Wind speed error diagnosis methods, systems and media for wind turbine anemometers

Abstract

This invention relates to a method, system, and medium for diagnosing wind speed errors in a wind turbine anemometer. The method includes: acquiring historical operating data from the wind turbine's SCADA system (instantaneous data); performing outlier detection and data cleaning based on the acquired historical operating data; performing power calibration based on local altitude and temperature; calculating the effective wind speed based on a standard power curve and time-averaged information; calculating the wind speed error based on the effective wind speed and the measured average wind speed; and diagnosing the wind speed error of the anemometer to obtain a diagnostic result. This invention is based on SCADA data, resulting in rapid calculation, low operating costs, and high versatility.

Description

Technical Field

[0001] This invention relates to the field of wind turbine fault diagnosis technology. In particular, it relates to a method, system, and medium for diagnosing wind speed errors in wind turbine anemometers. Background Technology

[0002] Operation and maintenance (O&M) of wind turbines is a key variable for enhancing the competitiveness of the wind energy industry. As a core indicator for actual power estimation and expected power assessment, the deviation of wind speed measured by anemometers not only has a significant impact on the evaluation of wind turbine performance, but can also seriously affect the estimation of annual energy production (AEP) of wind farms.

[0003] Standard wind speed measurements are performed using a cup anemometer or an ultrasonic anemometer located behind the rotor and above the nacelle. The free-flow wind speed is estimated retrospectively using the nacelle transfer function. The cup anemometer is a momentum transfer-based wind speed measurement method. Wind speed drives a rotating shaft, and the rotation of magnets on the shaft generates a change in the magnetic field. A magnetic sensor detects this change and converts it into an electrical signal, which is then processed to obtain the wind speed. The ultrasonic anemometer measures wind speed based on the characteristic that the speed of ultrasonic waves in air is affected by wind speed. It calculates wind speed and direction by measuring the propagation time of ultrasonic waves in different directions.

[0004] Anemometers are constantly exposed to the atmospheric environment, affected by blade turbulence, nacelle boundary layer, and harsh conditions such as rainfall and sandstorms. Combined with the aging of the instruments themselves, it's not uncommon for anemometers to fail to measure wind speed accurately. Therefore, anemometer diagnostics are crucial. Considering economic costs, these environmental influences are generally impossible to fully measure in a laboratory setting. Thus, a method for diagnosing anemometer errors is urgently needed to improve the power performance of wind turbines. Summary of the Invention

[0005] This invention provides a method, system, and medium for diagnosing wind speed errors in wind turbine anemometers, in order to solve the problem that in the prior art, anemometers cannot accurately measure wind speed when they are exposed to the atmospheric environment for a long time.

[0006] To achieve the above objectives, in a first aspect, the present invention relates to a method for diagnosing wind speed errors in a wind turbine anemometer, used for diagnosing wind speed errors in a cup-type anemometer or acoustic anemometer of a wind turbine, comprising:

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

[0008] Anomaly detection and data cleaning are performed based on the acquired historical operational data.

[0009] Power calibration is performed based on local altitude and temperature. A standard power curve is reconstructed based on standard power information. Time-averaged information is calculated based on the instantaneous data. Effective wind speed is calculated based on the standard power curve and the time-averaged information.

[0010] The wind speed error is calculated based on the effective wind speed and the measured average wind speed, and the wind speed error of the anemometer is diagnosed to obtain the diagnostic result.

[0011] To achieve the above objectives, in a second aspect, the present invention relates to a wind speed error diagnosis system for a wind turbine anemometer, used for diagnosing wind speed errors in a cup-type anemometer or acoustic anemometer of a wind turbine, comprising:

[0012] The data extraction module is used to acquire historical operating data of the SCADA system of the wind turbine, wherein the historical operating data is instantaneous data;

[0013] The data cleaning module is used to detect outliers and clean the data based on the acquired historical operating data.

[0014] The effective wind speed calculation module is used to perform power calibration based on local altitude and temperature, reconstruct the standard power curve based on standard power information, calculate the time-averaged information based on the instantaneous data, and calculate the effective wind speed based on the standard power curve and the time-averaged information.

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

[0016] To achieve the above objectives, in a third aspect, the present invention also relates to a computer-readable storage medium storing instructions that, when executed, perform the above-described method for diagnosing wind speed errors in a wind turbine anemometer.

[0017] The present invention relates to a method, system, and medium for diagnosing wind speed errors in a wind turbine anemometer, which has the following advantages compared to the prior art:

[0018] This invention is based on SCADA data, requires no additional equipment costs, is easy to use, quick to calculate, has low operating costs, and is highly universal. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the wind turbine structure and the installation position of the anemometer, which is part of the wind speed error diagnosis method for a wind turbine anemometer in Embodiment 1.

[0020] Figure 2 A schematic diagram of a cup-type anemometer and an acoustic anemometer used in a wind turbine anemometer error diagnosis method in Example 1.

[0021] Figure 3 The wind speed error diagnosis method of a wind turbine anemometer in Example 1 is illustrated by a schematic diagram of different regions of the wind turbine power curve.

[0022] Figure 4 The wind speed error diagnosis method of a wind turbine anemometer in Example 1 is shown in the schematic diagram of the power curve of the wind turbine affected by wind speed error.

[0023] Figure 5 A flowchart illustrating a wind speed error diagnosis method for a wind turbine anemometer in Example 1.

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

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

[0026] Figure 8 Schematic diagram of a wind speed error diagnosis system for a wind turbine anemometer in Example 2 Figure 1 ;

[0027] Figure 9 This is a schematic diagram of the wind speed error diagnosis system for a wind turbine anemometer according to Embodiment 2 of the present invention. Figure 2 ;

[0028] Figure 10 The power curve fitted after calibration of the wind speed measurement error data of the anemometer in Example 1 and the standard power curve. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention and not the entire structure.

[0030] Example 1

[0031] A method for diagnosing wind speed errors in wind turbine anemometers; please refer to [link / reference]. Figure 1 After the free flow passes through the wind turbine hub 1 and blades 2, the wind speed is measured by the anemometer 4 located above the nacelle 5. The measurement results are uploaded to the SCADA system and saved.

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

[0033] Anemometers are constantly exposed to the atmosphere, subjected to harsh environmental factors such as blade turbulence, nacelle boundary layer, rainfall, and sandstorms. Inaccuracies in wind speed measurements are not uncommon. (See also...) Figure 4 The standard power curve 11 for wind turbines consists of three parts, see [link to relevant documentation]. Figure 5 In Zone 1, the wind speed is lower than the cut-in wind speed, and the wind turbine does not operate. When the wind speed increases and exceeds the cut-in wind speed, the wind turbine starts operating, and the power curve is in Zone 2. When the wind speed exceeds the rated wind speed, it enters Zone 3, and the wind turbine begins pitch control, operating at rated power. When the anemometer underestimates the wind speed and is affected by turbulence, the power curve 12 is to the left of the standard power curve 11; while when the anemometer overestimates the wind speed and is affected by turbulence, the power curve 13 is to the right of the standard power curve 11.

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

[0035] S101 acquires historical operating data from the SCADA system of the wind turbine generator. The historical operating data is instantaneous data.

[0036] Specifically, historical operating data should include at least: wind turbine operating time records, pitch angle of the first blade, pitch angle of the second blade, pitch angle of the third blade, rotor speed, real-time wind speed, ambient temperature, and active power.

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

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

[0039] Specifically, S102 includes S121 to S122:

[0040] S121 uses the clustering-based DBSCAN method to detect outliers in historical running data;

[0041] S122 cleans historical operating data based on the results of outlier detection.

[0042] S103 performs power calibration based on local altitude and temperature, reconstructs a standard power curve based on standard power information, calculates hourly average information based on instantaneous data, and calculates the effective wind speed based on the standard power curve and hourly average information. Please refer to [link / reference]. Figure 7 It shows a flowchart of the calculation of effective wind speed based on wind turbine operating data and power curves, which incorporates environmental influences.

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

[0044] S131 calibrates the active power based on the local altitude and temperature of the wind turbine, using historical operating data.

[0045] This embodiment incorporates environmental influences (gas state and turbulence intensity) and calculates the effective wind speed based on wind turbine operating data and power curves, specifically including:

[0046] Obtain the local elevation h of the wind turbine local and the hub height h of the wind turbine hub The calculated elevation 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 wheel hub, and T represents the local air temperature.

[0048] Using ambient air density ρ and formula Complete the calibration of active power, where P c To correct the power, P a It 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] To obtain standard power curve information, the wind turbine power curve parameters should include at least: cut-in wind speed, power corresponding to cut-in wind speed, rated wind speed, rated power corresponding to rated wind speed, cut-out wind speed, an intermediate wind speed between cut-in wind speed and rated wind speed, and power corresponding to the intermediate wind speed.

[0051] The power curve is reconstructed by fitting and refining a four-segment quadratic polynomial, as shown in the following expression:

[0052]

[0053] Where u represents wind speed, u in To cut off the wind speed, u med The intermediate wind speed between the two-segment and the three-segment, u rate For the rated wind speed, u out To cut off the wind speed; P(u) is the power, P1(u) is the power corresponding to the second segment, P2(u) is the power corresponding to the third segment, P rate The rated power is denoted as a1, a2, and a3; these are the fitting parameters for the second segment, and b1, b2, and b3 are the fitting parameters for the third segment.

[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 as follows:

[0057]

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

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

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

[0061] Using formula Calculate the standard deviation of wind speed over 10 minutes; where σ is the standard deviation of wind speed, u is the average wind speed, and u is the mean wind speed. i Where N is the instantaneous wind speed, and N is the number of wind speed samples;

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

[0063] The formula P = X1 + X2 + X3 + X4 + X5 gives an expression for the effective wind speed. Based on this expression, the effective wind speed is calculated using the bisection method, where P is the average power, and the specific expressions for X1, X2, X3, X4, and X5 are:

[0064]

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

[0066]

[0067] Among them, u in For the cutoff wind speed, u1 is the segmented wind speed in two segments or three segments, u rate For the rated wind speed, u out To cut off the wind speed.

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

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

[0070] The wind speed error of all samples was statistically analyzed and fitted using the Gaussian formula. The statistical wind speed error is obtained; where a, b, and c are fitting parameters. The wind speed error distribution over multiple time periods approximates a Gaussian distribution. This model uses the mean parameter b obtained from Gaussian fitting as the wind speed error of the anemometer. The wind speed error of the anemometer is diagnosed to obtain the diagnostic results. Based on the diagnostic results obtained from the error diagnosis, the anemometer's wind measurement error can be calibrated. Figure 10 The power curve fitted from calibrated data (under low turbulence intensity and with calibrated density) was compared with the standard power curve. It can be seen that the difference in the power curves of the wind turbines is significantly reduced, and the local differences are due to environmental factors. Furthermore, under near-standard conditions, the difference between the fitted curve and the standard power curve is very small. Therefore, this method demonstrates good detection and calibration effects for anemometer measurement errors.

[0071] Example 2

[0072] A wind speed error diagnosis system for wind turbine anemometers is provided for diagnosing wind speed errors in cup-type or acoustic anemometers on wind turbines. It is implemented using hardware on an electronic device with a central processing unit, such as a personal computer, smart terminal, local area network, or server. For implementation details in this example, please refer to [link to relevant documentation]. Figure 8It includes 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 acquire historical operating data of the SCADA system of the wind turbine. The historical operating data is instantaneous data.

[0074] The historical operating data includes at least: wind turbine operating time records, pitch angle of the first blade, pitch angle of the second blade, pitch angle of the third blade, rotor speed, real-time wind speed, ambient temperature, and active power.

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

[0076] The data cleaning module 62 is used to detect outliers and clean the data based on the acquired historical operating data.

[0077] The effective wind speed calculation module 63 is used to perform power calibration based on local altitude and temperature, reconstruct the standard power curve based on standard power information, calculate the time-averaged information based on instantaneous data, and calculate the effective wind speed based on 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 based on the effective wind speed and the measured average wind speed, and to diagnose the wind speed error of the anemometer to obtain the diagnosis result.

[0079] Specifically, the calculation of wind speed error includes:

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

[0081] The wind speed error of all samples was statistically analyzed and fitted using the Gaussian formula. The statistical wind speed error is obtained; where a, b, and c are fitting parameters. The wind speed error distribution over multiple time periods approximates a Gaussian distribution. This model uses the mean parameter b obtained from Gaussian fitting as the wind speed error of the anemometer. The wind speed error of the anemometer is then diagnosed to obtain the 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 historical operating data based on the local altitude and temperature of the wind turbine.

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

[0085] Standard power curve reconstruction submodule 633 is used to reconstruct the standard power curve of a wind turbine based on standard power information.

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

[0087] The wind speed error diagnosis system for a wind turbine anemometer in this embodiment is the same as the wind speed error diagnosis method for a wind turbine anemometer described in Embodiment 1 in terms of implementation process, method and effect, and will not be repeated here.

[0088] Example 3

[0089] This invention relates to a computer-readable storage medium storing instructions that, when executed, perform a wind speed error diagnosis method for a wind turbine anemometer according to Embodiment 1. The execution process and effects are the same as those described in Embodiment 1, and will not be repeated here.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0091] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for diagnosing wind speed errors in a wind turbine anemometer, characterized in that, Wind speed error diagnosis for cup anemometers or acoustic anemometers used in wind turbines includes: Acquire historical operating data of the SCADA system of the wind turbine, wherein the historical operating data is instantaneous data; Anomaly detection and data cleaning are performed based on the acquired historical operational data. The power calibration is performed based on the local altitude and temperature to obtain calibration data. The standard power curve is reconstructed based on the standard power information. The time-averaged information is calculated based on the instantaneous data and calibration data. The effective wind speed is calculated based on the standard power curve and the time-averaged information. The wind speed error is calculated based on the effective wind speed and the measured average wind speed, and the wind speed error of the anemometer is diagnosed to obtain the diagnostic result.

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

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

4. The wind speed error diagnosis method for a wind turbine anemometer according to claim 1, characterized in that, The step of detecting outliers and cleaning data based on the acquired historical operating data specifically includes: Outlier detection is performed on the historical running data using the clustering-based DBSCAN method. Based on the results of the outlier detection, the historical operational data is cleaned.

5. A method for diagnosing wind speed errors in a wind turbine anemometer according to claim 2 or 3, characterized in that, The process of power calibration based on local altitude and temperature, reconstruction of a standard power curve based on standard power information, calculation of time-averaged information based on instantaneous data, and calculation of effective wind speed based on the standard power curve and the time-averaged information specifically includes: Step 1: Calibrate the active power of the historical operating data based on the local altitude and temperature of the wind turbine: Obtain the local altitude of the wind turbine and the hub height of the wind turbine The calculated altitude of the wheel hub is: ; Using formula Calculate the ambient air density at different temperatures and altitudes; where, It is ambient air density. The elevation of the wheel hub. Represents the local temperature; Using the ambient air density and formula Complete the calibration of active power, including To correct the power, It is the measured active power. Represents standard air density; Step 2: Reconstruct the standard power curve of the wind turbine based on the standard power information: To obtain standard power curve information, the wind turbine power curve parameters should include at least: cut-in wind speed, power corresponding to cut-in wind speed, rated wind speed, rated power corresponding to rated wind speed, cut-out wind speed, an intermediate wind speed between cut-in wind speed and rated wind speed, and power corresponding to the intermediate wind speed. The power curve is reconstructed by fitting and refining a four-segment quadratic polynomial, as shown in the following expression: ； in, Represents wind speed. To cut into wind speed, This represents the intermediate wind speed between the two-segment and the three-segment. Rated wind speed, To cut off the wind speed; For power, For the power corresponding to the second segment, The power corresponding to the three segments, Rated power; , , These are the fitting parameters for the second segment. , , The fitting parameters for the third segment are: Pi = Pid ... Fitting parameters , , The following relationships exist: ； Fitting parameters , , The relationship is: ； Step 3: Calculate the time-averaged information based on the instantaneous data and calibration data: Using formula Calculate the 10-minute average wind speed; where, The average wind speed, The instantaneous wind speed over a 10-minute period. 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 formula Calculate the 10-minute average power; where, The power to be calibrated within 10 minutes. The number of instantaneous power samples within 10 minutes, wherein the instantaneous power is the active power in the instantaneous data; Using formula Calculate the standard deviation of wind speed over 10 minutes; where, For wind speed standard deviation, For instantaneous wind speed, This represents the number of wind speed samples. Step 4: Calculate the effective wind speed using the bisection method based on the standard power curve and the time-averaged information. According to the formula An expression for the effective wind speed is obtained. Based on this expression, the effective wind speed is calculated using the bisection method, where... Average power, The specific expression is: ； in, , , , , , For fitting parameters, For integration variables, The Gaussian error function is... For different segments, the upper and lower limits of integration are... The effective wind speed needs to be estimated.

6. The wind speed error diagnosis method for a wind turbine anemometer according to claim 1, characterized in that, The calculation of wind speed error based on the effective wind speed and the measured average wind speed specifically includes: Using formula Calculate the wind speed error; where, To account for the calculated wind speed error, For effective wind speed, Average wind speed; The wind speed error of all samples was statistically analyzed and fitted using the Gaussian formula. Obtain the statistical wind speed error; where, All are fitting parameters. The fitting parameter b obtained by Gaussian fitting is used as the wind speed error of the anemometer. The wind speed error of the anemometer is diagnosed to obtain the diagnosis result.

7. A wind speed error diagnosis system for a wind turbine anemometer, characterized in that, Wind speed error diagnosis for cup anemometers or acoustic anemometers used in wind turbines includes: The data extraction module is used to acquire historical operating data of the SCADA system of the wind turbine, wherein the historical operating data is instantaneous data; The data cleaning module is used to detect outliers and clean the data based on the acquired historical operating data. The effective wind speed calculation module is used to obtain calibration data by performing power calibration based on local altitude and temperature, reconstruct the standard power curve based on standard power information, calculate the time-averaged information based on the instantaneous data and calibration data, and calculate the effective wind speed based on the standard power curve and the time-averaged information. The error calculation and diagnosis module is used to calculate the wind speed error based on the effective wind speed and the measured average wind speed, and to diagnose the wind speed error of the anemometer to obtain a diagnosis result.

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

9. The wind speed error diagnosis system for a wind turbine anemometer according to claim 8, characterized in that, The effective wind speed calculation module also includes: The calibration data submodule is used to calibrate the active power of the historical operating data based on the local altitude and temperature of the wind turbine to obtain calibration data. The time-average information submodule is used to obtain time-average information based on the instantaneous data and calibration data after data cleaning. 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 a wind turbine based on standard power information. The effective wind speed calculation submodule is used to calculate the effective wind speed using the binary search method based on the standard power curve and the time-averaged information.

10. A computer-readable storage medium, characterized in that: The storage medium stores instructions that, when executed, perform a wind speed error diagnosis method for a wind turbine anemometer as described in any one of claims 1-6.

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