade according to claim 1, wherein, The determination method of the first preset threshold value comprises: obtaining historical operation data of the wind turbine in a normal operation state without icing, the historical operation data at least comprising wind speed and generator power; calculating a corresponding normal power coefficient according to the wind speed and the generator power in each group of historical operation data; calculating the deviation between each normal power coefficient and the theoretical power coefficient of the wind turbine under the same wind speed to obtain a plurality of normal deviation values; setting the first preset threshold value based on the statistical analysis result of the plurality of normal deviation values.

6. A method of ice detection on a wind turbine blade according to claim 1, wherein, The determination method of the second preset threshold value comprises: obtaining historical ambient temperature and historical icing period of the wind turbine; extracting the historical ambient temperature corresponding to the historical period and determining the data distribution characteristics of the historical ambient temperature; setting the second preset threshold value according to the data distribution characteristics.

7. A method of ice detection on wind turbine blades as claimed in claim 1, wherein, After determining that the wind turbine blade is iced, the method further comprises: continuously monitoring the deviation value and the ambient temperature within a preset time; if the deviation value continuously exceeds the first preset threshold value and the ambient temperature continuously is lower than the second preset threshold value within the preset time, confirming that the icing determination result is valid.

8. A wind turbine blade icing identification system characterized by, The method comprises: a data acquisition module for obtaining operation data of a wind turbine, the operation data comprising wind speed, generator power and ambient temperature; an actual power coefficient calculation module for calculating an actual power coefficient of the wind turbine according to the wind speed and the generator power; a theoretical power coefficient acquisition module for obtaining a theoretical power coefficient of the wind turbine corresponding to the wind speed; a deviation value calculation module for calculating a deviation value of the actual power coefficient and the theoretical power coefficient; an icing determination module for determining that the wind turbine blade is iced when the deviation value exceeds a first preset threshold value and the ambient temperature is lower than a second preset threshold value.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method according to any one of claims 1-7. The processor executes the computer program to realize the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, which when executed by a processor, implements the steps of the method according to any one of claims 1-7.

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