Wind speed error diagnosis method and system for anemometer of wind driven generator and medium

Abstract

The invention relates to a wind speed error diagnosis method and system of a wind driven generator anemometer and a medium, and the wind speed error diagnosis method of the wind driven generator anemometer comprises the following steps: obtaining historical operation data of an SCADA system of a wind driven generator, the historical operation data is instantaneous data; performing abnormal value detection according to the acquired historical operation data and completing data cleaning; performing power calibration according to local altitude and air temperature, and calculating effective wind speed according to a standard power curve and time-average information; and calculating a wind speed error according to the effective wind speed and the actually measured average wind speed, and diagnosing the wind speed error of the anemometer to obtain a diagnosis result. Based on SCADA data, the method is fast in calculation, low in operation cost and high in universality.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine fault diagnosis, and in particular to a wind speed error diagnosis method, system and medium of an anemometer of a wind turbine. Background Art

[0002] The operation and maintenance (O&M) of wind turbines is a key variable in improving the competitiveness of the wind energy industry. As the core indicator for actual power estimation and expected power assessment, the deviation of wind speed measurement by anemometers not only has a huge impact on the performance assessment of wind turbines, but also seriously affects the estimation of the annual energy production (AEP) of wind farms.

[0003] Standard wind speed measurement is done by a cup anemometer or ultrasonic anemometer located behind the rotor and above the cabin. The free stream wind speed is estimated afterwards through the cabin transfer function. The cup anemometer is a wind speed measurement method based on momentum transfer. The wind speed drives the shaft, and the magnet on the shaft rotates to produce a change in the magnetic field. The magnetic sensor senses the change in the magnetic field and converts it into an electrical signal, which is processed to obtain the wind speed. The ultrasonic anemometer measures wind speed based on the characteristic that the propagation speed of ultrasonic waves in the air is affected by wind speed. The wind speed and wind direction are calculated by measuring the propagation time of ultrasonic waves in different directions.

[0004] Anemometers are exposed to the atmosphere for a long time, and are affected by blade turbulence, the cabin boundary layer, and harsh environments such as rainfall and sandstorms. In addition, due to the aging of the instrument itself, it is not uncommon for anemometers to fail to accurately measure wind speed. Therefore, anemometer diagnosis is very important. Considering the economic cost, these environmental influences cannot be fully measured in the laboratory. Therefore, a method is urgently needed to diagnose the anemometer error to improve the power performance of wind turbines. Summary of the invention

[0005] The present invention provides a wind speed error diagnosis method, system and medium for an anemometer of a wind turbine generator, so as to solve the problem in the prior art that the anemometer is exposed to the atmospheric environment for a long time and the anemometer cannot accurately measure the wind speed.

[0006] To achieve the above object, in a first aspect, the present invention relates to a method for diagnosing wind speed errors of an anemometer of a wind turbine generator, which is used for diagnosing wind speed errors of a cup anemometer or a sonic anemometer of a wind turbine generator, comprising:

[0007] Acquire historical operation data of the SCADA system of the wind turbine, wherein the historical operation data is instantaneous data;

[0008] Perform outlier detection and complete data cleaning based on the acquired historical operation data;

[0009] Perform power calibration according to the local altitude and temperature, reconstruct a standard power curve according to the standard power information, calculate time-averaged information according to the instantaneous data, and calculate an effective wind speed according to the standard power curve and the time-averaged information;

[0010] The wind speed error is calculated according to the effective wind speed and the measured average wind speed, and the wind speed error of the anemometer is diagnosed to obtain a diagnosis result.

[0011] To achieve the above object, in a second aspect, the present invention relates to a wind speed error diagnosis system for an anemometer of a wind turbine generator, which is used for wind speed error diagnosis of a cup anemometer or a sonic anemometer of a wind turbine generator, and comprises:

[0012] A data extraction module is used to obtain historical operation data of the SCADA system of the wind turbine, wherein the historical operation data is instantaneous data;

[0013] A data cleaning module, used to perform outlier detection and complete data cleaning based on the acquired historical operation data;

[0014] An effective wind speed calculation module, used to perform power calibration according to the local altitude and temperature, reconstruct a standard power curve according to standard power information, calculate time-averaged information according to the instantaneous data, and calculate the effective wind speed according to the standard power curve and the time-averaged information;

[0015] The error calculation and diagnosis module is used to calculate the wind speed error according to the effective wind speed and the measured average wind speed, and diagnose the wind speed error of the anemometer to obtain a diagnosis result.

[0016] To achieve the above objectives, the third aspect of the present invention also relates to a computer-readable storage medium, in which instructions are stored, and when the instructions are executed, the above-mentioned method for diagnosing wind speed errors of an anemometer of a wind turbine is executed.

[0017] The wind speed error diagnosis method, system and medium of a wind turbine anemometer according to the present invention have the following beneficial effects compared with the prior art:

[0018] The present invention is based on SCADA data, does not require additional equipment costs, is easy to use, has fast calculation, low operating costs, and high universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a wind turbine structure and an anemometer installation position of a wind turbine anemometer in a wind speed error diagnosis method of a wind turbine anemometer in Embodiment 1.

[0020] Figure 2 Schematic diagram of a cup anemometer and a sonic anemometer in a method for diagnosing wind speed errors of an anemometer of a wind turbine in Embodiment 1.

[0021] Figure 3 A wind speed error diagnosis method for an anemometer of a wind turbine in the first embodiment is a schematic diagram of different regions of a wind turbine power curve.

[0022] Figure 4 In the first embodiment, a method for diagnosing wind speed error of an anemometer of a wind turbine is a schematic diagram of a power curve of a wind turbine affected by wind speed error.

[0023] Figure 5 A schematic flow chart of a method for diagnosing wind speed errors of an anemometer of a wind turbine in Embodiment 1.

[0024] Figure 6 A schematic diagram of a four-segment quadratic polynomial wind turbine power curve fitting method for a wind speed error diagnosis method of a wind turbine anemometer in Embodiment 1.

[0025] Figure 7 A schematic diagram of an effective wind speed calculation method of a wind speed error diagnosis method of an anemometer of a wind turbine in Embodiment 1;

[0026] Figure 8 A schematic diagram of the structure of a wind speed error diagnosis system for an anemometer of a wind turbine in Embodiment 2 Figure 1 ;

[0027] Fig. 9 The structure of a wind speed error diagnosis system of an anemometer of a wind turbine in the second embodiment of the present invention is schematically shown. Figure 2 ;

[0028] Fig.10 The power curve fitted after the anemometer wind measurement error data is calibrated in Example 1 and the standard power curve. DETAILED DESCRIPTION

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only the parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0030] Embodiment 1

[0031] A wind speed error diagnosis method for an anemometer of a wind turbine generator, see Figure 1 After the free flow passes through the wind turbine hub 1 and the blades 2, the anemometer 4 located above the nacelle 5 completes the wind speed measurement, and the measurement result is uploaded to the SCADA system and saved.

[0032] See also Figure 2 The commonly used anemometer is the cup anemometer Figure 2 (a) and sonic anemometer Figure 2 (b) The incoming flow drives the wind cup 6 to rotate around the rotating shaft 7, which is converted into a data signal by the wind speed sensor 8. The sonic anemometer uses a pair of sonic probes 9 to transmit and receive sonic signals, and the control circuit 10 processes the signal to obtain the wind speed.

[0033] The anemometer is exposed to the atmosphere for a long time and is affected by blade turbulence, cabin boundary layer, rain, sand and other harsh environments. It is not uncommon for the anemometer to measure wind speed inaccurately. Figure 4 The standard power curve 11 of a wind turbine consists of three parts, see Figure 5 In area 1, the wind speed is lower than the cut-in wind speed, and the wind turbine does not work; when the wind speed increases and exceeds the cut-in wind speed, the wind turbine starts to work, and the power curve is in area 2; when the wind speed is greater than the rated wind speed and enters area 3, the wind turbine starts to change the pitch, and the wind turbine works at the rated power state. When the anemometer underestimates the wind speed and is affected by turbulence, the power curve 12 is on the left side of the standard power curve 11; and when the anemometer overestimates the wind speed and is affected by turbulence, the power curve 13 is on the right side of the standard power curve 11.

[0034] like Figure 5 As shown, a wind speed error diagnosis method of a wind turbine anemometer of the present invention is used for wind speed error diagnosis of a cup anemometer or a sonic anemometer of a wind turbine, and includes the following steps: S101 to S106.

[0035] S101 obtains historical operation data of the SCADA system of the wind turbine, where the historical operation data is instantaneous data.

[0036] Specifically, the historical operation data includes at least: wind turbine operation time record, pitch angle of the first blade, pitch angle of the second blade, pitch angle of the third blade, impeller speed, real-time wind speed, ambient temperature and active power.

[0037] In some embodiments, the historical operating data may further include at least one of atmospheric pressure and air density.

[0038] S102 performs outlier detection based on the acquired historical operation data and completes data cleaning.

[0039] Among them, S102 specifically includes S121 to S122:

[0040] S121 performs outlier detection on historical operation data based on the clustering-based DBSCAN method;

[0041] S122 completes the cleaning of historical operation data according to the results of abnormal value detection.

[0042] S103 performs power calibration based on the local altitude and temperature, reconstructs the standard power curve based on the standard power information, calculates the time-averaged information based on the instantaneous data, and calculates the effective wind speed based on the standard power curve and the time-averaged information. Figure 7 , which shows a flow chart for integrating environmental influences and completing effective wind speed calculation based on wind turbine operating data and power curves.

[0043] like Figure 7 As shown, S103 may specifically include S131 to S134:

[0044] S131 completes the active power calibration of historical operating data based on the local altitude and temperature of the wind turbine:

[0045] This embodiment integrates environmental influences (gas state and turbulence intensity) and completes effective wind speed calculation based on wind turbine operation data and power curves, specifically including:

[0046] Get the local altitude h of the wind turbine local And the hub height h of the wind turbine hub , the calculated height of the wheel hub is h = h local +h hub ;

[0047] According to the Clapeyron equation, using the formula Calculate the ambient air density at different temperatures and altitudes; where ρ is the ambient air density, h is the altitude of the hub, and T represents the local temperature;

[0048] Using the ambient air density ρ and the formula Complete the calibration of active power, where P c is the correction power, P a is the measured active power, ρ s Represents standard air density.

[0049] S132 reconstructs the standard power curve of the wind turbine based on the standard power information:

[0050] Obtain standard power curve information, requiring that the wind turbine power curve parameters obtained include at least: cut-in wind speed, power corresponding to the cut-in wind speed, rated wind speed, rated power corresponding to the rated wind speed, cut-out wind speed, an intermediate wind speed between the cut-in wind speed and the rated wind speed, and power corresponding to the intermediate wind speed;

[0051] The power curve fitting and reconstruction is completed according to the four-segment quadratic polynomial. The specific expression is as follows:

[0052]

[0053] Where u represents the wind speed, u in is the cut-in wind speed, u med is the middle wind speed between the second and third segments, u rate is the rated wind speed, u out is the cut-out wind speed; P(u) is the power, P1(u) is the power corresponding to the second section, P2(u) is the power corresponding to the third section, P rate is the rated power; a1, a2, a3 are the fitting parameters of the second section, b1, b2, b3 are the fitting parameters of the third section;

[0054] The fitting parameters a1, a2, and a3 have the following relationship:

[0055]

[0056] The relationship between the fitting parameters b1, b2, and b3 is:

[0057]

[0058] S133 obtains the time-averaged information based on the instantaneous data after the active power calibration:

[0059] Using the formula Calculate the 10-minute average wind speed; where u is the average wind speed, u i is the instantaneous wind speed within 10 minutes, and N is the number of instantaneous wind speed samples within 10 minutes, where the real-time wind speed in the instantaneous data is the instantaneous wind speed;

[0060] Using the formula Calculate the average power for 10 minutes; where P is the average power, P i is the instantaneous power within 10 minutes, and N is the number of instantaneous power samples within 10 minutes, where instantaneous power is the active power in the instantaneous data;

[0061] Using the formula Calculate the 10-minute wind speed standard deviation; where σ is the wind speed standard deviation, u is the average wind speed, and u i is the instantaneous wind speed, N is the number of wind speed samples;

[0062] S134 calculates the effective wind speed using the dichotomy method based on the standard power curve and time-averaged information:

[0063] According to the formula P=X1+X2+X3+X4+X5, the expression of effective wind speed is obtained. The effective wind speed is calculated by dichotomy based on the expression of effective wind speed, where P is the average power, and the specific expressions of X1, X2, X3, X4, and X5 are:

[0064]

[0065] Among them, P rate is the rated power, a1, a2, a3, b1, b2, b3 are fitting parameters, σ is the standard deviation of wind speed, x is the integral variable, erf() is the Gaussian error function, l1, l2, l3, l4 are the upper and lower limits of the integral of different segments, is the effective wind speed to be estimated. The specific form of the upper and lower limits of the integral is:

[0066]

[0067] Among them, u in is the cut-in wind speed, u1 is the two-segment wind speed or the three-segment wind speed, u rate is the rated wind speed, u out To cut out wind speed.

[0068] S104 calculates the wind speed error according to the effective wind speed and the measured average wind speed, and diagnoses the wind speed error of the anemometer to obtain a diagnosis result.

[0069] In this embodiment, the formula Calculate the wind speed error; where Δu is the calculated wind speed error, is the effective wind speed, u is the average wind speed;

[0070] Count all sample wind speed errors and use the Gaussian fitting formula Obtain statistical wind speed errors; where a, b, and c are all fitting parameters. The wind speed error distribution in multiple time periods is approximately Gaussian. This model uses the mean parameter b obtained by Gaussian fitting as the wind speed error of the anemometer. Diagnose the wind speed error of the anemometer to obtain diagnostic results. Based on the diagnostic results obtained by error diagnosis, the wind measurement error of the anemometer can be calibrated. Fig.10 The power curve fitted by the calibrated data (under low turbulence intensity and calibrated density) is compared with the standard power curve. It can be seen that the difference in the power curve of the wind turbine is significantly reduced, and the local difference is affected by environmental factors. In addition, under a nearly standard environment, the difference between the fitted curve and the standard power curve is very small. Therefore, it is concluded that this method has a good detection and calibration effect on the wind measurement error of the anemometer.

[0071] Embodiment 2

[0072] A wind speed error diagnosis system for an anemometer of a wind turbine generator is used for wind speed error diagnosis of a cup anemometer or an acoustic anemometer of a wind turbine generator. It is implemented by hardware of an electronic device with a central processor and can be implemented by a personal computer, a smart terminal, a local area network, a server, etc. For implementation in this embodiment, please refer to Figure 8, including a data extraction module 61, a data cleaning module 62, an effective wind speed calculation module 63 and an error calculation and diagnosis module 64.

[0073] The data extraction module 61 is used to obtain the historical operation data of the SCADA system of the wind turbine, and the historical operation data is instantaneous data.

[0074] The historical operation data includes at least: the operation time record of the wind turbine, the pitch angle of the first blade, the pitch angle of the second blade, the pitch angle of the third blade, the impeller speed, the real-time wind speed, the ambient temperature and the active power;

[0075] In this embodiment, the historical operation data may further include at least one of atmospheric pressure and air density.

[0076] The data cleaning module 62 is used to perform outlier detection and complete data cleaning based on the acquired historical operation data.

[0077] The effective wind speed calculation module 63 is used to perform power calibration according to the local altitude and temperature, reconstruct the standard power curve according to the standard power information, obtain the time-averaged information according to the instantaneous data, and calculate the effective wind speed according to the standard power curve and the time-averaged information.

[0078] The error calculation and diagnosis module 64 is used to calculate the wind speed error according to the effective wind speed and the measured average wind speed, and diagnose the wind speed error of the anemometer to obtain a diagnosis result.

[0079] Among them, the wind speed error is calculated, specifically including:

[0080] Using the formula Calculate the wind speed error; where Δu is the calculated wind speed error, is the effective wind speed, u is the average wind speed;

[0081] Count all sample wind speed errors and use the Gaussian fitting formula Obtain statistical wind speed errors; where a, b, and c are all fitting parameters, and the wind speed error distribution in multiple time periods is approximately Gaussian. This model uses the mean parameter b obtained by Gaussian fitting as the wind speed error of the anemometer. Diagnose the wind speed error of the anemometer to obtain diagnostic results.

[0082] In this embodiment, the effective wind speed calculation module 63 includes: a calibration data submodule 631 , a time-averaged information submodule 632 , and a standard power curve reconstruction submodule 633 .

[0083] The calibration data submodule 631 is used to calibrate the active power of the historical operation data according to the local altitude and temperature of the wind turbine:

[0084] The time-average information submodule 632 is used to obtain the time-average information according to the instantaneous data after the active power calibration, and the time-average information includes the average wind speed, the standard deviation of the wind speed and the average active power;

[0085] The standard power curve reconstruction submodule 633 is used to reconstruct the standard power curve of the wind turbine according to the standard power information:

[0086] The effective wind speed calculation submodule 634 is used to calculate the effective wind speed using the dichotomy method according to the standard power curve and the time-averaged information.

[0087] The wind speed error diagnosis system of a wind turbine anemometer in this embodiment has the same implementation process, method and effect as the wind speed error diagnosis method of a wind turbine anemometer described in the first embodiment, and will not be described in detail here.

[0088] Embodiment 3

[0089] The present invention relates to a computer-readable storage medium, in which instructions are stored. When the instructions are executed, a method for diagnosing a wind speed error of an anemometer of a wind turbine generator is implemented. The implementation process, method and effect thereof are the same as those of the method for diagnosing a wind speed error of an anemometer of a wind turbine generator described in Example 1, and will not be repeated here.

[0090] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0091] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A wind speed error diagnosis method for an anemometer of a wind turbine, characterized in that: Wind speed error diagnostics for wind turbine cup anemometers or sonic anemometers, including: Acquire historical operation data of the SCADA system of the wind turbine, wherein the historical operation data is instantaneous data; Perform outlier detection and complete data cleaning based on the acquired historical operation data; Perform power calibration according to the local altitude and temperature, reconstruct a standard power curve according to the standard power information, calculate time-averaged information according to the instantaneous data, and calculate an effective wind speed according to the standard power curve and the time-averaged information; The wind speed error is calculated according to the effective wind speed and the measured average wind speed, and the wind speed error of the anemometer is diagnosed to obtain a diagnosis result.

2. The wind speed error diagnosis method of a wind turbine anemometer according to claim 1, characterized in that: The historical operation data at least includes: the wind turbine operation time record, the pitch angle of the first blade, the pitch angle of the second blade, the pitch angle of the third blade, the impeller speed, the real-time wind speed, the ambient temperature and the active power.

3. The wind speed error diagnosis method of a wind turbine anemometer according to claim 2, characterized in that: The historical operating data also includes at least one of atmospheric pressure and air density.

4. The wind speed error diagnosis method of a wind turbine anemometer according to claim 1, characterized in that: The performing of outlier detection and data cleaning based on the acquired historical operation data specifically includes: Performing outlier detection on the historical operation data according to a clustering-based DBSCAN method; According to the result of the abnormal value detection, the cleaning of the historical operation data is completed.

5. The wind speed error diagnosis method of a wind turbine anemometer according to claim 2 or 3, characterized in that: The power calibration is performed according to the local altitude and temperature, the standard power curve is reconstructed according to the standard power information, the time-averaged information is calculated according to the instantaneous data, and the effective wind speed is calculated according to the standard power curve and the time-averaged information, specifically including: Step 1: Complete the calibration of the active power of the historical operation data according to the local altitude and temperature of the wind turbine: Get the local altitude h of the wind turbine local And the hub height h of the wind turbine hub , the calculated height of the wheel hub is h = h local +h hub ; Using the formula Calculate the ambient air density at different temperatures and altitudes; where ρ is the ambient air density, h is the altitude of the hub, and T represents the local temperature; Using the ambient air density ρ and the formula Complete the calibration of active power, where P c is the correction power, P a is the measured active power, ρ s represents standard air density; Step 2: Reconstruct the standard power curve of the wind turbine based on the standard power information: Obtain standard power curve information, requiring that the wind turbine power curve parameters obtained include at least: cut-in wind speed, power corresponding to the cut-in wind speed, rated wind speed, rated power corresponding to the rated wind speed, cut-out wind speed, an intermediate wind speed between the cut-in wind speed and the rated wind speed, and power corresponding to the intermediate wind speed; The power curve fitting and reconstruction is completed according to the four-segment quadratic polynomial. The specific expression is as follows: Where u represents the wind speed, u in is the cut-in wind speed, u med is the middle wind speed between the second and third segments, u rate is the rated wind speed, u out is the cut-out wind speed; P(u) is the power, P1(u) is the power corresponding to the second stage, P2(u) is the power corresponding to the third stage, P rate is the rated power; a1, a2, a3 are the fitting parameters of the second section, b1, b2, b3 are the fitting parameters of the third section; The fitting parameters a1, a2, and a3 have the following relationship: The relationship between the fitting parameters b1, b2, and b3 is: Step 3: Obtain time-averaged information based on the instantaneous data after active power calibration: Using the formula Calculate the 10-minute average wind speed; where u is the average wind speed, u i is the instantaneous wind speed within 10 minutes, and N is the number of instantaneous wind speed samples within 10 minutes, wherein the real-time wind speed in the instantaneous data is the instantaneous wind speed; Using the formula Calculate the average power for 10 minutes; where P is the average power, P i is the instantaneous power within 10 minutes, and N is the number of instantaneous power samples within 10 minutes, wherein the instantaneous power is the active power in the instantaneous data; Using the formula Calculate the 10-minute wind speed standard deviation; where σ is the wind speed standard deviation, u is the average wind speed, and u i is the instantaneous wind speed, N is the number of wind speed samples; Step 4: Calculate the effective wind speed using the dichotomy method based on the standard power curve and the time-averaged information: According to the formula P=X1+X2+X3+X4+X5, an expression for effective wind speed is obtained, and the effective wind speed is calculated using the dichotomy method according to the expression for effective wind speed, where P is the average power, and the specific expressions of X1, X2, X3, X4, and X5 are: Among them, P rate is the rated power, a1, a2, a3, b1, b2, b3 are fitting parameters, σ is the standard deviation of wind speed, x is the integral variable, erf() is the Gaussian error function, l1, l2, l3, l4 are the upper and lower limits of the integral of different segments, is the effective wind speed that needs to be estimated.

6. The wind speed error diagnosis method of a wind turbine anemometer according to claim 1, characterized in that: The calculating of the wind speed error according to the effective wind speed and the measured average wind speed specifically includes: Using the formula Calculate the wind speed error; where Δu is the calculated wind speed error, is the effective wind speed, u is the average wind speed; Count all sample wind speed errors and use the Gaussian fitting formula Obtain a statistical wind speed error; wherein a, b, and c are all fitting parameters, and the fitting parameter b obtained by the Gaussian fitting is used as the wind speed error of the anemometer, and the wind speed error of the anemometer is diagnosed to obtain a diagnosis result.

7. A wind speed error diagnosis system for an anemometer of a wind turbine, characterized in that: Wind speed error diagnostics for wind turbine cup anemometers or sonic anemometers, including: A data extraction module is used to obtain historical operation data of the SCADA system of the wind turbine, wherein the historical operation data is instantaneous data; A data cleaning module, used to perform outlier detection and complete data cleaning based on the acquired historical operation data; An effective wind speed calculation module, used to perform power calibration according to the local altitude and temperature, reconstruct a standard power curve according to standard power information, calculate time-averaged information according to the instantaneous data, and calculate the effective wind speed according to the standard power curve and the time-averaged information; The error calculation and diagnosis module is used to calculate the wind speed error according to the effective wind speed and the measured average wind speed, and diagnose the wind speed error of the anemometer to obtain a diagnosis result.

8. The wind speed error diagnosis method of a wind turbine anemometer according to claim 7, characterized in that: The historical operation data at least includes: the operation time record of the wind turbine, the pitch angle of the first blade, the pitch angle of the second blade, the pitch angle of the third blade, the impeller speed, the real-time wind speed, the ambient temperature and the active power; The historical operating data also includes at least one of atmospheric pressure and air density.

9. The wind speed error diagnosis method of a wind turbine anemometer according to claim 8, characterized in that: The effective wind speed calculation module further includes: The calibration data submodule is used to calibrate the active power of the historical operation data according to the local altitude and temperature of the wind turbine: A time-average information submodule, used to obtain time-average information based on the instantaneous data after the data cleaning, wherein the time-average information includes average wind speed, wind speed standard deviation and average active power; The standard power curve reconstruction submodule is used to reconstruct the standard power curve of the wind turbine according to the standard power information: The effective wind speed calculation submodule is used to calculate the effective wind speed using the dichotomy method according to the standard power curve and the time-averaged information.

10. A computer-readable storage medium, characterized in that: The storage medium stores instructions, which, when executed, execute a method for diagnosing wind speed errors of a wind turbine anemometer according to any one of claims 1 to 6.

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