Method for calibrating systematic bias of wind direction data based on wake effect and related device

Abstract

The application belongs to a wind direction calibration method, aiming at the problem that when calibrating wind direction data, the readout deviation between the wind direction indicator and the standard instrument is calibrated by using the artificial calibration method, and the recorded data with wind direction deviation has been unable to be corrected, a method for calibrating systematic deviation of wind direction data based on wake effect and related device are provided, a plurality of wind turbine generators with the greatest influence on the wake effect of each wind turbine generator are screened out, a wind farm engineering wake model is established to calculate the theoretical maximum wake loss wind direction of the wind turbine generator, the SCADA data record is analyzed, the actual maximum wake loss wind direction of the specific wind turbine generator is calculated, then the wind direction calibration value is calculated according to the theoretical maximum wake loss wind direction and the actual maximum wake loss wind direction, and the SCADA wind direction data in the SCADA data record is calibrated. The wind direction data calibration of the wind turbine generator can be completed only by relying on the data recorded by the SCADA system of the wind turbine generator without synchronous calibration data.

Description

Technical Field

[0001] This application pertains to a wind direction calibration method, specifically a method and related apparatus for calibrating systematic deviations in wind direction data based on the wake effect. Background Technology

[0002] The sources of deviation in the wind turbine roof vane include systematic deviation, random deviation, nonlinear response, temperature-related deviation, hysteresis during wind direction changes, vane malfunction, and calibration drift. Among these, systematic deviation has a continuous impact on wind direction measurement, and therefore has a significant impact on long-term data collection and wind energy utilization efficiency.

[0003] Systematic bias manifests as a wind vane reading consistently deviating from the actual wind direction by a fixed angle or direction. This can be caused by incorrect equipment installation, sensor positioning errors, or manufacturing defects. Typically, detecting systematic bias in wind direction requires comparing the equipment reading with known, accurate wind direction data, or using specialized calibration equipment and methods. However, wind measurement towers specifically designed for wind resource measurement and assessment are often dismantled after wind farm construction due to safety concerns and additional maintenance costs. Furthermore, wind measurement equipment such as lidar and acoustic radar are not optimal choices for calibrating long-term operational data due to their high equipment costs.

[0004] Methods for calibrating wind direction data can be broadly categorized into three types: data correction calibration, reference data comparison calibration, and historical trend analysis calibration. Data correction calibration typically relies on certain experiences and prior assumptions. Reference data comparison calibration is difficult to perform when calibration data is unavailable. Historical trend analysis calibration requires complete historical calibration data; while it can calibrate data over a large time span, it cannot calibrate wind direction data at every specific time step.

[0005] Therefore, in actual wind farm production, the challenge lies in how to perform wind direction calibration solely based on data recorded by the wind turbine's SCADA (Supervisory Control and Data Acquisition) system, without historical or concurrent calibration records. Currently, wind farms actually use periodic manual calibration to ensure the accuracy of measurement results. However, periodic manual calibration cannot correct recorded data that has already shown wind direction deviations. Summary of the Invention

[0006] This application addresses the problem that current methods for calibrating wind direction data, which rely on manual calibration to correct reading deviations between the wind vane and a standard instrument, cannot correct recorded data that already exhibits wind direction deviations. It provides a method and related apparatus for calibrating systematic deviations in wind direction data based on the wake effect.

[0007] To achieve the above objectives, this application adopts the following technical solution:

[0008] Firstly, this application proposes a method for calibrating systematic biases in wind direction data based on wake effects, including:

[0009] S1, acquire wind farm data; the wind farm data includes SCADA data records, performance parameters of each wind turbine, and geographical coordinates;

[0010] S2, calculate the spacing and direction between every two wind turbine units in the wind farm, and select the wind turbine units that have the greatest impact on the wake effect of each wind turbine unit.

[0011] S3, Establish a wake model for wind farm projects;

[0012] S4, combining the data of several wind turbines with the greatest wake effect of each wind turbine and the wind farm data, calculate the theoretical maximum wake loss wind direction of a specific wind turbine using the wind farm engineering wake model; the specific wind turbine is any wind turbine in the wind farm.

[0013] S5. Analyze SCADA data records, combine data from several wind turbines with the greatest wake effect impact on each wind turbine with the data from the wind farm, and calculate the actual maximum wake loss wind direction for a specific wind turbine.

[0014] S6, calculate the average value of multiple wind direction calibration values ​​for a specific wind turbine; the wind direction calibration value is obtained by subtracting the theoretical maximum wake loss wind direction from the actual maximum wake loss wind direction;

[0015] S7 subtracts the average value of multiple wind direction calibrations of a specific wind turbine from the SCADA wind direction data record of the specific wind turbine, thus completing the calibration of the specific wind turbine.

[0016] S8. Select a new wind turbine as the specific wind turbine and return to step S4 until the calibration of all wind turbines in the wind farm is completed.

[0017] Furthermore, in step S7, before completing the calibration of a specific wind turbine, the following steps are also included: calculating the wind direction calibration uncertainty of the specific wind turbine, determining whether the wind direction calibration uncertainty is greater than the uncertainty threshold, if so, returning to step S2, and filtering out more wind turbines that have the greatest impact on the wake effect of each wind turbine; otherwise, completing the calibration of the specific wind turbine.

[0018] Furthermore, in step S1, the SCADA data record includes the measured average wind speed, average wind direction, average power, nacelle position, and timestamp for each wind turbine within a preset time period; and the time range of the SCADA data record is greater than or equal to one year.

[0019] The performance parameters of each wind turbine include rated wind speed, rated power, rotor diameter, hub height, power and thrust curves;

[0020] The geographic coordinates include the latitude and longitude information of each wind turbine and the specific geographic coordinate system.

[0021] Further, step S2 includes:

[0022] S2.1, convert the geographic coordinates of each wind turbine into projected coordinates in a projected coordinate system that meets the directional accuracy requirements, and obtain the projected coordinates of the wind turbine.

[0023] S2.2, calculate the relative distance and relative orientation angle between every two wind turbines in the wind farm;

[0024] S2.3, select the N wind turbine units closest to T0 and form a set, denoted as {T1, T2, ..., T0}. N}, which is the unit most affected by the wake effect; where T0 represents a specific wind turbine unit.

[0025] Furthermore, in step S3, the wake model of the wind farm project includes a velocity loss model, a wake superposition model, and a turbulence model;

[0026] The velocity loss model adopts the Gaussian velocity loss model, the wake superposition model adopts the free flow sum of squares superposition model, and the turbulence model adopts the Crespo-Hernandez model.

[0027] Further, step S4 includes:

[0028] S4.1 Input the projected coordinates, performance parameters and inflow wind direction of a specific wind turbine into the wake model of the wind farm project, and calculate the power generation of T0 under the influence of wake effect under different inflow wind directions;

[0029] S4.2, when T0 is located at the unit pair {(T1,T0),(T2,T0),…,(TN Downstream of T0, calculate the wind direction within a preset range corresponding to the incoming wind direction that minimizes the power generation of T0, denoted as α1, α2, ..., α N This set of wind directions is taken as the theoretical maximum wake loss wind direction.

[0030] Further, step S5 includes:

[0031] S5.1 Filter the SCADA data records of T0 records that have a power greater than zero, and the records where the absolute value of the difference between the incoming wind direction and the nacelle direction is less than 15°.

[0032] S5.2, for {(T1,T0),(T2,T0),…,(T N For each turbine pair in T0, calculate the power ratio of T0 to each wind turbine when T0 is downstream;

[0033] S5.3 Divide the wind direction into 1° intervals and calculate the ratio of T0 to the power of the upstream unit under different wind directions;

[0034] S5.4, find the minimum value of the power ratio within the preset range of the theoretical maximum wake loss wind direction, and use it as the actual maximum wake loss wind direction, denoted as β1, β2, ..., β N .

[0035] Furthermore, the wind direction calibration uncertainty is calculated using the following formula:

[0036]

[0037] Where s is the sample standard deviation;

[0038]

[0039] Among them, c i Let be the wind direction calibration value for the i-th time, and c be the average value of multiple wind direction calibration values.

[0040] Secondly, this application proposes a system for calibrating systematic biases in wind direction data based on wake effects, comprising:

[0041] The acquisition module is used to acquire wind farm data; the wind farm data includes SCADA data records, performance parameters of each wind turbine, and geographical coordinates.

[0042] The filtering module is used to calculate the spacing and direction between every two wind turbines in the wind farm and filter out the wind turbines that have the greatest impact on the wake effect of each wind turbine.

[0043] The model module is used to build wake models for wind farm projects;

[0044] The theoretical calculation module is used to combine the data of several wind turbines with the greatest wake effect of each wind turbine with the wind farm data, and to calculate the theoretical maximum wake loss wind direction of a specific wind turbine using the wind farm engineering wake model; the specific wind turbine is any wind turbine in the wind farm.

[0045] The actual calculation module is used to analyze SCADA data records, combine the data of several wind turbines with the greatest wake effect of each wind turbine and the wind farm data, and calculate the actual maximum wake loss wind direction of a specific wind turbine.

[0046] The mean value module is used to calculate the average value of multiple calibrations of a specific wind turbine; the average value of the calibration is obtained by subtracting the theoretical maximum wake loss wind direction from the actual maximum wake loss wind direction.

[0047] The calibration module is used to subtract the average value of multiple calibrations of a specific wind turbine from the SCADA wind direction data in the SCADA data record of that specific wind turbine, thereby completing the calibration of that specific wind turbine.

[0048] Thirdly, this application proposes an electronic device, comprising:

[0049] Memory, used to store computer programs;

[0050] A processor is used to implement the steps of the above-described method for calibrating systematic deviations in wind direction data based on wake effect when executing the computer program.

[0051] Compared with the prior art, this application has the following beneficial effects:

[0052] This application proposes a method for calibrating systematic deviations in wind direction data based on wake effects. It identifies several wind turbines that have the greatest impact on the wake effect of each individual turbine, establishes a wake model for the wind farm project to calculate the theoretical maximum wake loss wind direction for each turbine, analyzes SCADA data records to calculate the actual maximum wake loss wind direction for a specific turbine, and then calculates the wind direction calibration value based on both the theoretical and actual maximum wake loss wind directions. This calibration is then performed in conjunction with the SCADA wind direction data from the SCADA data records. Using this calibration method, wind direction data calibration for wind turbines can be completed solely based on data recorded by the SCADA system of the wind turbines, even without concurrent calibration data. This method is expected to significantly improve the accuracy of SCADA data, reduce calibration costs, and enhance competitiveness.

[0053] This application also proposes a system for calibrating systematic biases in wind direction data based on wake effect, an electronic device, and a computer storage medium, which possess all the advantages of the aforementioned methods for calibrating systematic biases in wind direction data based on wake effect. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the first process of the method for calibrating systematic deviations in wind direction data based on the wake effect in this application;

[0056] Figure 2 This is a schematic diagram of the second process of the method for calibrating systematic deviations in wind direction data based on the wake effect in this application;

[0057] Figure 3 This is a schematic diagram of the third process of the method for calibrating systematic deviations in wind direction data based on the wake effect in this application;

[0058] Figure 4 This is a schematic diagram of the layout of the Penmanshiel wind farm in an embodiment of this application;

[0059] Figure 5 This is a schematic diagram showing the power ratio of wind turbine T01 to wind turbine T02 under theoretical simulation in the embodiments of this application;

[0060] Figure 6 This is a schematic diagram showing the power ratio of wind turbine T01 to wind turbine T04 under theoretical simulation in the embodiments of this application;

[0061] Figure 7 This is a schematic diagram showing the power ratio of wind turbine T01 to wind turbine T05 under theoretical simulation in the embodiments of this application;

[0062] Figure 8 This is a schematic diagram showing the power ratio of wind turbine T01 to wind turbine T02 in the actual SCADA data of this application embodiment;

[0063] Figure 9 This is a schematic diagram showing the power ratio of wind turbine T01 to wind turbine T04 in the actual SCADA data of this application embodiment;

[0064] Figure 10 This is a schematic diagram showing the power ratio of wind turbine T01 to wind turbine T05 in the actual SCADA data of this application embodiment;

[0065] Figure 11 This is a schematic diagram of the system for calibrating systematic deviations in wind direction data based on the wake effect, as described in this application. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0067] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0069] In the description of the embodiments of this application, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0070] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0071] In the description of the embodiments of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0072] Accurate wind direction data is crucial for the operational efficiency and safety of wind turbines. Errors in wind direction data can lead to blade misalignment, reducing wind energy capture efficiency and potentially causing equipment malfunctions. Therefore, regularly calibrating wind direction data is an important part of wind farm operation and maintenance.

[0073] Typically, detecting systematic deviations in wind direction requires comparing readings with known, accurate wind direction data, or using specialized calibration equipment and methods. However, wind measurement towers specifically designed for wind resource measurement and assessment are often dismantled after wind farm construction due to safety concerns and additional maintenance costs. Furthermore, wind measurement equipment such as lidar and acoustic radar are not the optimal choice for calibrating long-term operational data due to their high equipment costs.

[0074] Ensuring accurate anemometer readings is crucial for the efficient, stable, and safe operation of wind turbines. First, the wind turbine control system needs to adjust the yaw angle based on real-time wind direction data to maximize wind energy capture. Inaccurate wind direction data can lead to improper yaw angle adjustments, affecting power generation efficiency. Second, accurate wind direction data is essential for the safe operation of wind turbines under strong winds or extreme weather conditions. Inaccurate anemometer readings can cause the wind turbine to malfunction, increasing the risk of failure. Finally, accurate wind direction data is vital for subsequent wind turbine performance analysis and wind resource post-assessment. Calibration processes ensure the reliability of recorded data, providing an accurate foundation for wind turbine performance analysis and wind farm resource assessment.

[0075] Existing methods for calibrating wind direction data can be broadly categorized into three types: data correction calibration, reference data comparison calibration, and historical trend analysis calibration. Data correction calibration involves analyzing recorded wind direction data to identify systematic biases or errors, then applying mathematical models or algorithms to correct the data. For example, interpolation methods can be used to estimate data loss or inaccuracies in certain time periods based on data from surrounding time points. This method typically relies on certain experience and prior assumptions. Reference data comparison calibration is a commonly used data correction method in the wind power industry. It uses accurate wind direction data recorded during the same period (e.g., data from nearby weather stations or other calibrated anemometers) as a reference to compare and correct the wind direction data to be calibrated. This method requires obtaining calibration data from the same period, and the real challenge often lies in completing calibration when such data is difficult to obtain. Historical trend analysis calibration determines the trend of error development by analyzing historical calibration records and performance data, and adjusts historical wind direction data accordingly. This method can identify the cumulative error process of anemometer data and discover deviation trends in the data. The limitations of this method are similar to those of the reference data comparison calibration method: it requires complete historical calibration data to complete the calibration. In addition, this method can calibrate data over a large time span, but it cannot calibrate wind direction data at each time step.

[0076] Currently, there are methods that use meteorological satellite data to calibrate wind farm data, but these methods are costly and the data resolution is insufficient for wind farm requirements. Therefore, in actual wind farm production, the challenge lies in how to calibrate wind direction solely based on data recorded by the wind turbine's SCADA system, without historical or concurrent calibration records. In this situation, the methods mentioned above all have limitations to varying degrees.

[0077] In wind farms, the actual method used is periodic manual calibration to ensure the accuracy of measurement results. This calibration process typically includes: 1. Shutting down the turbine and checking the physical condition of the wind vane; 2. Comparing the readings of the wind vane on the wind turbine roof with those of a standard wind vane (the standard wind vane is usually an instrument with known measurement accuracy calibrated by an authoritative institution); 3. Adjusting the mechanical and electronic parameters of the wind vane based on the reading deviation; 4. Ensuring that the reading deviation between the adjusted wind vane and the standard instrument is within an acceptable range; 5. After calibration, continuously monitoring the wind vane readings to ensure its continuous and stable operation. However, the currently used periodic manual calibration method cannot correct recorded data that has already shown wind direction deviations.

[0078] Based on the above, this application proposes a method for calibrating systematic deviations in wind direction data based on wake effect. The following is a detailed description of this application in conjunction with embodiments and accompanying drawings.

[0079] like Figure 1 The image shown is a first embodiment of a method for calibrating systematic biases in wind direction data based on wake effects, which may include:

[0080] S101, acquire wind farm data.

[0081] The wind farm data includes SCADA data records, performance parameters of each wind turbine, and geographical coordinates. SCADA systems are data acquisition and monitoring systems widely used in industrial control. In wind farms, SCADA systems play a crucial role, collecting, recording, and analyzing real-time operational data from wind turbines, weather stations, substations, and other equipment, providing strong support for wind farm operation and management. They offer comprehensive and accurate information support to the wind farm's operation and maintenance team, helping to improve the wind farm's operational efficiency and safety.

[0082] The basic performance parameters of wind turbines should include rated wind speed, rated power, rotor diameter, hub height, power and thrust curves, etc. The geographical coordinates of each wind turbine should include its latitude and longitude information, as well as the specific geographical coordinate system. The SCADA data records for each wind turbine should include ten minutes of measured average wind speed, average wind direction, average power, nacelle location, and timestamps. The time range for SCADA data recording should be no less than one year.

[0083] Average wind speed refers to the average wind speed over a specific time period, and wind speed directly affects the power generation of wind turbines. Average wind direction is the average wind direction over a specific time period. Understanding wind direction helps assess the wind turbine's response to changes in wind direction and the potential impact of wind direction on turbine performance. Average power is the average electrical output of the wind turbine over a specific time period, reflecting the turbine's power generation efficiency during that period. Nacelle position typically refers to the yaw angle of the wind turbine nacelle, i.e., the nacelle's position relative to the wind direction. Recording the nacelle position helps analyze the wind turbine's ability to track wind direction. A timestamp is the specific time when data was recorded, generally including the date, hour, minute, and second.

[0084] In practical applications, the specific content of wind farm data can be adjusted according to the specific calculation methods in subsequent steps, and this application does not impose specific restrictions.

[0085] S102, calculate the spacing and direction between every two wind turbine units in the wind farm, and select the wind turbine units that have the greatest impact on the wake effect of each wind turbine unit.

[0086] It should be noted that the wake effect of a wind turbine refers to the region behind the turbine where the wind speed is reduced and turbulence is enhanced due to the obstruction and disturbance of airflow by the turbine blades during operation. This wake effect can affect downstream wind turbines, reducing their power generation efficiency and potentially increasing the fatigue load on the turbines.

[0087] In practical applications, the degree of wake effect for each wind turbine depends on a variety of factors, including the distance between wind turbines, wind direction, wind speed, turbine size, and power. Therefore, to determine the wind turbines that have the greatest impact on the wake effect of each individual wind turbine, it is usually necessary to comprehensively consider these factors.

[0088] S103, Establish a wake model for wind farm projects.

[0089] In practical applications, engineering wake models are mainly used to describe and predict the characteristics of the wake field of wind turbine generators. The specific algorithm for the engineering wake model in this application can be selected according to computational needs.

[0090] S104, combining the data of several wind turbines with the greatest wake effect of each wind turbine and the wind farm data, calculate the theoretical maximum wake loss wind direction of a specific wind turbine using the wind farm engineering wake model; the specific wind turbine is any wind turbine in the wind farm.

[0091] S105, Analyze SCADA data records, combine data from several wind turbines with the greatest wake effect impact on each wind turbine and the wind farm data, and calculate the actual maximum wake loss wind direction for a specific wind turbine.

[0092] S106, calculate the average value of multiple wind direction calibration values ​​for a specific wind turbine.

[0093] The wind direction calibration value for a single instance is obtained by subtracting the theoretical maximum wake loss wind direction from the actual maximum wake loss wind direction. The average value of all single wind direction calibration values ​​is then calculated to obtain the average value of multiple wind direction calibration values.

[0094] S107, subtracts the average of multiple wind direction calibration values ​​of a specific wind turbine from the SCADA wind direction data recorded in the SCADA data record of the specific wind turbine, and completes the calibration of the specific wind turbine.

[0095] S108, select a new wind turbine as the specific wind turbine, return to step S104, and continue until the calibration of all wind turbines in the wind farm is completed.

[0096] By using steps S104 to S107 above, all wind turbine units are calibrated to complete the calibration of all wind turbine units.

[0097] Detecting systematic deviations in wind direction typically requires comparing readings with known, accurate wind direction data, or using specialized calibration equipment and methods. However, in the absence of concurrent calibration data, special methods are often needed to calibrate wind direction measurements. To construct a specific reference wind direction for calibrating wind direction measurements, this application utilizes the principle of reduced power generation efficiency caused by the wake effect.

[0098] This application proposes a method for calibrating systematic deviations in wind direction data based on the wake effect. The wake effect refers to the swirling flow and slowed-down region downstream of the wind turbine blades. The flow characteristics in this region include uneven velocity distribution, increased turbulence, and reduced average wind speed. The wake effect causes a decrease in incoming wind speed, reduced output power, and increased fatigue load on downstream wind turbines. To calibrate wind direction data using SCADA data recording, this method utilizes the principle of reduced power generation efficiency caused by the wake effect to construct a special reference wind direction, thereby achieving the calibration purpose. This allows for the calibration of wind turbine wind direction data solely based on data recorded by the wind turbine's SCADA system, even in the absence of concurrent calibration data. It is expected to significantly improve the accuracy of SCADA data, reduce calibration costs, and enhance competitiveness. It also has significant implications for improving the reliability of wind power SCADA data.

[0099] like Figure 2 As shown, as a second embodiment of the method for calibrating systematic biases in wind direction data based on the wake effect of this application, it may include:

[0100] S201, Obtain wind farm data, including the basic performance parameters of each wind turbine in the wind farm, the geographical coordinates of the wind turbine, and the SCADA data records of the wind turbine.

[0101] S202, calculate the spacing and direction between each pair of wind turbines in the wind farm, and select the turbines that have the greatest impact on the wake effect of each wind turbine.

[0102] (1) Select a projection coordinate system that provides sufficient directional accuracy, such as the Universal Transverse Mercator (UTM) projection coordinate system, and transform the geographic coordinates of each wind turbine in the wind farm into the selected projection coordinates. In practical applications, the UTM coordinate system is implemented by dividing the Earth's surface into multiple longitude zones, each spanning 6 degrees of longitude, and using the transverse Mercator projection to project the topography of each zone onto a two-dimensional plane.

[0103] (2) Calculate the relative distance and relative direction angle between each pair of wind turbines in the wind farm.

[0104] (3) Select any specific wind turbine, denoted as T0, and filter the N turbines closest to T0, denoted as {T1, T2, ..., T...} N}, where N is the number of wind turbine units selected, which can be 2-4, as the units with the greatest wake effect.

[0105] S203. Establish a wake model for wind farm projects and calculate the theoretical maximum wake loss wind direction for a specific wind turbine (such as T0).

[0106] (1) Input the wind turbine projection coordinates, wind turbine performance parameters and inflow direction into the engineering wake model, and calculate the power generation of wind turbine T0 under the influence of wake effect under different inflow directions.

[0107] It should be noted that the inflow direction refers to the direction in which wind enters a specific area or flows past an object, and it affects aerodynamic performance, structural stability, and airflow patterns in the environment.

[0108] The selected engineering wake model mainly consists of three parts: a velocity loss model, a wake superposition model, and a turbulence model. Among them, the velocity loss model can be a Gaussian velocity loss model, the wake superposition model can be a free flow sum of squares superposition model, and the turbulence model can be the Crespo-Hernandez model.

[0109] (2) When T0 is located at the unit pair {(T1,T0),(T2,T0),…,(T…)} N Downstream of T0, calculate the wind direction within a small range corresponding to the incoming wind direction that minimizes the power generation of unit T0, denoted as α1, α2, ..., α N This set of wind directions is taken as the theoretical maximum wake loss wind direction.

[0110] S204, analyze SCADA data records to calculate the wind direction with the maximum actual wake loss of the wind turbine.

[0111] (1) Screen the SCADA data records of unit T0 where the power is greater than zero, and screen the SCADA data records of unit T0 where the absolute value of the difference between the incoming airflow direction and the nacelle direction is less than 15°.

[0112] (2) For each unit, {(T1,T0),(T2,T0),…,(T N ,T0)}, calculate the power ratio of wind turbine T0 to each turbine when it is located downstream. When the wind direction is directly opposite the upstream and downstream turbines, the wake effect is the largest and the ratio of the downstream turbine to the upstream turbine is the smallest.

[0113] (3) Divide the wind direction into 1° intervals and calculate the unit T1, T2, ..., T under different wind directions. N The ratio of power to T0.

[0114] (4) Find the pair of wind turbine units {(T1,T0),(T2,T0),…,(T… N The minimum value of the ratio of the power generation of the wind turbines to the theoretical maximum wake loss wind direction is the wind direction at which the actual maximum wake loss wind direction is located. The wind direction corresponding to the minimum value is taken as the actual maximum wake loss wind direction for each wind turbine, and is denoted as β1, β2, ..., β1. N .

[0115] S205, calculate the average value of multiple calibrations and calculate the uncertainty to complete the calibration.

[0116] (1) Calculate the single wind direction calibration value c i :

[0117] c i =β i -α i

[0118] Where, β i α represents the wind direction with the greatest actual wake loss. i The wind direction with the greatest theoretical wake loss.

[0119] (2) The wind direction calibration value of unit T0 is taken as the average value c of each calibration:

[0120]

[0121] (3) Calibrate the uncertainty u according to the wind direction of the statistical uncertainty computer group T0. A :

[0122]

[0123] Where s is the sample standard deviation.

[0124]

[0125] (4) Subtract the average value c from the SCADA wind direction data of unit T0 to complete the calibration. If the calibration uncertainty is too high, increase the N value and repeat steps S202-S205; otherwise, complete the calibration.

[0126] S206, select a new wind turbine and return to step (3) of S202 for calibration until all wind turbines in the wind farm have been calibrated.

[0127] S207. If high-precision wind direction measurement data can be collected, new SCADA data records are continuously collected and analyzed. Then, steps S201-S206 are executed to evaluate the accuracy of the wind direction calibration.

[0128] Consider the special case where the incoming wind direction aligns with the line connecting the two wind turbines. In this situation, the downstream wind turbine is directly facing the wake region of the upstream turbine, thus experiencing the most significant wake effect and having the lowest power generation efficiency. In this case, the wind direction data corresponding to the lowest power generation performance of the downstream turbine in the SCADA system is used as the measured wind direction for this special situation. To reduce measurement errors, the incoming wind direction measurement data corresponding to the lowest power generation efficiency of the downstream turbine should be selected from a period of time, and their average value should be calculated as the wind direction measurement value for this special case. On the other hand, based on the relative position information of the upstream and downstream turbines, the incoming wind direction when the wake effect is greatest can be accurately determined; this wind direction can be used as a reference wind direction and compared with the measured wind direction in the aforementioned special case. By comparing the difference between the measured wind direction in the special case and the reference wind direction, the systematic deviation of the turbine's measured wind direction can be determined. Subtracting this deviation from the SCADA measured wind direction data completes the calibration of the wind direction data.

[0129] In wind farms consisting of multiple wind turbines, due to the superposition effect of the wake region, downstream turbines may be simultaneously affected by multiple upstream turbines, making it complex to directly determine the reference wind direction. In this case, an engineering wake model of the wind farm can be constructed to calculate the power generation efficiency of downstream turbines under different incoming wind directions. The incoming wind direction with the lowest power generation efficiency in the wake influence region can then be selected as the reference wind direction, thereby calibrating the systematic bias of the wind direction data measured by SCADA.

[0130] like Figure 3 As shown, as a third embodiment of the method for calibrating systematic biases in wind direction data based on wake effects in this application, and also as an example, it may include:

[0131] S301, acquire wind farm data.

[0132] The selected data is SCADA data from the Penmanshiel Wind Farm in the UK. This dataset includes static data of the wind farm (including WGS84 coordinates, rated power, rotor diameter, and hub height of the wind turbines), SCADA (Monitoring and Data Acquisition) data and event data for every 10 minutes from 2016 to 2022, PMU instrument data, and grid connection point instrument data. The wind farm comprises 14 Senvion MM82 wind turbines, of which turbine T03 was decommissioned for specific reasons.

[0133] It should be noted that WGS84 coordinates are a geocentric coordinate system with the origin at the Earth's center of mass, and are also a type of conventional Earth reference system.

[0134] S302, calculate the spacing and direction between each pair of wind turbines in the wind farm, and select the turbines that have the greatest impact on the wake effect of each turbine.

[0135] like Figure 4 The diagram shows the layout of the Penmanshiel wind farm. In this embodiment, the pairwise distances and relative orientations between the 14 turbines were calculated, with the distances measured by the rotor diameter D. To reduce computational complexity, N=3 was chosen in this embodiment, meaning that only the three turbines with the greatest influence on each other were considered for calculating their relative distances and orientations. Figure 4 As can be seen, the three units most relevant to unit T01 are numbered T02, T04, and T05.

[0136] S303, establish a wake model for a wind farm project, and calculate the theoretical maximum wake loss wind direction for wind turbine T01.

[0137] The results are as follows Figures 5 to 7 The diagram shown illustrates the power ratio between wind turbine T1 and other wind turbines under theoretical simulation. Figure 5 To theoretically simulate the power ratio of wind turbine T01 to wind turbine T02, Figure 6 To theoretically simulate the power ratio of wind turbine T01 to wind turbine T04, Figure 7 This is a theoretical simulation of the power ratio of wind turbine T01 to wind turbine T05. Calculations show that the theoretical maximum wake loss wind direction between wind turbine T01 and wind turbine T02 is 130.34 degrees, between wind turbine T01 and wind turbine T04 is 30.55 degrees, and between wind turbine T01 and wind turbine T05 is 79.45 degrees.

[0138] S304, Analyze SCADA data to calculate the wind direction with the maximum actual wake loss of the wind turbine.

[0139] like Figures 8 to 10 The diagram shown illustrates the power ratio between wind turbine T1 and other wind turbines obtained from actual SCADA data. Figure 8 This represents the power ratio of wind turbine T01 to wind turbine T02 in the actual SCADA data. Figure 9 This represents the power ratio of wind turbine T01 to wind turbine T04 in the actual SCADA data. Figure 10This represents the power ratio of wind turbine T01 to wind turbine T05 in the actual SCADA data. Calculations show that the difference between the actual maximum wake loss wind direction and the theoretical maximum wake loss wind direction between wind turbine T01 and wind turbine T02 is -2.34 degrees; the difference between the actual maximum wake loss wind direction and the theoretical maximum wake loss wind direction between wind turbine T01 and wind turbine T04 is -1.55 degrees; and the difference between the actual maximum wake loss wind direction and the theoretical maximum wake loss wind direction between wind turbine T01 and wind turbine T05 is -4.55 degrees.

[0140] S305, calculate the average value of multiple calibrations and calculate the uncertainty to complete the calibration.

[0141] The calculation shows that the average calibration value is -2.813° and the uncertainty is 0.898°. The calibration can be completed by subtracting -2.813° from the wind direction data in the SCADA data.

[0142] Since the Penmanshiel wind farm lacks high-precision wind direction calibration data, the step of evaluating the accuracy of the wind direction calibration is omitted.

[0143] Based on the aforementioned method for calibrating systematic biases in wind direction data using the wake effect, this application also proposes a system for calibrating systematic biases in wind direction data using the wake effect. For example... Figure 11 The image shown is an embodiment of a system for calibrating systematic biases in wind direction data based on the wake effect, which may include:

[0144] The acquisition module is used to acquire wind farm data; the wind farm data includes SCADA data records, performance parameters of each wind turbine, and geographical coordinates.

[0145] The filtering module is used to calculate the spacing and direction between every two wind turbines in the wind farm and filter out the wind turbines that have the greatest impact on the wake effect of each wind turbine.

[0146] The model module is used to build wake models for wind farm projects;

[0147] The theoretical calculation module is used to combine the data of several wind turbines with the greatest wake effect of each wind turbine with the wind farm data, and to calculate the theoretical maximum wake loss wind direction of a specific wind turbine using the wind farm engineering wake model; the specific wind turbine is any wind turbine in the wind farm.

[0148] The actual calculation module is used to analyze SCADA data records, combine the data of several wind turbines with the greatest wake effect of each wind turbine and the wind farm data, and calculate the actual maximum wake loss wind direction of a specific wind turbine.

[0149] The mean value module is used to calculate the average value of multiple calibrations of a specific wind turbine; the average value of the calibration is obtained by subtracting the theoretical maximum wake loss wind direction from the actual maximum wake loss wind direction.

[0150] The calibration module is used to subtract the average value of multiple calibrations of a specific wind turbine from the SCADA wind direction data in the SCADA data record of that specific wind turbine, thereby completing the calibration of that specific wind turbine.

[0151] For each module, the aforementioned implementation of the method for calibrating systematic deviations in wind direction data based on wake effect can also be used.

[0152] An electronic device provided in this application includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for calibrating systematic deviations of wind direction data based on wake effect as described in any of the above embodiments.

[0153] Another electronic device provided in this application embodiment may further include: an input port connected to a processor for transmitting multimodal data collected by an external acquisition device to the processor; a display unit connected to the processor for displaying the processor's processing results to the outside world; and a communication module connected to the processor for enabling communication between the electronic device and the outside world. The display unit may be a display panel, a laser scanner, or the like; the communication method used by the communication module includes, but is not limited to, Mobile High Definition Link (HML), Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), wireless connectivity: Wi-Fi, Bluetooth communication, Bluetooth Low Energy communication, and IEEE 802.11s-based communication technology.

[0154] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the method for calibrating systematic deviations of wind direction data based on wake effect as described in any of the above embodiments.

[0155] The computer-readable storage media involved in this application include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage media known in the art.

[0156] For descriptions of relevant parts of the system, electronic equipment, and computer-readable storage medium for calibrating systematic deviations of wind direction data based on wake effect provided in the embodiments of this application, please refer to the detailed description of the corresponding parts in the method for calibrating systematic deviations of wind direction data based on wake effect provided in the embodiments of this application, which will not be repeated here. Furthermore, parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0157] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for calibrating systematic biases in wind direction data based on wake effect, characterized in that, include: S1, acquire wind farm data; the wind farm data includes SCADA data records, performance parameters of each wind turbine, and geographical coordinates; S2, calculate the spacing and direction between every two wind turbine units in the wind farm, and select the wind turbine units that have the greatest impact on the wake effect of each wind turbine unit. S3, Establish a wake model for wind farm projects; S4, combining the data of several wind turbines with the greatest wake effect of each wind turbine and the wind farm data, calculate the theoretical maximum wake loss wind direction of a specific wind turbine using the wind farm engineering wake model; the specific wind turbine is any wind turbine in the wind farm. S5. Analyze SCADA data records, combine data from several wind turbines with the greatest wake effect impact on each wind turbine with the data from the wind farm, and calculate the actual maximum wake loss wind direction for a specific wind turbine. S6, calculate the average value of multiple wind direction calibration values ​​for a specific wind turbine; the wind direction calibration value is obtained by subtracting the theoretical maximum wake loss wind direction from the actual maximum wake loss wind direction; S7 subtracts the average value of multiple wind direction calibrations of a specific wind turbine from the SCADA wind direction data record of the specific wind turbine, thus completing the calibration of the specific wind turbine. S8. Select a new wind turbine as the specific wind turbine and return to step S4 until the calibration of all wind turbines in the wind farm is completed.

2. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 1, characterized in that, In step S7, before completing the calibration of a specific wind turbine, the following steps are also included: calculating the wind direction calibration uncertainty of the specific wind turbine, determining whether the wind direction calibration uncertainty is greater than the uncertainty threshold, if so, returning to step S2, and filtering out more wind turbines that have the greatest impact on the wake effect of each wind turbine; otherwise, completing the calibration of the specific wind turbine.

3. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 1 or 2, characterized in that, In step S1, the SCADA data record includes the measured average wind speed, average wind direction, average power, nacelle position, and timestamp for each wind turbine within a preset time period; and the time range of the SCADA data record is greater than or equal to one year. The performance parameters of each wind turbine include rated wind speed, rated power, rotor diameter, hub height, power and thrust curves; The geographic coordinates include the latitude and longitude information of each wind turbine and the specific geographic coordinate system.

4. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 3, characterized in that, Step S2 includes: S2.1, convert the geographic coordinates of each wind turbine into projected coordinates in a projected coordinate system that meets the directional accuracy requirements, and obtain the projected coordinates of the wind turbine. S2.2, calculate the relative distance and relative orientation angle between every two wind turbines in the wind farm; S2.3, select the N wind turbine units closest to T0 and form a set, denoted as {T1, T2, ..., T0}. N }, which is the unit most affected by the wake effect; where T0 represents a specific wind turbine unit.

5. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 4, characterized in that: In step S3, the wake model of the wind farm project includes a velocity loss model, a wake superposition model, and a turbulence model; The velocity loss model adopts the Gaussian velocity loss model, the wake superposition model adopts the free flow sum of squares superposition model, and the turbulence model adopts the Crespo-Hernandez model.

6. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 5, characterized in that, Step S4 includes: S4.1 Input the projected coordinates, performance parameters and inflow wind direction of a specific wind turbine into the wake model of the wind farm project, and calculate the power generation of T0 under the influence of wake effect under different inflow wind directions; S4.2, when T0 is located at the unit pair {(T1,T0),(T2,T0),…,(T N Downstream of T0, calculate the wind direction within a preset range corresponding to the incoming wind direction that minimizes the power generation of T0, denoted as α1, α2, ..., α N This set of wind directions is taken as the theoretical maximum wake loss wind direction.

7. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 6, characterized in that, Step S5 includes: S5.1 Filter the SCADA data records of T0 records that have a power greater than zero, and the records where the absolute value of the difference between the incoming wind direction and the nacelle direction is less than 15°. S5.2, for {(T1,T0),(T2,T0),…,(T N For each turbine pair in T0, calculate the power ratio of T0 to each wind turbine when T0 is downstream; S5.3 Divide the wind direction into 1° intervals and calculate the ratio of T0 to the power of the upstream unit under different wind directions; S5.4 Find the minimum value of the power ratio within the preset range of the theoretical maximum wake loss wind direction, and use it as the actual maximum wake loss wind direction, denoted as β1, β2, ..., β N .

8. The method for calibrating systematic deviations in wind direction data based on wake effect according to claim 7, characterized in that, The wind direction calibration uncertainty is calculated using the following formula: Where s is the sample standard deviation; Among them, c i Let be the wind direction calibration value for the i-th time, and c be the average value of multiple wind direction calibration values.

9. A system for calibrating systematic biases in wind direction data based on wake effect, characterized in that, include: The acquisition module is used to acquire wind farm data; the wind farm data includes SCADA data records, performance parameters of each wind turbine, and geographical coordinates. The filtering module is used to calculate the spacing and direction between every two wind turbines in the wind farm and filter out the wind turbines that have the greatest impact on the wake effect of each wind turbine. The model module is used to build wake models for wind farm projects; The theoretical calculation module is used to combine the data of several wind turbines with the greatest wake effect of each wind turbine with the wind farm data, and to calculate the theoretical maximum wake loss wind direction of a specific wind turbine using the wind farm engineering wake model; the specific wind turbine is any wind turbine in the wind farm. The actual calculation module is used to analyze SCADA data records, combine the data of several wind turbines with the greatest wake effect of each wind turbine and the wind farm data, and calculate the actual maximum wake loss wind direction of a specific wind turbine. The mean value module is used to calculate the average value of multiple calibrations of a specific wind turbine; the average value of the calibration is obtained by subtracting the theoretical maximum wake loss wind direction from the actual maximum wake loss wind direction. The calibration module is used to subtract the average value of multiple calibrations of a specific wind turbine from the SCADA wind direction data in the SCADA data record of that specific wind turbine, thereby completing the calibration of that specific wind turbine.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for calibrating systematic deviations of wind direction data based on wake effects as described in any one of claims 1 to 8.

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