A wind turbine blade icing identification method and device

Abstract

The application discloses a kind of wind generating set blade icing identification method and device, the method includes: obtaining the current speed of unit and carrying out standard deviation calculation after filtering processing, the standard deviation is compared with preset speed fluctuation threshold, obtain whether current unit exists speed abnormal fluctuation;Current wind speed and wind wheel surface effective wind speed are obtained and are respectively filtered, the deviation value of wind wheel surface effective wind speed after filtering and the deviation percentage of filtered measured wind speed are calculated, obtain whether current unit exists wind speed power mismatch abnormality;According to the current environment temperature and current environment humidity obtained, calculate current environment index, obtain whether unit exists blade icing possibility under current environment temperature;When current unit exists speed abnormal fluctuation, wind speed power mismatch abnormality and blade icing possibility simultaneously, determine that blade is in icing state.The application reduces the risk of unit icing operation, improves the safety and reliability of unit.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation, in particular to a wind turbine blade icing identification method and device. BACKGROUND

[0002] Wind turbines are usually built in regions rich in wind energy resources, and these regions are cold in winter, with low temperature and high humidity. Wind energy has the characteristics of obvious regional imbalance distribution. In China, a large number of wind farms are in northern regions or central and southern mountainous areas, and these areas are prone to ice and snow. If the wind turbine blades are covered with ice and snow, the wind turbine will not run normally, affecting the power generation of the entire wind farm.

[0003] When the wind turbine blades of the power generation system are covered with ice, the airfoil of the wind turbine blades will be affected, causing irregular changes, and the surface height of the blades will become uneven, greatly increasing the roughness. Therefore, the resistance of the blades will be greatly increased, the aerodynamic performance will be greatly reduced, the wind power utilization rate will be greatly reduced, and the wind speed power mismatch will occur. In addition, after the blade icing occurs, the load of the blade itself and other machine parts will increase. If the icing is not uniform, the different blade qualities will cause the entire wind wheel to be unbalanced, causing greater vibration of the blades and the wind wheel, and more damage to the blades, greatly reducing the stability of the wind turbine and increasing the operation risk of the wind turbine.

[0004] In summary, running the blades after icing will affect the power generation and service life of the wind turbine. Therefore, timely determination of whether the blades are iced becomes a key monitoring technology for protecting the service life of the wind turbine. SUMMARY

[0005] In view of this, the present application provides a wind turbine blade icing identification method and device, which can quickly find that the wind turbine is in an icing state in a cold and harsh external environment, make control actions to protect the service life of the wind turbine more quickly, reduce the risk of running the wind turbine in icing, and improve the safety and reliability of the wind turbine.

[0006] The present application discloses a wind turbine blade icing identification method, which comprises:

[0007] The current speed of the wind turbine is obtained, and after filtering and processing, the standard deviation is calculated. The standard deviation is compared with a preset speed fluctuation threshold to determine whether the wind turbine currently has abnormal speed fluctuation.

[0008] The current wind speed and the effective wind speed of the wind wheel surface are acquired and filtered respectively, the deviation value of the filtered effective wind speed of the wind wheel surface and the filtered measured wind speed is calculated, and the deviation percentage is calculated, compared with the preset deviation threshold, to determine whether the wind speed power mismatch abnormality exists in the current unit;

[0009] According to the acquired current environment temperature and current environment humidity, the current environment index is calculated, and the environment index is compared with the preset environment index to determine whether the blade icing possibility exists in the current unit under the current environment temperature;

[0010] When the current unit simultaneously exists the rotational speed abnormal fluctuation, the wind speed power mismatch abnormality and the blade icing possibility, it is determined that the blade is in the icing state.

[0011] Further, the rotational speed of the current unit is acquired, filtered and then the standard deviation is calculated, and the standard deviation is compared with the preset rotational speed fluctuation threshold to determine whether the rotational speed abnormal fluctuation exists in the current unit, comprising:

[0012] The measured rotational speed is filtered and calculated to remove the normal fluctuation of the rotational speed caused by the wind speed change, and some high or low frequency burrs are filtered out to make the rotational speed smoother, and the filtered rotational speed is obtained;

[0013] The standard deviation of the obtained filtered rotational speed is calculated to obtain the standard deviation of the filtered rotational speed; the standard deviation is used to describe the amplitude of the abnormal fluctuation of the rotational speed caused by the unbalanced force due to the blade icing;

[0014] It is judged whether the standard deviation of the rotational speed fluctuation is greater than the preset rotational speed threshold; if yes, the rotational speed abnormal fluctuation monitoring module monitors the abnormality.

[0015] Further, the current wind speed and the effective wind speed of the wind wheel surface are acquired and filtered respectively, the deviation value of the filtered effective wind speed of the wind wheel surface and the filtered measured wind speed is calculated, and the deviation percentage is calculated, compared with the preset deviation threshold, to determine whether the wind speed power mismatch abnormality exists in the current unit, comprising:

[0016] The wind speed measured by the anemometer is read, and the measured wind speed is filtered by moving average filtering;

[0017] The effective wind speed of the wind wheel surface is calculated and read; wherein the effective wind speed of the wind wheel surface is the effective wind speed of the wind wheel surface calculated from the fan operation data; at the same time, the calculated effective wind speed of the wind wheel surface is also filtered by moving filtering, and the window value consistent with the measured wind speed is maintained;

[0018] The wind speed deviation percentage is calculated, and if the wind speed deviation percentage is greater than the preset wind speed deviation and lasts for a period of time, the wind speed power mismatch module monitors the abnormality.

[0019] Further, the specific calculation steps of calculating the effective wind speed of the wind wheel surface are as follows:

[0020] Based on the aerodynamic model of the wind turbine generator set, the aerodynamic moment formula of the wind wheel surface is derived as follows:

[0021]

[0022] Where, p is the air density, R is the wind wheel radius, v is the incoming wind speed, C p (λ) is a polynomial of the wind turbine about λ tip speed ratio, called power coefficient;

[0023] And the dynamics model of the wind turbine transmission chain is as follows:

[0024]

[0025] Through the energy conservation formula, the effective wind speed of the wind wheel surface is obtained as follows:

[0026]

[0027] Where, v is the effective wind speed of the wind wheel surface.

[0028] Further, the percentage of the wind speed deviation is as follows:

[0029]

[0030] Where, Wd indicates the percentage of wind speed deviation, W1 indicates the filtered measured wind speed, and W2 indicates the filtered calculated wind speed.

[0031] Further, the effective wind speed of the wind wheel surface refers to the wind speed calculated by actually running the wind turbine to absorb wind energy. If the actually measured wind speed is much higher than the effective wind speed actually absorbed by the wind turbine, it indicates that the aerodynamic performance of the wind turbine has decreased due to blade icing or blade stall.

[0032] Further, under the condition that the blade is not iced, the effective wind speed of the wind wheel surface is basically the same as the wind speed measured by the anemometer; under the condition that the blade is iced, the effective wind speed of the wind wheel surface and the wind speed measured by the anemometer have obvious deviation.

[0033] Further, the current environment index is calculated according to the obtained current environment temperature and current environment humidity, and the current environment index is compared with the preset environment index to determine whether the blade icing is possible under the current environment temperature, comprising:

[0034] The current temperature and current humidity of the external environment are measured by the sensor, and it is judged whether the combination of temperature and humidity meets the icing condition;

[0035] The environment index is determined by using a weighted integration method;

[0036] The calculated environment index is compared with a preset environment index threshold value; if greater, it indicates that the unit has a possibility of blade icing at the current environment temperature.

[0037] Further, the environment index is:

[0038] E=aT×bH

[0039] Wherein, a and b are weighting coefficients, T is the environment temperature, H is the environment humidity, and E is the calculated environment index.

[0040] The application also discloses a device based on the blade icing identification method of the wind turbine generator set.

[0041] The rotational speed abnormal fluctuation monitoring module is used for obtaining the rotational speed of the current unit, performing filtering processing, and calculating the standard deviation, comparing the standard deviation with a preset rotational speed fluctuation threshold value, and determining whether the current unit has rotational speed abnormal fluctuation.

[0042] The wind speed and power mismatch monitoring module is used for obtaining the current wind speed and the effective wind speed of the wind wheel surface, performing filtering on the wind speed and the effective wind speed respectively, calculating the deviation value of the filtered effective wind speed of the wind wheel surface and the filtered measured wind speed, calculating the deviation percentage, comparing the deviation percentage with a preset deviation threshold value, and determining whether the current unit has wind speed and power mismatch abnormality.

[0043] The external environment monitoring module is used for calculating the current environment index according to the obtained current environment temperature and current environment humidity, and comparing the environment index with a preset environment index, to determine whether the current unit has a possibility of blade icing at the current environment temperature.

[0044] The determination module is used for determining that the blade is in the icing state when the current unit has rotational speed abnormal fluctuation, wind speed and power mismatch abnormality, and blade icing possibility at the same time.

[0045] Due to the above technical scheme, the application has the following advantages:

[0046] 1. Without increasing the hardware cost, the rotational speed fluctuation monitoring, wind speed and power mismatch, and external environment detection are used to determine whether the blade of the unit is iced, thereby effectively improving the accuracy and reliability of identifying the blade icing of the unit.

[0047] 2、The method is different from the previous wind speed power matching judgment method, can use the performance of the wind turbine blade in the icing state, the abnormal fluctuation of the speed caused by the imbalance of the wind wheel, and the combination of the two ways to identify the wind turbine in the blade icing state, more accurate and fast to identify the icing condition, protect the monitoring ability of the wind turbine in the harsh environment, and effectively improve the service life of the unit. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0049] Figure 1 It is a kind of wind turbine blade icing identification device schematic diagram;

[0050] Figure 2 It is a speed abnormal fluctuation monitoring module monitoring flow chart;

[0051] Figure 3 It is a wind speed power mismatch monitoring module monitoring flow chart;

[0052] Figure 4 It is an external environment monitoring module monitoring flow chart;

[0053] Figure 5 The effective wind speed of the wind wheel when the unit is running under the condition that the blade is not iced;

[0054] Figure 6 The effective wind speed of the wind wheel when the unit is running under the condition that the blade is iced;

[0055] Figure 7 The timing diagram of speed abnormal fluctuation under the condition that the blade is iced. DETAILED DESCRIPTION

[0056] The present application is further illustrated in conjunction with the drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all. All other embodiments obtained by those skilled in the art shall belong to the scope of protection of the embodiments of the present application.

[0057] The present application provides an embodiment of a wind turbine blade icing identification method, specifically:

[0058] As Figure 2 Step S01 reads different sensor speeds according to different models (direct drive or double-fed), such as converter speed, generator speed or gearbox speed value, as measurement speed;

[0059] AsFigure 2 As shown in step S02, the measured rotational speed is filtered to remove normal fluctuations in rotational speed caused by wind speed changes, and some excessively high or low frequency spikes are filtered out to make the rotational speed smoother, thus obtaining the filtered rotational speed.

[0060] The filter is a high-order bandpass filter, and its difference equation is:

[0061]

[0062] Where n is the order of the filter, a k ,b k y(n) is the constant coefficient of the filter, y(n) is the filter output value, i.e. the filtered rotational speed obtained in step S02, and x(n) is the rotational speed signal input value, i.e. the measured rotational speed read in step S01.

[0063] like Figure 2 As shown in step S03, the standard deviation of the filtered speed obtained in step S02 is calculated to obtain the standard deviation result of the filtered speed. The formula is as follows:

[0064]

[0065]

[0066] Where n is the number of samples in the sequence used to calculate the variance, and E(y(n)) is the expected value, i.e. the average value, of the selected sequence.

[0067] The standard deviation of the rotational speed fluctuation calculated after step S03 is actually used to describe whether there is a periodic amplification trend in the rotational speed fluctuation, i.e., the rotational speed oscillates. For example... Figure 7 As shown, this abnormal amplification of speed fluctuations (speed oscillations) at a certain frequency is caused by the unevenness of the icing mass and thickness on the three blades, resulting in an imbalance of forces on the entire rotor surface. The standard deviation of the filtered speed is actually used to calculate the magnitude of the deviation of the maximum and minimum speed oscillations from the average speed, describing the magnitude of the amplified abnormal speed fluctuations caused by the unbalanced forces resulting from blade icing. Similarly, as... Figure 7 The envelopes of the maximum and minimum speeds actually show a significant amplification trend, with the deviation from the average speed of 10.5 rpm increasing, exhibiting an oscillating amplification trend. For this data characteristic, it's not necessary to focus excessively on the actual numerical value of the operating speed; rather, attention should be paid to the degree of oscillation amplification of the speed described by the standard deviation.

[0068] like Figure 2 As shown in step S04, it is determined whether the standard deviation of the speed fluctuation is greater than the threshold A1. If it is greater, the speed abnormal fluctuation monitoring module detects the abnormality.

[0069] The above is the main function of the rotating speed oscillation identification module. It can identify whether the rotating speed of the fan has an oscillation amplification trend. By modifying different monitoring thresholds, the effect of monitoring rotating speed oscillation caused by different influencing factors can be achieved.

[0070] As Figure 3 The flow chart of the wind speed power mismatch monitoring module is shown.

[0071] Step L01, read the wind speed measured by an anemometer, not limited to the wind speed measured by a mechanical anemometer or an ultrasonic anemometer. Step L02, perform a sliding average filter on the measured wind speed, where the formula of the sliding average filter is as follows:

[0072]

[0073] Where n is the order of the filter, y(n) is the output value of the filter, x(n) is the input value of the wind speed signal, and M is the window value using the sliding average.

[0074] Step L03 calculates and reads the effective wind speed of the wind wheel surface. The effective wind speed of the wind wheel surface is the effective wind speed of a single point of the wind wheel surface calculated from the fan operation data. At the same time, the calculated effective wind speed of the wind wheel surface is also subjected to the same sliding filter, maintaining the same window value as the measured wind speed. The specific calculation steps of calculating the effective wind speed of the wind wheel surface are as follows:

[0075] Based on the aerodynamic model of the wind turbine generator set, the aerodynamic moment formula of the wind wheel surface is derived as follows:

[0076]

[0077] Where < is the air density, R is the wind wheel radius, v is the incoming wind speed, C p (λ) is a polynomial of the fan about λ tip speed ratio, called the power coefficient.

[0078] And the dynamics model of the fan transmission chain is as follows:

[0079]

[0080] Through the energy conservation formula, the effective wind speed of the wind wheel surface can be obtained as:

[0081]

[0082] At this time, the calculated effective wind speed of the wind wheel surface refers to the actual running wind speed of the fan, which is calculated by absorbing wind energy. If the actual external measured wind speed is much higher than the effective wind speed actually absorbed by the fan, it means that the aerodynamic performance of the fan has declined (excluding the case of fan scheduling limited power), and this is often caused by blade icing or blade stall. As Figure 5As shown, under the condition of no icing of the blade, the effective wind speed (calculated) of the wind wheel surface is basically the same as the trend of the anemometer measured wind speed, while as Figure 6 As shown, under the condition of icing of the blade, the effective wind speed (calculated) of the wind wheel surface is obviously deviated from the anemometer measured wind speed, the anemometer measured wind speed is about 13 m / s, while the effective wind speed of the wind wheel surface at this time is calculated to be about 8 m / s. This is the main judgment principle of the wind speed power mismatch module.

[0083] As shown in step L04 in FIG. 4, the percentage of wind speed deviation is calculated, and the formula is as follows: Figure 3

[0084]

[0085] Wherein, Wd refers to the percentage of wind speed deviation, W1 refers to the filtered measured wind speed, and W2 refers to the filtered calculated wind speed.

[0086] As shown in step L05 in FIG. 4, if the percentage of wind speed deviation is greater than a threshold A2 and lasts for a period of time T1, a wind speed power mismatch abnormality occurs. Figure 3 As shown in FIG. 5, a flowchart of the external environment monitoring module monitoring the climate conditions conforming to the blade icing.

[0087] Figure 4 The current temperature and humidity of the external environment are mainly measured by the sensors of the external environment measurement (steps T01 and T02 in FIG. 5), and whether the conditions conform to the icing are determined according to the combination of the temperature and humidity. For example, the lower the temperature and the greater the humidity, the higher the possibility of blade icing, and the higher the temperature and the lower the humidity, the lower the possibility of blade icing.

[0088] As shown in step T03 in FIG. 5, the current environment index is calculated, and the weighted quadrature method is used to determine the environment index: Figure 4 E=aT×bH

[0089] Figure 4 Wherein, a and b are weighting coefficients, T is the environment temperature, H is the environment humidity, and E refers to the calculated environment index.

[0090] According to the comparison between the calculated environment index and the set threshold A3, if it is greater than a certain index A3, it means that under the current environment temperature, the unit exists the possibility of blade icing.

[0091] When the unit currently exists the abnormal fluctuation of the rotating speed, the abnormality of the wind speed power mismatch and the possibility of blade icing at the same time, it is determined that the blade is in the icing state.

[0092] Based on the above embodiment, refer to

[0093]

[0094] Based on the above embodiment, refer to Figure 1 ​​​​The application also provides an embodiment of a wind turbine blade icing identification device, which comprises:

[0095] A rotating speed abnormal fluctuation monitoring module is configured to obtain the rotating speed of the current unit, filter the rotating speed, calculate the standard deviation, compare the standard deviation with a preset rotating speed threshold, and determine whether the current unit has rotating speed abnormal fluctuation.

[0096] A wind speed-power mismatch monitoring module is configured to obtain the current wind speed and the effective wind speed of the wind wheel surface, filter the wind speed and the effective wind speed respectively, calculate the deviation value of the filtered effective wind speed of the wind wheel surface and the filtered measured wind speed, calculate the deviation percentage, compare the deviation percentage with a preset deviation threshold, and determine whether the current unit has wind speed-power mismatch abnormality.

[0097] An external environment monitoring module is configured to calculate the current environment index according to the obtained current environment temperature and current environment humidity, compare the environment index with a preset environment index, and determine whether the current unit has blade icing possibility under the current environment temperature.

[0098] A determination module is configured to determine that the blade is in icing state when the current unit has rotating speed abnormal fluctuation, wind speed-power mismatch abnormality and blade icing possibility at the same time.

[0099] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the application and not to limit it, although the application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: the specific embodiments of the application can still be modified or replaced by the same, without departing from the spirit and scope of the application, any modification or equivalent replacement, which should be covered within the protection scope of the claims of the application.

Claims

1. A method for identifying icing on wind turbine blades, characterized in that, include: The current unit speed is obtained and filtered before the standard deviation is calculated. The standard deviation is then compared with a preset speed fluctuation threshold to determine whether there is any abnormal speed fluctuation in the unit. The current wind speed and the effective wind speed at the rotor surface are obtained and filtered respectively. The deviation between the effective wind speed at the rotor surface after filtering and the measured wind speed after filtering is calculated and the deviation percentage is calculated. The deviation percentage is compared with the preset deviation threshold to determine whether there is an abnormality in wind speed-power mismatch of the unit. Based on the current ambient temperature and humidity, the current environmental index is calculated, and compared with the preset environmental index to determine whether the unit's blades are likely to freeze under the current ambient temperature. When the unit is simultaneously experiencing abnormal fluctuations in rotational speed, abnormal wind speed-power mismatch, and the possibility of blade icing, the blades are determined to be in an icing state.

2. The method for identifying icing on wind turbine blades according to claim 1, characterized in that, The process of acquiring the current unit speed, filtering it, calculating its standard deviation, and comparing the standard deviation with a preset speed fluctuation threshold to determine whether the unit currently experiences abnormal speed fluctuations includes: The measured rotational speed is filtered to remove normal fluctuations in rotational speed caused by wind speed changes and to filter out some excessively high or low frequency spikes, making the rotational speed smoother and obtaining the filtered rotational speed. The standard deviation of the filtered speed is calculated to obtain the standard deviation of the filtered speed; the standard deviation is used to describe the magnitude of the abnormal fluctuation of speed caused by the unbalanced force caused by blade icing. Determine if the standard deviation of the speed fluctuation is greater than the preset speed fluctuation threshold; if it is, the speed abnormal fluctuation monitoring module detects an anomaly.

3. The method for identifying icing on wind turbine blades according to claim 1, characterized in that, The process of acquiring the current wind speed and the effective wind speed at the wind turbine surface, filtering them separately, calculating the deviation between the filtered effective wind speed at the wind turbine surface and the measured wind speed, calculating the percentage deviation, and comparing the percentage deviation with a preset deviation threshold to determine whether the unit currently has a wind speed-power mismatch anomaly includes: Read the wind speed measured by the anemometer and perform a moving average filter on the measured wind speed; Calculate and read the effective wind speed at the wind turbine surface; the effective wind speed at the wind turbine surface is the single-point effective wind speed at the wind turbine surface calculated from the wind turbine operating data; at the same time, the calculated effective wind speed at the wind turbine surface is also subjected to the same sliding filter to maintain a window value consistent with the measured wind speed. Calculate the percentage of wind speed deviation. If the percentage of wind speed deviation is greater than the preset wind speed deviation and continues for a period of time, the wind speed power mismatch module will detect the abnormality.

4. The method for identifying icing on wind turbine blades according to claim 3, characterized in that, The specific calculation steps for calculating the effective wind speed at the wind turbine surface are as follows: Based on the aerodynamic model of the wind turbine generator, the formula for the aerodynamic torque on the rotor surface is derived as follows: Where ρ is the air density, R is the rotor radius, v is the incoming wind speed, and C p (λ) is the polynomial of the wind turbine with respect to the tip speed ratio λ, and is called the power coefficient; The dynamic model of the wind turbine drive train is as follows: By combining the energy conservation formulas, the effective wind speed at the wind turbine surface can be obtained as follows: Where v is the effective wind speed at the wind turbine surface.

5. The method for identifying icing on wind turbine blades according to claim 4, characterized in that, The percentage of the wind speed deviation is calculated using the following formula: Where Wd refers to the percentage of wind speed deviation, W1 refers to the measured wind speed after filtering, and W2 refers to the calculated wind speed after filtering.

6. The method for identifying icing on wind turbine blades according to claim 3, characterized in that, The effective wind speed at the rotor surface refers to the wind speed calculated by back-calculating the wind energy absorbed by the wind turbine during actual operation. If the actual external wind speed is much higher than the effective wind speed absorbed by the wind turbine during operation, it indicates that the aerodynamic performance of the wind turbine has decreased, which is caused by blade icing or blade stall.

7. The method for identifying icing on wind turbine blades according to claim 3, characterized in that, When the blades are not icy, the effective wind speed on the rotor surface and the wind speed measured by the anemometer show a similar trend; when the blades are icy, there is a significant deviation between the effective wind speed on the rotor surface and the wind speed measured by the anemometer.

8. The method for identifying icing on wind turbine blades according to claim 1, characterized in that, The step of calculating the current environmental index based on the acquired current ambient temperature and humidity, and comparing the environmental index with a preset environmental index to determine whether the unit's blades are likely to ice up under the current ambient temperature, includes: The current temperature and humidity of the external environment are measured by sensors, and the combination of temperature and humidity is used to determine whether the conditions for freezing are met. The environmental index is determined using a weighted product method; The calculated environmental index is compared with the preset environmental index threshold; if it is greater, it indicates that the unit's blades may freeze under the current ambient temperature.

9. The method for identifying icing on wind turbine blades according to claim 8, characterized in that, The environmental index is: E = aT × bH Where a and b are weighting coefficients, T is ambient temperature, H is ambient humidity, and E is the calculated environmental index.

10. An apparatus applicable to the method for identifying icing on wind turbine blades according to any one of claims 1-9, characterized in that, The device includes: The abnormal speed fluctuation monitoring module is used to acquire the current speed of the unit, filter it, calculate the standard deviation, and compare the standard deviation with the preset speed threshold to determine whether there is an abnormal speed fluctuation in the unit. The wind speed-power mismatch monitoring module is used to acquire the current wind speed and the effective wind speed on the wind turbine surface, and filter them respectively. It calculates the deviation between the effective wind speed on the wind turbine surface after filtering and the measured wind speed after filtering, and calculates the percentage of deviation. The percentage of deviation is compared with the preset deviation threshold to determine whether there is an abnormality of wind speed-power mismatch in the unit. The external environment monitoring module is used to calculate the current environmental index based on the current ambient temperature and humidity, and compare the environmental index with the preset environmental index to determine whether there is a possibility of blade icing at the current ambient temperature. The determination module is used to determine whether the blades are in an icing state when the unit is simultaneously experiencing abnormal fluctuations in rotational speed, abnormal wind speed-power mismatch, and the possibility of blade icing.

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