Wind direction measurement deviation identification method, device and equipment of wind turbine and medium

Abstract

The application discloses a kind of wind direction measurement deviation identification method, device, equipment and medium of wind turbine unit.The method comprises: determining the multiple reference operating information of wind turbine unit;By the power, wind speed and wind direction corresponding to each reference operating information are curved surface fitting, the reference operating characteristic curved surface information of wind turbine unit is obtained;According to reference operating characteristic curved surface information, the reference wind direction measurement deviation characteristic curve information of wind turbine unit is determined;Wind direction measurement deviation characteristic curve information is used to describe the continuous curve of the relationship between wind speed and the wind direction measurement deviation corresponding to the maximum power point of wind turbine unit under corresponding wind speed;According to reference wind direction measurement deviation characteristic curve information, wind direction measurement deviation identification and correction are carried out on wind turbine unit.The technical scheme of the present application identifies the deviation between the measured wind direction and the actual wind direction, realizes more accurate yawing wind effect of wind turbine unit, and improves the power generation performance of wind turbine unit.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power generation, and particularly relates to a wind direction measurement deviation identification method, device, equipment and medium for a wind turbine. BACKGROUND

[0002] Wind power generation refers to converting wind energy into electric energy, and occupies an important position in the energy field. The angle between the center line of the nacelle of a wind turbine (full name: wind turbine generator system) and the wind direction is referred to as wind direction. If the wind direction is large and persistent, the wind turbine will not be able to obtain maximum wind energy, thereby greatly reducing the power generation performance of the wind turbine.

[0003] In related solutions, the wind turbine determines the wind direction through a wind vane located on the nacelle, and then adjusts the direction of the nacelle through yaw action to make the angle between the direction of the nacelle and the wind direction approach 0 degrees, thereby ensuring that the wind turbine obtains maximum wind energy as much as possible. However, due to problems such as dynamic characteristics of the wind vane, wake interference caused by rotation of the impeller, and installation deviation of the wind vane, there is a measurement deviation between the measured wind direction and the actual wind direction, which causes the wind turbine to be unable to obtain maximum wind energy and reduces the power generation of the wind turbine. SUMMARY

[0004] The present application provides a wind direction measurement deviation identification method, device, equipment and medium for a wind turbine, to identify the deviation between the measured wind direction and the actual wind direction, correct the measured wind direction to obtain a corrected wind direction angle, and use it for wind turbine yaw control, thereby improving the power generation performance of the wind turbine.

[0005] According to an aspect of the present application, a wind direction measurement deviation identification method for a wind turbine is provided, which can include:

[0006] determining a plurality of reference operating information of the wind turbine; the reference operating information includes reference power, reference wind speed and reference wind direction measured in a reference preset time period;

[0007] obtaining reference operating characteristic surface information of the wind turbine by surface fitting of power, wind speed and wind direction corresponding to each of the reference operating information;

[0008] determining reference wind direction measurement deviation characteristic curve information of the wind turbine according to the reference operating characteristic surface information; the wind direction measurement deviation characteristic curve information is used to describe a continuous curve between wind speed and wind direction measurement deviation corresponding to the maximum power point of the wind turbine under the corresponding wind speed;

[0009] identifying and correcting the wind direction measurement deviation of the wind turbine according to the reference wind direction measurement deviation characteristic curve information.

[0010] According to another aspect of the present application, there is provided a wind direction measurement deviation identification device of a wind turbine, which can comprise:

[0011] a first information determining module configured to determine a plurality of reference operating information of the wind turbine, wherein the reference operating information comprises reference power, reference wind speed and reference wind direction measured at a reference time period;

[0012] a second information determining module configured to obtain reference operating characteristic surface information of the wind turbine by performing surface fitting on power, wind speed and wind direction corresponding to each of the reference operating information;

[0013] a third information determining module configured to determine reference wind direction measurement deviation characteristic curve information of the wind turbine according to the reference operating characteristic surface information, wherein the reference wind direction measurement deviation characteristic curve information is used to describe a continuous curve between wind speed and wind direction measurement deviation corresponding to a maximum power point of the wind turbine under the corresponding wind speed;

[0014] a control module configured to identify and correct wind direction measurement deviation of the wind turbine according to the reference wind direction measurement deviation characteristic curve information.

[0015] According to another aspect of the present application, there is provided an electronic device, which can comprise:

[0016] at least one processor; and

[0017] a memory connected with the at least one processor; wherein,

[0018] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the wind direction measurement deviation identification method of the wind turbine according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to perform the wind direction measurement deviation identification method of the wind turbine according to any one of the embodiments of the present application when executed by the processor.

[0020] The technical scheme of the embodiment of the present application determines multiple reference operation information of the wind turbine generator set, obtains reference operation characteristic surface information of the wind turbine generator set by surface fitting of power, wind speed and wind direction corresponding to each reference operation information, determines reference wind direction measurement deviation characteristic curve information of the wind turbine generator set according to the reference operation characteristic surface information, the wind direction measurement deviation characteristic curve information is used to describe a continuous curve between wind speed and wind direction measurement deviation corresponding to the maximum power point of the wind turbine generator set under the corresponding wind speed, identifies and corrects the wind direction measurement deviation of the wind turbine generator set according to the reference wind direction measurement deviation characteristic curve information, corrects the measured wind direction, further obtains the corrected wind direction, and uses the corrected wind direction for yaw control of the wind turbine generator set, so that more accurate yaw control effect of the wind turbine generator set is achieved, and the power generation performance of the wind turbine generator set is improved.

[0021] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] 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 below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is a flow chart of a wind direction measurement deviation identification method of a wind turbine generator set according to the first embodiment of the present application;

[0024] Figure 2 is a flow chart of a wind direction measurement deviation identification method of a wind turbine generator set according to the second embodiment of the present application;

[0025] Figure 3 is a flow chart of a wind direction measurement deviation identification method of a wind turbine generator set according to the third embodiment of the present application;

[0026] Figure 4 is a structural schematic diagram of a wind direction measurement deviation identification device of a wind turbine generator set according to the fourth embodiment of the present application;

[0027] Figure 5 is a structural schematic diagram of an electronic device for implementing the wind direction measurement deviation identification method of the wind turbine generator set according to the present application. DETAILED DESCRIPTION

[0028] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort should belong to the scope of the present application.

[0029] It should be noted that the terms "first", "second", "third", and "reference" and the like in the description, claims, and drawings of the present application are intended to distinguish similar objects, and are not necessarily used to describe a particular order or sequence. It should be understood that the data thus used can be interchanged, where appropriate, so that the embodiments of the present application described herein can be carried out in other sequences than those illustrated or described herein. Moreover, the terms "comprising" and "having" and any variations thereof are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a list of steps or units not necessarily limited to those clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products, or apparatus.

[0030] Embodiment one

[0031] Figure 1 For a flowchart of a wind direction measurement deviation identification method of a wind turbine provided according to the first embodiment of the present application, the present embodiment can be applicable to the case of identifying the wind direction measurement deviation of a wind turbine. The method can be executed by a wind direction measurement deviation identification device of a wind turbine, which can be realized in the form of hardware and / or software. The wind direction measurement deviation identification device of the wind turbine can be configured in any electronic device with network communication function. As shown in the figure, the method comprises: Figure 1

[0032] S110, determining a plurality of reference operation information of the wind turbine; the reference operation information comprises reference power, reference wind speed, and reference wind direction measured in a reference preset time period.

[0033] The reference preset time period can be a preset historical time period or a preset online time period.

[0034] Specifically, the data of the wind turbine during operation can be stored in an electronic device. When the operation data is needed, the data of the wind turbine in the reference preset time period can be extracted from the storage of the electronic device, and the reference operation information can be determined after processing. Alternatively, the operation data generated in real time in the preset online time period can be obtained.

[0035] ​S120, fitting the power, the wind speed and the wind direction corresponding to each of the reference operation information to obtain the reference operation characteristic surface information of the wind turbine generator set.

[0036] Optionally, the surface fitting can be fitting three-dimensional surface by using a three-dimensional surface fitting algorithm to different two-dimensional data, wherein the three-dimensional surface fitting algorithm can include a least mobile square surface fitting algorithm. For example, the power, the wind speed and the wind direction are fitted by using the least mobile square surface fitting algorithm to obtain the reference operation characteristic surface information of the wind turbine generator set. Specifically, the node fitting function of the least mobile square surface fitting algorithm is as follows:

[0037]

[0038] wherein a j (x node ) is a set of coefficient vector functions, p j (x) is a base function. When the distribution of a large number of discrete data is relatively disorderly, the method can be applied to segment fitting of the data to avoid the problem of discontinuity and non-smoothness of the fitting curve on adjacent segments.

[0039] In a feasible embodiment, the reference operation characteristic surface information of the wind turbine generator set is obtained by fitting the power, the wind speed and the wind direction corresponding to each of the reference operation information, which can include steps A1-A2:

[0040] Step A1, fitting the power, the wind speed and the wind direction corresponding to each of the reference operation information of the wind turbine generator set by using a three-dimensional surface fitting algorithm to obtain the historical operation characteristic surface information of the wind turbine generator set; the reference time period corresponding to the reference operation information is a preset historical time period;

[0041] Step A2, determining the historical operation characteristic surface information as the reference operation characteristic surface information of the wind turbine generator set.

[0042] Specifically, the power, the wind speed and the wind direction corresponding to each of the reference operation information of the preset historical time period can be fitted by using a three-dimensional surface fitting algorithm to obtain the historical operation characteristic surface information of the wind turbine generator set, and the historical operation characteristic surface information is determined as the reference operation characteristic surface information of the wind turbine generator set. For example, the power, the wind speed and the wind direction corresponding to each of the reference operation information of the determined preset historical time period can be fitted by using the least mobile square surface fitting algorithm to obtain the historical operation characteristic surface information of the wind turbine generator set, and the historical operation characteristic surface information is determined as the reference operation characteristic surface information of the wind turbine generator set.

[0043] The power, the wind speed and the wind direction corresponding to each reference operation information of the preset historical time period are three-dimensionally curved surface fitted, so that the disordered information is regularized.

[0044] In one feasible embodiment, the reference operation characteristic surface information of the wind turbine generator is obtained by curved surface fitting the power, the wind speed and the wind direction corresponding to each reference operation information, which can include steps B1-B2:

[0045] Step B1, three-dimensionally curved surface fitting the power, the wind speed and the wind direction corresponding to each reference operation information of the wind turbine generator by using a three-dimension curved surface fitting algorithm, to obtain the online operation characteristic surface information of the wind turbine generator; the reference time period corresponding to the reference operation information is a preset online time period;

[0046] Step B2, weighting the online operation characteristic surface information of the wind turbine generator and the reference operation characteristic surface information of the wind turbine generator obtained by the last curved surface fitting, to obtain the reference operation characteristic surface information of the wind turbine generator optimized by the online operation data.

[0047] The reference operation information of the preset online time period is the operation information of the wind turbine generator acquired by the wind turbine generator in the preset online time period, i.e., the latest operation information or online operation data of the wind turbine generator.

[0048] Specifically, according to the embodiment of the application, the wind direction measurement deviation of the acquired historical operation data of the wind turbine generator is identified, and the wind direction measurement deviation value is generally about 3°-8°. Since the wind direction data in the historical operation data is normally distributed with 0° as the symmetric center, the data amount near the identified wind direction measurement deviation value is relatively small. In order to more accurately determine the wind direction measurement deviation, the wind direction measurement deviation caused by the adjustment of the installation position of the wind direction marker or the replacement of the wind direction marker in the actual operation and maintenance process of the wind turbine generator is also considered. The online operation data needs to be accumulated and stored to reach the required amount of data, and then the online operation characteristic surface information is obtained by analyzing and processing the online operation data. That is, the power, the wind speed and the wind direction corresponding to each reference operation information of the wind turbine generator in the preset online time period are determined, and a three-dimension curved surface fitting algorithm is used to three-dimensionally curved surface fit the power, the wind speed and the wind direction, to obtain the online operation characteristic surface information of the wind turbine generator, for example, using a least moving quadratic curved surface fitting algorithm to fit the power, the wind speed and the wind direction. Then, the online operation characteristic surface information of the wind turbine generator is weighted with the reference operation characteristic surface information of the wind turbine generator obtained by the last curved surface fitting, to obtain the reference operation characteristic surface information of the wind turbine generator optimized by the online operation data.

[0049] The reference operation characteristic surface information is optimized and updated through the reference operation information of the obtained preset online time period, so that the reference operation characteristic surface information is more accurate and reliable, and the subsequent confirmation of the wind direction measurement deviation characteristic curve information is more beneficial, and the wind direction measurement deviation result is more accurate. After obtaining the above-mentioned reference operation characteristic surface information, new online operation characteristic surface information is continuously generated as time goes by, and the reference operation characteristic surface information can be continuously optimized and updated in real time through the iterative weighting of the online operation characteristic surface information as time goes by.

[0050] In one possible embodiment, weighting the online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting can include: determining the weight of the online operation characteristic surface information and the weight of the last reference operation characteristic surface information, wherein the weight of the online operation characteristic surface information and the weight of the last reference operation characteristic surface information are added to 1, and the online operation characteristic surface information and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting are weighted in proportion to the weight. The specific formula is as follows:

[0051] f new (v,r,p)=(1-w)*f off (v,r,p)+w*f on (v,r,p)

[0052] Wherein, f new , f off and f on are the reference operation characteristic surface information, the last reference operation characteristic surface information and the online operation characteristic surface information respectively, v, r and p are the wind speed, the wind direction and the power respectively, (1-w) is the weight corresponding to the last reference operation characteristic surface information, and w is the weight corresponding to the online operation characteristic surface information.

[0053] In another possible embodiment, weighting the online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting can include steps C1-C3:

[0054] Step C1, determining the first data amount corresponding to the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting and the second data amount corresponding to the online operation characteristic surface information; the first data amount is the data amount of the operation data of the wind turbine generator set obtained by the last surface fitting, and the second data amount is the data amount of the operation data of the wind turbine generator set in the online time period;

[0055] Step C2, calculating a first weight of the first data amount in the total of the first data amount and the second data amount, and calculating a second weight of the second data amount in the total of the first data amount and the second data amount;

[0056] Step C3, weighting the online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information according to the first weight and the second weight.

[0057] Specifically, after determining the first data amount corresponding to the last reference operation characteristic surface information and the second data amount corresponding to the online operation characteristic surface information, the first weight and the second weight are calculated, and finally the online operation characteristic surface information of the wind turbine generator set is weighted with the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting according to the first weight and the second weight, to obtain the reference operation characteristic surface information of the wind turbine generator set. In an example, the online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting can be weighted by using a weight fitting formula, wherein the weight fitting formula is:

[0058]

[0059] wherein f new , f off and f on are the reference operation characteristic surface information, the last reference operation characteristic surface information and the online operation characteristic surface information respectively, v, r and p are the wind speed, the wind direction and the power respectively, N off is the first data amount corresponding to the last reference operation characteristic surface information, and N on is the second data amount corresponding to the online operation characteristic surface information.

[0060] In the technical solution, the online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting are weighted by using the weight fitting formula, so that the reference operation characteristic surface information of the wind turbine generator set can be obtained more accurately.

[0061] S130, determining the reference wind direction measurement deviation characteristic curve information of the wind turbine generator set according to the reference operation characteristic surface information; the wind direction measurement deviation characteristic curve information is used to describe a continuous curve of the relationship between the wind speed and the wind direction measurement deviation corresponding to the maximum power point of the wind turbine generator set under the corresponding wind speed.

[0062] Specifically, the reference wind direction measurement deviation characteristic curve information can include the wind direction measurement deviation of the wind turbine corresponding to different actual wind speeds, i.e., a mapping relationship between the wind speed and the wind direction measurement deviation is given, and the wind turbine can obtain the maximum wind energy to achieve the maximum power at the wind direction measurement deviation angle. The actual wind speed is input into the reference wind direction measurement deviation characteristic curve information, and the wind direction measurement deviation of the corresponding wind turbine is output, so as to further correct the measured wind direction, so that the average error between the corrected wind direction and the actual wind direction is within the preset error range, such as the average error being less than the preset error value. The corrected wind direction is used in the yaw control to achieve more accurate yawing to the wind, obtain more wind energy, and improve the power generation capacity and the power generation efficiency of the wind turbine.

[0063] S140, identifying and correcting the wind direction measurement deviation of the wind turbine according to the reference wind direction measurement deviation characteristic curve information.

[0064] Specifically, the mapping relationship between the wind speed and the wind direction measurement deviation is obtained by using the determined reference wind direction measurement deviation characteristic curve information, i.e., the actual wind speed is input into the reference wind direction measurement deviation characteristic curve, and the wind direction measurement deviation is output. The measured wind direction is corrected and compensated for measurement deviation, so that the average error between the corrected wind direction and the actual wind direction is within the preset error range, such as the average error being less than the preset error value. The corrected wind direction is used in the yaw control to achieve more accurate yawing to the wind, obtain more wind energy, and improve the power generation capacity and the power generation efficiency of the wind turbine.

[0065] In one possible embodiment, identifying the wind direction measurement deviation of the wind turbine according to the reference wind direction measurement deviation characteristic curve information can include the following steps D1-D2:

[0066] Step D1, determining the wind direction measurement deviation of the wind turbine corresponding to the target wind speed measured at the target time point according to the reference wind direction measurement deviation characteristic curve information;

[0067] Step D2, correcting the wind direction measured at the target wind speed according to the wind direction measurement deviation of the wind turbine corresponding to the target wind speed to obtain the corrected wind direction, and using the corrected wind direction for the yawing to the wind control of the wind turbine.

[0068] The target wind speed can be the actual wind speed after the reference processing of the actual wind speed, so that the actual wind speed is unified as the wind speed under the reference air density. According to the wind direction measurement deviation of the wind turbine corresponding to the target wind speed and the wind direction measured at the target wind speed, the corrected wind direction is obtained, and is used for the yawing to the wind control of the wind turbine, so as to achieve more accurate yawing to the wind of the wind turbine and improve the power generation performance of the wind turbine.

[0069] By referring to the wind direction measurement deviation characteristic curve information, the wind direction measurement deviation of the wind turbine corresponding to the target wind speed measured at the target time point is determined, and the corrected wind direction is determined according to the wind direction measurement deviation of the wind turbine corresponding to the target wind speed and the measured wind direction at the target wind speed, and is used for the yaw-to-wind control of the wind turbine, so that more accurate yaw-to-wind effect of the wind turbine is realized, and the power generation performance of the wind turbine is improved.

[0070] In the technical scheme, after the reference wind direction measurement deviation characteristic curve information is determined, the wind direction measurement deviation of the wind turbine corresponding to the target wind speed measured at the target time point can be determined on the reference wind direction measurement deviation characteristic curve information, and the traditional wind direction measurement deviation identification can only obtain one corresponding wind direction measurement deviation at all wind speeds, so that the wind direction measurement deviation at different target wind speeds can be more accurately identified, and in addition, the reference wind direction measurement deviation characteristic curve information is optimized and updated by using the real-time acquired online running data of the wind turbine, so that the reference wind direction measurement deviation characteristic curve information used for judging the wind direction measurement deviation is more consistent with the actual running condition of the wind turbine, and therefore, the obtained wind direction measurement deviation is more accurate.

[0071] In the technical scheme, by determining the plurality of reference running information of the wind turbine, the reference running characteristic surface information of the wind turbine is obtained by surface fitting the power, the wind speed and the wind direction corresponding to each reference running information; the reference wind direction measurement deviation characteristic curve information of the wind turbine is determined according to the reference running characteristic surface information; the wind direction measurement deviation characteristic curve information is used to describe a continuous curve between the wind speed and the wind direction measurement deviation corresponding to the maximum power point of the wind turbine at the corresponding wind speed; the wind direction measurement deviation of the wind turbine is identified and corrected according to the reference wind direction measurement deviation characteristic curve information, and the measured wind direction is corrected, and then the corrected wind direction is obtained, and is used for the yaw-to-wind control of the wind turbine, so that more accurate yaw-to-wind effect of the wind turbine is realized, and the power generation performance of the wind turbine is improved.

[0072] Embodiment two

[0073] Figure 2 A flowchart of a wind direction measurement deviation identification method of a wind turbine according to the embodiment two of the present application is provided, and the embodiment is a detailed description of S110 in the embodiment one. As shown in the figure, the method comprises the following steps. Figure 2

[0074] S210, acquiring initial running information of the wind turbine measured in a reference preset time period.

[0075] ​Specifically, the data of the wind turbine during operation is stored in the background data center, and the initial operating information of the wind turbine for a preset time period is extracted from the background data center.

[0076] S220. Preprocess the initial operating information to obtain the reference operating information; wherein, the preprocessing includes removing unusable operating information from the initial operating information and / or performing benchmark processing on the initial operating information.

[0077] Optionally, abnormal operation information includes at least one of the following: wind turbine standby data, wind turbine start-up and shutdown data, wind turbine power limitation data, wind turbine communication anomaly data, wind turbine fault operation data, and out-of-systems operation data. Baseline processing includes uniformly processing the initial wind speed in the initial operation information into a reference wind speed under a baseline air density.

[0078] The reference air density can refer to the mass of a unit volume of air at 0°C and 1 standard atmosphere, which is 1.225 kg / m³. 3 .

[0079] Optionally, the initial operating information is the operating data of the wind turbine during operation. However, the operation of wind turbines is complex. For example, the air density is different under different wind conditions. For instance, the wind speed in the initial operating information is not under the same air density. Therefore, when comparing wind speeds, it will be affected by air density and other factors. So, the initial wind speed in the initial operating information is uniformly processed into a reference wind speed under the benchmark air density.

[0080] Specifically, calculate the initial air density corresponding to the initial wind speed, and then calculate the reference wind speed based on the initial air density corresponding to the initial wind speed and the reference air density. The formula for calculating the initial air density is:

[0081]

[0082] Where ρ is the initial air density, B is the atmospheric pressure, R is the gas constant, taken as 287 J / (kg*K), and T is the ambient temperature.

[0083] The formula for calculating the reference wind speed is:

[0084]

[0085] Among them, v * The reference wind speed is given by the baseline air density, v is the initial wind speed given the initial air density, and ρ is the initial wind speed given the baseline air density. * The baseline air density is 1.225 kg / m³. 3 .

[0086] In one possible implementation, the preprocessing of the initial operation information can include the following steps E1-E5:

[0087] Step E1, obtaining the operation data corresponding to the standby time period or the start-stop time period from the initial operation information and determining the wind turbine standby data and the start-stop data for elimination.

[0088] Step E2, obtaining the operation data corresponding to the power limit time period from the initial operation information and determining the wind turbine power limit data for elimination.

[0089] Step E3, obtaining the operation data corresponding to the communication abnormal time period from the initial operation information and determining the wind turbine communication abnormal data.

[0090] Step E4, obtaining the operation data corresponding to the fault time period from the initial operation information and determining the fault operation data for elimination.

[0091] Step E5, obtaining the operation data of the target density area from the initial operation characteristic surface drawn from the initial operation information and determining the outlier operation data for elimination; the operation data of the target density area includes operation data whose number in the neighborhood within the neighborhood distance threshold is lower than the preset number. The wind turbine standby data can be the data corresponding to the standby time period of the wind turbine, for example, the data of the wind turbine power being zero or the blade angle being at the shutdown position. The wind turbine start-stop data can be the data of the wind turbine in the start-stop time period, for example, the data of the wind turbine in the start-up power increasing stage and the shutdown power decreasing stage. The wind turbine power limit data can be the data of the wind turbine in the power limit time period, for example, the data of the wind turbine not being full-load and the blade angle not being at the optimal pitch angle. The wind turbine communication abnormal data can be the data of the wind turbine in the communication abnormal time period, for example, the data of the data center signal abnormality or the data being constant.

[0092] Specifically, the standby data of the wind turbine, the start-stop data of the wind turbine, the power limit data of the wind turbine, the communication abnormal data of the wind turbine, the fault operation data of the wind turbine and the outlying operation data contained in the initial operation information will cause the reference operation characteristic surface obtained by fitting to be not accurate, and then affect the identification result of the wind direction measurement deviation, so it is necessary to eliminate the abnormal operation information. For example, the outlying operation data can be eliminated by using an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. The DBSCAN clustering algorithm is based on a set of parameters to describe the closeness of samples, and the improved DBSCAN clustering algorithm can adaptively select the set of parameters, so that the outlying operation data can be accurately obtained and the outlying data can be eliminated.

[0093] The technical solution ensures that there is no abnormal operation information in the reference operation information by preprocessing the initial operation information, and the DBSCAN clustering algorithm can more accurately locate the outlying operation data and eliminate the outlying operation data, so that the accuracy of the reference operation information can be more accurately ensured, which is beneficial to subsequent determination of the reference operation characteristic surface by fitting. In addition, after the reference processing of the initial operation information, the initial wind speed in the initial operation information can be uniformly processed into the reference wind speed under the reference air density, so that the comparison of the reference wind speed can be effectively performed.

[0094] Embodiment Three

[0095] Figure 3 is a flowchart of a wind direction measurement deviation identification method of a wind turbine according to Embodiment Three of the present application. This embodiment is a detailed description of S130 in Embodiment One. As shown in Figure 3 The method comprises the following steps:

[0096] S310, cutting the reference operation characteristic surface at different discrete wind speed conditions with a preset wind speed value as a step to obtain corresponding cutting curves at different discrete wind speed conditions.

[0097] The preset wind speed value is a step length for cutting the reference operating characteristic surface information. Taking the preset wind speed value in the preset range can ensure that more cutting curves corresponding to different discrete wind speed conditions are obtained, and thus the obtained data can be more representative. The preset range can be set according to the amount of data actually obtained, for example, if the actual wind speed range is 0-15 m / s, the preset range can be set as 0.1 m / s-1.0 m / s, and the preset wind speed value can be selected as any wind speed value in the range. For example, if the preset wind speed value is 0.5 m / s, the operating characteristic surface can be cut at a step length of 0.5 m / s to obtain cutting curves corresponding to discrete wind speed conditions of 0.5 m / s, 1 m / s and 1.5 m / s.

[0098] In S320, a regression method is used to re-optimize the cutting curves corresponding to different discrete wind speed conditions, and the wind direction measurement deviation of the wind turbine when the wind turbine reaches the maximum power under the discrete wind speed condition corresponding to the cutting curve is obtained by using the wind direction corresponding to the maximum power on the optimized cutting curve.

[0099] Specifically, the operating characteristic surface is cut at a step length of the preset wind speed value to obtain cutting curves corresponding to different discrete wind speed conditions. A plurality of powers and wind directions can be obtained from the cutting curves. In theory, the closer the actual wind direction is to 0 degrees, the larger the windward surface of the wind turbine, and the more wind energy the wind turbine can capture, and thus the output power is larger. Therefore, the wind direction value corresponding to the maximum power on the cutting curve is selected as the wind direction measurement deviation value.

[0100] For example, the preset wind speed value step length is 0.5 m / s, the cutting curves corresponding to discrete wind speed conditions of 0.5 m / s, 1 m / s and 1.5 m / s are determined, and a plurality of powers and a plurality of wind directions corresponding to the discrete wind speeds of 0.5 m / s, 1 m / s and 1.5 m / s are obtained according to the cutting curves. The wind direction corresponding to the maximum power on the cutting curve is selected, and thus the wind directions of 3°, 3.5° and 3.8° corresponding to the maximum power of the discrete wind speeds of 0.5 m / s, 1 m / s and 1.5 m / s are obtained, and the wind directions of 3°, 3.5° and 3.8° are used as the wind direction measurement deviation.

[0101] In S330, a regression method is used to fit the wind direction measurement deviation of the wind turbine when the wind turbine reaches the maximum power under different discrete wind speed conditions into reference wind direction measurement deviation characteristic curve information of the wind turbine.

[0102] The regression method can be a moving least squares curve fitting algorithm.

[0103] For example, the discrete wind speeds 0.5 m / s, 1 m / s and 1.5 m / s, etc. are respectively 3°, 3.5° and 3.8°, etc. The maximum power corresponding to the wind direction measurement deviation, then the reference wind direction measurement deviation characteristic curve information of the wind turbine can be obtained by regression method according to (0.5 m / s, 3°), (1 m / s, 3.5°) and (1.5 m / s, 3.8°), etc.

[0104] In the technical solution, after the reference wind direction measurement deviation characteristic curve information is determined, the wind direction measurement deviation under different actual wind speeds can be accurately identified according to the wind direction measurement deviation corresponding to each actual wind speed on the reference wind direction measurement deviation characteristic curve information. In addition, after the reference operation characteristic surface information is obtained, the reference wind direction measurement deviation characteristic curve information may become inaccurate compared with the actual situation over time. Therefore, new online operation characteristic surface information is continuously generated by using the wind turbine online operation data obtained in real time, and the reference operation characteristic surface is optimized and updated through the iteration weighting of the online operation characteristic surface information over time, so that the reference wind direction measurement deviation characteristic curve information used to determine the wind direction measurement deviation is more consistent with the actual operation of the wind turbine.

[0105] Embodiment Four

[0106] Figure 4 A structural schematic diagram of a wind direction measurement deviation identification device of a wind turbine provided according to Embodiment Four of the present application is shown in FIG. 4. As shown in FIG. 4, the device includes: Figure 4

[0107] A first information determination module 410 is configured to determine a plurality of reference operation information of the wind turbine. The reference operation information includes reference power, reference wind speed and reference wind direction measured in a reference time period.

[0108] A second information determination module 420 is configured to obtain reference operation characteristic surface information of the wind turbine by surface fitting of the power, wind speed and wind direction corresponding to each reference operation information.

[0109] A third information determination module 430 is configured to determine reference wind direction measurement deviation characteristic curve information of the wind turbine according to the reference operation characteristic surface information. The wind direction measurement deviation characteristic curve information is used to describe a continuous curve between the wind speed and the wind direction measurement deviation corresponding to the maximum power point of the wind turbine under the corresponding wind speed.

[0110] A control module 440 is configured to identify and correct the wind direction measurement deviation of the wind turbine according to the reference wind direction measurement deviation characteristic curve information.

[0111] ​Optionally, the first information determining module is specifically configured to:

[0112] acquire initial operation information of the wind turbine measured in a reference preset time period;

[0113] preprocess the initial operation information to obtain the reference operation information;

[0114] wherein the preprocessing comprises eliminating abnormal operation information in the initial operation information and / or performing benchmark processing on the initial operation information; the abnormal operation information comprises at least one of the following: wind turbine standby data, wind turbine start-stop machine data, wind turbine limited power data, wind turbine communication abnormal data, wind turbine fault operation data, and outlying operation data, and the benchmark processing comprises uniformly processing initial wind speed in the initial operation information into reference wind speed under benchmark air density.

[0115] Optionally, the first information determining module further comprises a preprocessing unit, which is specifically configured to:

[0116] acquire operation data corresponding to a standby time period or a start-stop machine time period of the wind turbine from the initial operation information and determine the operation data as wind turbine standby data and start-stop machine data for elimination;

[0117] acquire operation data corresponding to a limited power time period of the wind turbine from the initial operation information and determine the operation data as wind turbine limited power data for elimination;

[0118] acquire operation data corresponding to a communication abnormal time period of the wind turbine from the initial operation information and determine the operation data as wind turbine communication abnormal data for elimination;

[0119] acquire operation data corresponding to a fault time period of the wind turbine from the initial operation information and determine the operation data as fault operation data for elimination;

[0120] acquire operation data of a target density area from an initial operation characteristic surface drawn from the initial operation information and determine the operation data as outlying operation data for elimination; the operation data of the target density area comprises operation data whose number in a neighborhood within a neighborhood distance threshold is lower than a preset number.

[0121] Optionally, the second information determining module is specifically configured to:

[0122] perform three-dimensional surface fitting on power, wind speed, and wind direction corresponding to each reference operation information of the wind turbine by using a three-dimensional surface fitting algorithm to obtain historical operation characteristic surface information of the wind turbine; the reference operation information corresponds to a reference time period which is a preset historical time period;

[0123] The historical operation characteristic surface information is determined as reference operation characteristic surface information of the wind turbine generator set.

[0124] Optionally, the second information determining module is specifically configured to:

[0125] The power, the wind speed and the wind direction corresponding to each reference operation information of the wind turbine generator set are fitted by using a three-dimensional surface fitting algorithm to obtain online operation characteristic surface information of the wind turbine generator set; the reference time period corresponding to the reference operation information is a preset online time period;

[0126] The online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting are weighted to obtain the reference operation characteristic surface information of the wind turbine generator set optimized by the online operation data.

[0127] Optionally, the second information determining module comprises an operation unit and is specifically configured to:

[0128] The first data amount corresponding to the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting and the second data amount corresponding to the online operation characteristic surface information are determined; the first data amount is the data amount of the operation data of the wind turbine generator set obtained by the last surface fitting, and the second data amount is the data amount of the operation data of the wind turbine generator set in the online time period;

[0129] The first weight of the first data amount in the sum of the first data amount and the second data amount is calculated, and the second weight of the second data amount in the sum of the first data amount and the second data amount is calculated;

[0130] The online operation characteristic surface information of the wind turbine generator set and the reference operation characteristic surface information of the wind turbine generator set obtained by the last surface fitting are weighted according to the first weight and the second weight.

[0131] Optionally, the third information determining module is specifically configured to:

[0132] The reference operation characteristic surface is cut at different discrete wind speed conditions with a preset wind speed value as a step to obtain corresponding cutting curves at different discrete wind speed conditions;

[0133] For the corresponding cutting curves at different discrete wind speed conditions, the cutting curves are re-fitted and optimized by using a regression method, and the wind direction measurement deviation of the wind turbine generator set when the power reaches the maximum value at the discrete wind speed condition corresponding to the cutting curve is obtained by using the wind direction corresponding to the maximum power value on the optimized cutting curve;

[0134] The wind direction measurement deviation of the wind turbine at different discrete wind speed conditions is fitted as the reference wind direction characteristic curve information of the wind turbine by using a regression method.

[0135] Optionally, the control module is specifically used for:

[0136] According to the reference wind direction measurement deviation characteristic curve information, the wind direction measurement deviation of the wind turbine corresponding to a target wind speed measured at a target time point is determined.

[0137] According to the wind direction measurement deviation of the wind turbine corresponding to the target wind speed, the wind direction measured at the target wind speed is corrected to obtain a corrected wind direction, which is used for the yaw-to-wind control of the wind turbine.

[0138] The wind direction measurement deviation identification device of the wind turbine provided in the embodiments of the present application can execute the wind direction measurement deviation identification method of the wind turbine provided in any of the embodiments of the present application, has the corresponding functions and advantages of executing the wind direction measurement deviation identification method of the wind turbine, and the detailed processes are referred to the related operations of the wind direction measurement deviation identification method of the wind turbine in the foregoing embodiments.

[0139] Embodiment five

[0140] Figure 5 A structural schematic diagram of an electronic device that can be used to implement the wind direction measurement deviation identification method of the wind turbine of the embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0141] As Figure 5As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0142] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0143] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the wind direction measurement bias identification method for a wind turbine.

[0144] In some embodiments, the wind direction measurement bias identification method for a wind turbine can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the wind direction measurement bias identification method for a wind turbine described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the wind direction measurement bias identification method for a wind turbine by any other appropriate means, such as by means of firmware.

[0145] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0146] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a standalone software package and partially on a remote machine or entirely on a remote machine or server.

[0147] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0148] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0149] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0150] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0151] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0152] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A method for identifying wind direction measurement deviation in wind turbine units, characterized in that, The method includes: Determine multiple reference operating information for the wind turbine generator set; the reference operating information includes reference power, reference wind speed, and reference wind direction measured over a reference preset time period. The reference operating characteristic surface information of the wind turbine generator is obtained by performing surface fitting on the power, wind speed and wind direction corresponding to each of the reference operating information. The reference wind direction measurement deviation characteristic curve information of the wind turbine generator set is determined based on the reference operating characteristic surface information; the reference wind direction measurement deviation characteristic curve information is used to describe the relationship between wind speed and wind direction measurement deviation corresponding to the maximum power point of the wind turbine generator set under the corresponding wind speed. The wind direction measurement deviation of the wind turbine generator is identified and corrected based on the reference wind direction measurement deviation characteristic curve information.

2. The method according to claim 1, characterized in that, The determination of multiple reference operating information for the wind turbine generator set includes: Obtain initial operating information of the wind turbine generator set measured within a reference preset time period; The reference operating information is obtained by preprocessing the initial operating information. The preprocessing includes removing abnormal operating information from the initial operating information and / or performing benchmark processing on the initial operating information; the abnormal operating information includes at least one of the following: wind turbine standby data, wind turbine start-up and shutdown data, wind turbine power limit data, wind turbine communication abnormal data, wind turbine fault operation data and outlier operation data; the benchmark processing includes uniformly processing the initial wind speed in the initial operating information into a reference wind speed under a benchmark air density.

3. The method according to claim 2, characterized in that, Preprocessing the initial running information includes: The operating data of the wind turbine generator set during the standby period or start-up / shutdown period is obtained from the initial operating information and identified as standby data and start-up / shutdown data of the wind turbine generator set for elimination. The operating data of the wind turbine generator set during the power limitation period are obtained from the initial operating information and identified as power limitation data of the wind turbine generator set for elimination. The operating data of the wind turbine generator set during the communication abnormality period are obtained from the initial operating information and identified as communication abnormality data of the wind turbine generator set for elimination. The operating data of the wind turbine generator set during the fault period are obtained from the initial operating information and identified as faulty operating data for removal. The running data of the target density region is obtained from the initial running characteristic surface drawn from the initial running information and identified as outlier running data for removal; the running data of the target density region includes running data in the neighborhood where the number of running data in the neighborhood is less than a preset number within the neighborhood distance threshold.

4. The method according to claim 1, characterized in that, The reference operating characteristic surface information of the wind turbine generator is obtained by performing surface fitting on the power, wind speed, and wind direction corresponding to each of the aforementioned reference operating information, including: A three-dimensional surface fitting algorithm is used to perform three-dimensional surface fitting on the power, wind speed and wind direction corresponding to each reference operating information of the wind turbine generator set to obtain the historical operating characteristic surface information of the wind turbine generator set; the reference time period corresponding to the reference operating information is a preset historical time period. The historical operating characteristic surface information is determined as the reference operating characteristic surface information of the wind turbine generator set.

5. The method according to claim 1, characterized in that, The reference operating characteristic surface information of the wind turbine generator is obtained by performing surface fitting on the power, wind speed, and wind direction corresponding to each of the aforementioned reference operating information, including: A three-dimensional surface fitting algorithm is used to perform three-dimensional surface fitting on the power, wind speed and wind direction corresponding to each reference operating information of the wind turbine generator set to obtain the online operating characteristic surface information of the wind turbine generator set; the reference time period corresponding to the reference operating information is a preset online time period. The online operating characteristic surface information of the wind turbine generator set is weighted with the reference operating characteristic surface information of the wind turbine generator set obtained by the previous surface fitting to obtain the reference operating characteristic surface information of the wind turbine generator set optimized by online operating data; the previous surface fitting is the process of using a three-dimensional surface fitting algorithm to perform three-dimensional surface fitting on the power, wind speed and wind direction corresponding to each operating information of the wind turbine generator set in a preset historical time period.

6. The method according to claim 1, characterized in that, The reference wind direction measurement deviation characteristic curve information of the wind turbine generator set is determined based on the reference operating characteristic surface information, including: The reference operating characteristic surface is cut with a preset wind speed value as the step size under different discrete wind speed conditions to obtain the corresponding cutting curves under different discrete wind speed conditions. For the corresponding cutting curves under different discrete wind speed conditions, the regression method is used to refit and optimize the cutting curves. The wind direction measurement deviation when the wind turbine reaches the maximum power under the corresponding discrete wind speed conditions is obtained by using the wind direction corresponding to the maximum power on the optimized cutting curve. The wind direction measurement deviation when the wind turbine reaches its maximum power under different discrete wind speed conditions is fitted into the reference wind direction measurement deviation characteristic curve information of the wind turbine by using the regression method.

7. The method according to claim 1, characterized in that, Based on the reference wind direction measurement deviation characteristic curve information, the wind turbine generator set is subjected to wind direction measurement deviation identification and correction, including: Based on the reference wind direction measurement deviation characteristic curve information, determine the wind direction measurement deviation corresponding to the wind turbine generator set at the target wind speed measured at the target time point; The wind direction measured at the target wind speed is corrected based on the wind direction measurement deviation corresponding to the wind turbine generator set at the target wind speed, and the corrected wind direction is used for the yaw control of the wind turbine generator set.

8. A wind direction measurement deviation identification device for a wind turbine generator set, characterized in that, include: The first information determination module is used to determine multiple reference operating information of the wind turbine generator set; The reference operating information includes reference power, reference wind speed, and reference wind direction measured during the reference time period; The second information determination module is used to obtain the reference operating characteristic surface information of the wind turbine generator by performing surface fitting on the power, wind speed and wind direction corresponding to each of the reference operating information. The third information determination module is used to determine the reference wind direction measurement deviation characteristic curve information of the wind turbine generator set based on the reference operating characteristic surface information. The reference wind direction measurement deviation characteristic curve information is a continuous curve used to describe the relationship between wind speed and the wind direction measurement deviation corresponding to the maximum power point of the wind turbine generator at the corresponding wind speed. The control module is used to identify and correct the wind direction measurement deviation of the wind turbine generator set based on the reference wind direction measurement deviation characteristic curve information.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the wind direction measurement deviation identification method for wind turbines according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the wind direction measurement deviation identification method for wind turbine units as described in any one of claims 1-7.

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