Method for detecting wind speed measurement accuracy of anemorumbometer of wind turbine generator

Abstract

The invention belongs to the field of sensor detection, and particularly relates to a method for detecting the wind speed measurement accuracy of an anemorumbometer of a wind turbine generator, and the method comprises the steps: obtaining SCADA data in a preset time level, and carrying out the cleaning of the data, and obtaining the cleaning data; grouping the cleaning data according to the working stage to obtain grouped data; constructing a plurality of power cells according to the manufacturer power curve; counting the number of data points, falling into different power cells, of the cleaning data, and removing the power cells to obtain an effective interval; calculating the mean value and the standard deviation of the wind speed in each effective interval, and obtaining the wind speed measurement state measured by the anemorumbometer in combination with the theoretical wind speed of the power curve of the manufacturer; and judging whether the measurement result of the anemorumbometer is accurate according to the wind speed measurement state. The method has the effect of improving the accuracy of judging the measurement result of the anemorumbometer.

Description

Technical Field

[0001] This invention belongs to the field of sensor detection, and specifically relates to a method for detecting the accuracy of wind speed measurement by an anemometer of a wind turbine. Background Technology

[0002] Anemometers are the core sensor components of wind turbines, their function being to measure wind speed and direction data in real time at the turbine's location. The wind turbine control system uses these measurements to determine when to switch operating states and adjusts the nacelle's orientation via the yaw system to align with the incoming wind direction, thereby maximizing wind energy capture efficiency. Therefore, the accuracy of the anemometer's measurements directly affects the wind turbine's power generation performance and operational safety.

[0003] However, due to their long-term exposure to the outside of the nacelle, anemometers are susceptible to environmental factors such as dust, corrosion, and freezing, leading to distorted measurement data. This deviation can severely impact the operational decisions of wind turbines, especially during the startup phase: if the measured wind speed is too low, the control system may misjudge the actual wind speed as being lower than the cut-in wind speed, causing the turbine to fail to start during many periods when power generation is possible, wasting wind energy resources; if the measured wind speed is too high, the turbine may start prematurely when the wind speed is insufficient, frequently switching to a constant wind state due to the inability to generate power normally. This not only reduces power generation efficiency but also accelerates the wear and tear on components such as the gearbox and oil pump, affecting equipment lifespan.

[0004] In related technologies, conventional analysis is usually performed based on the wind speed-power data of the actual operation of the wind turbine to determine whether the deviation from the theoretical wind speed and power data is too large, or simple statistical analysis is performed on the wind speed and power data, such as calculating the mean and variance, in order to determine whether there is a general deviation in the wind speed measurement.

[0005] Regarding the aforementioned technologies, whether it is a conventional analysis based on the wind speed-power data of the actual operation of the wind turbine or a simple statistical analysis of the wind speed and power data, such as calculating the mean and variance, to determine whether there is a general deviation in the wind speed measurement, there is a lack of in-depth comparison with the manufacturer's theoretical power curve, and the special influence of different operating states of the wind turbine (especially the start-up state) on the accuracy of wind speed measurement is not fully considered, which leads to a large deviation in judging the accuracy of the anemometer measurement results. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for detecting the accuracy of wind speed measurement by an anemometer of a wind turbine, thereby improving the accuracy of judging the measurement results of the anemometer.

[0007] A method for detecting the accuracy of wind speed measurement using an anemometer on a wind turbine includes: Acquire SCADA data within a preset time frame; The SCADA data is cleaned to obtain cleaned data; The working stages of the wind turbine are obtained, including the start-up stage, the maximum wind energy capture stage, the constant speed stage, and the rated power generation stage. The cleaning data is grouped according to the working stages to obtain grouped data. The grouped data of the start-up stage, the maximum wind energy capture stage, and the constant speed stage are selected as the analysis data. Obtain the manufacturer's power curve of the wind turbine under local air density conditions, and construct several power intervals based on the manufacturer's power curve; The number of data points falling into different power intervals is counted to obtain the number of data points in each power interval. Based on the number of data points, the power intervals are eliminated to obtain the effective intervals. The mean and standard deviation of wind speed in each effective interval are calculated based on the cleaning data. Based on the mean and standard deviation of wind speed, the theoretical wind speed and threshold of the manufacturer's power curve, the wind speed measurement status of the anemometer is obtained. The wind speed measurement status includes a healthy state, a small measurement deviation state that recommends planned maintenance, and a large measurement deviation state that recommends immediate maintenance. If the wind speed and direction meter measures the wind speed in a healthy state, then the wind speed and direction meter measurement is accurate. If the wind speed and direction meter measures wind speed in a state of small or large measurement deviation, then the wind speed and direction meter measurement is inaccurate.

[0008] Optionally, the step of cleaning the SCADA data to obtain cleaned data includes: The first data is obtained by taking the shutdown data in the SCADA data where the wind speed is ≤0, the generator speed is ≤0, and the active power is ≤0. The first data was cleaned to obtain the second data, which included wind curtailment data with active power less than 0.85 times the rated power and a pitch angle greater than 4°. The second data is divided into multiple wind speed intervals according to a preset interval. The first quantile Q1 and the third quantile Q3 of the power sequence are statistically analyzed for each wind speed interval, and the quantile distance I = Q3 - Q1 is calculated. Discrete data points with power less than Q1 - 1.5I and greater than Q3 + 1.5I are removed to obtain cleaned data.

[0009] Optionally, the operational phase of acquiring the wind turbine generator includes: The startup phase is when the wind turbine starts from a standstill, the wind speed and active power are both less than the corresponding thresholds, the pitch angle is 0, and the generator speed gradually increases from 0 and remains at the lowest grid-connected speed. As wind speed gradually increases, the tip speed ratio gradually decreases, and the wind energy utilization coefficient gradually increases to the Betz limit. The wind turbine maintains the maximum wind energy utilization coefficient Cp by continuously increasing the speed to the rated speed. The stage in which the speed gradually increases from the lowest grid-connected speed to the rated speed, the active power gradually increases but does not reach the rated power, and the pitch angle is 0 is the maximum wind energy capture stage. The wind turbine speed no longer increases and remains at the rated speed. The active power of the wind turbine is increased to the rated power by increasing the torque. The constant speed stage is when the pitch angle is also 0. When the wind speed exceeds the rated wind speed, the wind turbine generator operates at its rated power output during the rated power output phase.

[0010] Optionally, obtaining the manufacturer's power curve of the wind turbine under local air density conditions, and constructing several power intervals based on the manufacturer's power curve, includes: Obtain the manufacturer's power curve for the wind turbine under local air density conditions; Based on the manufacturer's power curve, obtain the corresponding active power at different wind speeds; Set the interval length; Based on the active power and interval length corresponding to different wind speeds, several power sub-intervals are constructed.

[0011] Optionally, the step of counting the number of data points falling into different power intervals to obtain the number of data points in each power interval, and then eliminating power intervals based on the number of data points to obtain valid intervals includes: The number of data points in each power sub-interval is counted to obtain the number of data points in each power sub-interval. Determine whether the number of data points in each power sub-range is less than a preset threshold. If the number of data points in each power interval is less than a preset threshold, then the power intervals with fewer data points than the preset threshold are removed to obtain the effective intervals.

[0012] Optionally, the step of calculating the mean and standard deviation of wind speed in each effective interval based on the cleaning data, and obtaining the wind speed measurement status measured by the anemometer based on the mean and standard deviation of wind speed, the theoretical wind speed from the manufacturer's power curve, and the threshold, includes: The mean and standard deviation of wind speed in each effective interval are calculated based on the cleaning data. If the wind speed in the manufacturer's power curve is within the effective range, the wind speed distribution is all in […]. μ-0.5σ,μ+0.5σ If the wind speed and direction are measured by the anemometer, then the wind speed measurement status is considered to be in a healthy state. If the wind speed in the manufacturer's power curve is within the effective range, the wind speed distribution is all in […]. μ-σ,μ-0.5σ]∪[ m+ 0.5σ, μ+σ If the wind speed and direction are measured, the wind speed measurement state obtained by the anemometer is a small measurement deviation state; If the wind speed in the manufacturer's power curve is within the effective range, the wind speed distribution is all in […]. -∞,μ-σ ]∪[ m+s,+ ∞ If this is the case, then the wind speed measurement state obtained by the anemometer is a state of large measurement deviation. in, m The mean, s The standard deviation is denoted as .

[0013] Optionally, the SCADA data includes wind speed, active power, pitch angle, and rotational speed.

[0014] A system for detecting the accuracy of wind speed measurement using an anemometer on a wind turbine unit, comprising: The first acquisition module is used to acquire SCADA data within a preset time period; The cleaning module is used to clean SCADA data to obtain cleaned data; The grouping module is used to obtain the working stages of the wind turbine, which include the start-up stage, the maximum wind energy capture stage, the constant speed stage, and the rated power generation stage. The cleaning data is grouped according to the working stages to obtain grouped data. The grouped data of the start-up stage, the maximum wind energy capture stage, and the constant speed stage are selected as the analysis data. The second acquisition module is used to acquire the manufacturer's power curve of the wind turbine under local air density conditions, and construct several power intervals based on the manufacturer's power curve. The filtering module is used to count the number of data points in the analysis data that fall into different power intervals, obtain the number of data points in each power interval, and eliminate power intervals based on the number of data points to obtain the effective intervals; The calculation module is used to calculate the mean and standard deviation of the wind speed in each effective interval based on the cleaning data. Based on the mean and standard deviation of the wind speed and the theoretical wind speed from the manufacturer's power curve, the wind speed measurement status measured by the anemometer is obtained. The wind speed measurement status includes a healthy status, a small measurement deviation status that suggests planned maintenance, and a large measurement deviation status that suggests immediate maintenance. If the wind speed and direction meter measures the wind speed in a healthy state, then the wind speed and direction meter measurement is accurate. If the wind speed and direction meter measures wind speed in a state of small or large measurement deviation, then the wind speed and direction meter measurement is inaccurate.

[0015] A terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs a method for detecting the accuracy of wind speed measurement using an anemometer on a wind turbine. A computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, employs a method for detecting the accuracy of wind speed measurement using an anemometer on a wind turbine.

[0016] The beneficial effects of this invention are: The process involves acquiring and cleaning SCADA data within a preset time frame to obtain cleaned data; grouping the cleaned data according to the operating stage to obtain grouped data; constructing several power intervals based on the manufacturer's power curve; counting the number of data points falling within different power intervals and eliminating intervals based on the number of data points to obtain valid intervals; calculating the mean and standard deviation of wind speed in each valid interval; and determining the wind speed measurement status of the anemometer based on the mean, standard deviation, and theoretical wind speed from the manufacturer's power curve. If the anemometer's measurement status is healthy, the anemometer is considered accurate; if it is in a state of small or large deviation, the anemometer is considered inaccurate. Compared to traditional power curve analysis or power data statistics, this application not only considers the impact of different operating states on the accuracy of anemometer measurements but also compares the actual wind speed-power data of the wind turbine with the manufacturer's theoretical power curve, thus improving the judgment of the accuracy of the anemometer's measurement. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the method for testing the accuracy of wind speed measurement using an anemometer on a wind turbine unit; Figure 2 This chart compares the duration of normal power generation, the duration of waiting-for-wind status, and the power generation statistics of Unit 1 (healthy) and Unit 9 (faulty). Figure 3 This is a graph showing the manufacturer's theoretical wind speed and power curves for the wind turbine model used in the case study under local air density conditions. Figure 4 This graph shows the analysis results of the anemometer measurement deviation of Unit 9, which was faulty. Figure 5 This graph shows the results of the anemometer deviation analysis for Unit 1 (a healthy unit). Figure 6 Case 1: Comparison of actual operating power and manufacturer's theoretical operating power during the recent startup phase of Unit 9 (faulty unit); Figure 7Case 2: Comparison of actual operating power and manufacturer's theoretical operating power during the recent startup phase of Unit 9 (faulty unit); Figure 8 Case 3: Comparison of actual operating power and manufacturer's theoretical operating power during the recent startup phase of Unit 9 (faulty unit); Figure 9 This is a case study of Unit 1 in its recent startup phase: a comparison chart of actual operating power and theoretical operating power from the manufacturer. Figure 10 This shows a comparison between the wind power curves of Unit 9 before and after wind speed correction and the manufacturer's wind power curve. Detailed Implementation

[0018] A method for detecting the accuracy of wind speed measurement using an anemometer on a wind turbine, such as... Figure 1 As shown, the present invention includes: S1. Obtain SCADA data within a preset time period.

[0019] Specifically, the time level refers to the time interval for data collection, which is set to 1 minute in this embodiment.

[0020] SCADA data includes wind speed, active power, pitch angle, and speed.

[0021] S2. Clean the SCADA data to obtain cleaned data.

[0022] The SCADA data is cleaned to obtain the cleaned data, which includes: The first data is obtained by taking the shutdown data in the SCADA data where the wind speed is ≤0, the generator speed is ≤0, and the active power is ≤0. The first data was cleaned to obtain the second data, which included wind curtailment data with active power less than 0.85 times the rated power and a pitch angle greater than 4°. The second data is divided into multiple wind speed intervals according to a preset interval. The first quantile Q1 and the third quantile Q3 of the power sequence are statistically analyzed for each wind speed interval, and the quantile distance I = Q3 - Q1 is calculated. Discrete data points with power less than Q1 - 1.5I and greater than Q3 + 1.5I are removed to obtain cleaned data.

[0023] Specifically, the second data is divided into multiple wind speed intervals at preset intervals. Power data within each wind speed interval is used as the statistical object, and all power values ​​are sorted in ascending order to form a power sequence. Then, the first quantile Q1 (the value at the 25th percentile after sorting, indicating that 25% of the power values ​​are less than or equal to it) and the third quantile Q3 (the value at the 75th percentile after sorting, indicating that 75% of the power values ​​are less than or equal to it) are determined, and the quantile interval I = Q3 - Q1 is calculated. Subsequently, a reasonable lower limit (Q1 - 1.5 × I) and upper limit (Q3 + 1.5 × I) of the power range are calculated based on Q1 and Q3. Data points with power values ​​less than Q1 - 1.5 × I or greater than Q3 + 1.5 × I are considered abnormal or outlier discrete data points and are removed, thus retaining stable and reasonable power data within the wind speed interval, ultimately obtaining the overall cleaned data.

[0024] S3. Obtain the operating stages of the wind turbine, which include the startup stage, maximum wind energy capture stage, constant speed stage, and rated power generation stage. Group the cleaning data according to the operating stages to obtain grouped data. Select the grouped data of the startup stage, maximum wind energy capture stage, and constant speed stage as the analysis data.

[0025] The working stages of acquiring wind turbine units include: The startup phase is when the wind turbine starts from a standstill, the wind speed and active power are both less than the corresponding thresholds, the pitch angle is 0, and the generator speed gradually increases from 0 and remains at the lowest grid-connected speed. As wind speed gradually increases, the tip speed ratio gradually decreases, and the wind energy utilization coefficient gradually increases to the Betz limit. The wind turbine maintains the maximum wind energy utilization coefficient Cp by continuously increasing the speed to the rated speed. The stage in which the speed gradually increases from the lowest grid-connected speed to the rated speed, the active power gradually increases but does not reach the rated power, and the pitch angle is 0 is the maximum wind energy capture stage. The wind turbine speed no longer increases and remains at the rated speed. The active power of the wind turbine is increased to the rated power by increasing the torque. The constant speed stage is when the pitch angle is also 0. When the wind speed exceeds the rated wind speed, the wind turbine generator operates at its rated power output during the rated power output phase.

[0026] S4. Obtain the manufacturer's power curve for the wind turbine under local air density conditions, and construct several power intervals based on the manufacturer's power curve.

[0027] Obtain the manufacturer's power curve for the wind turbine under local air density conditions. Based on the manufacturer's power curve, construct several power intervals, including: Obtain the manufacturer's power curve for the wind turbine under local air density conditions; Based on the manufacturer's power curve, obtain the corresponding active power at different wind speeds; Set the interval length; Based on the active power and interval length corresponding to different wind speeds, several power sub-intervals are constructed.

[0028] Specifically, the manufacturer's power curves for wind turbines under local air density are collected, consisting of wind speed and the corresponding active power output at that wind speed. For example, under wind speed v1, the theoretical active power of the wind turbine is p1; under wind speed v2, the theoretical active power is p2, and so on. Data from three stages—startup, maximum wind energy capture, and constant speed—are analyzed, with wind speeds greater than the cut-in wind speed but less than the rated wind speed. Power intervals [p1-Δ, p1+Δ], [p2-Δ, p2+Δ], etc., are constructed centered on the active power outputs p1, p2, etc., in the manufacturer's power curves, where Δ represents half the interval width. The cut-in wind speed is the minimum wind speed at which the turbine begins grid connection; below this wind speed, the turbine does not generate electricity or its power output is zero.

[0029] S5. Count the number of data points in different power intervals of the cleaned data, obtain the number of data points in each power interval, and remove the power intervals according to the number of data points to obtain the effective intervals.

[0030] The number of data points falling within different power intervals is counted to obtain the number of data points in each power interval. Power intervals are then removed based on the number of data points, resulting in the following valid intervals: The number of data points in each power sub-interval is counted by counting the number of data points in the cleaned data. Determine whether the number of data points in each power sub-range is less than a preset threshold. If the number of data points in each power interval is less than a preset threshold, then the power intervals with fewer data points than the preset threshold are removed to obtain the effective intervals.

[0031] Specifically, the cleaned SCADA data is divided into power intervals such as [p1-Δ, p1+Δ], [p2-Δ, p2+Δ], etc. The number of data points falling within each interval is counted. Intervals with less than a certain amount of data are not representative and are removed.

[0032] S6. Calculate the mean and standard deviation of wind speed in each effective interval based on the cleaning data. Based on the mean and standard deviation of wind speed and the theoretical wind speed from the manufacturer's power curve, obtain the wind speed measurement status measured by the anemometer. The wind speed measurement status includes a healthy state, a small measurement deviation state that suggests planned maintenance, and a large measurement deviation state that suggests immediate maintenance.

[0033] Based on the cleaning data, the mean and standard deviation of the wind speed in each effective interval are calculated. Using the mean, standard deviation, and theoretical wind speed from the manufacturer's power curve, the wind speed measurement status obtained by the anemometer includes: Calculate the mean and standard deviation of wind speed for each effective interval based on the cleaning data; If the theoretical wind speed of the manufacturer's power curve is within the effective range of wind speed distribution, then... μ-0.5σ,μ+0.5 s If the wind speed and direction are measured by the anemometer, then the wind speed measurement status is considered to be in a healthy state. If the theoretical wind speed of the manufacturer's power curve is within the effective range of wind speed distribution, then... μ-σ,μ-0.5σ ]∪[ μ+0.5σ,μ+σ If the wind speed and direction are measured, the wind speed measurement state obtained by the anemometer is a small measurement deviation state; If the theoretical wind speed of the manufacturer's power curve is within the effective range of wind speed distribution, then... -∞,μ-σ ]∪[ m+ σ,+∞ If this is the case, then the wind speed measurement state obtained by the anemometer is a state of large measurement deviation. in, m The mean, s The standard deviation is denoted as .

[0034] S7. If the wind speed and direction meter measures the wind speed in a healthy state, then the wind speed and direction meter measurement is accurate.

[0035] S8. If the wind speed and direction meter measures wind speed in a state of small or large measurement deviation, then the wind speed and direction meter measurement is inaccurate.

[0036] For units with severe measurement deviations and inability to be maintained in a timely manner, a monotonic piecewise linear calibration method is used to temporarily correct the measured wind speed. Specifically, the mean wind speed μ calculated in step 5 within each small interval is... n Wind speed v corresponding to the manufacturer's power curve n Pairing as calibrated nodes: (μ1, v1), (μ2, v2)...(μ n v n For any measured wind speed, if μ k ≤x≤μ k +1, then correct the wind speed. If x < μ1 or x > μ n Then, the slope of the first or last segment is used for linear extrapolation, where, x For wind speed, μ k For the first k The average wind speed in each interval.

[0037] It is worth noting that if the wind speed on the power curve is greater than the average wind speed in each interval, it can be assumed that the wind speed measured by the anemometer is too low; if the wind speed on the power curve is less than the average wind speed in each interval, it is necessary to first ensure that all components of the unit are in good operating condition and rule out the possibility of reduced power generation efficiency due to wind turbine performance issues before it can be assumed that there is a measurement deviation in the anemometer.

[0038] By utilizing SCADA operating data, statistical analysis is performed on various power segments. Measurement deviations are quantified based on the degree of distribution offset, and the analysis results are verified during the startup phase. This provides a verification method for the accuracy of wind speed and direction measurements by anemometers, and also offers a temporary wind speed correction method for units with severe measurement deviations that cannot be maintained in a timely manner. Specific implementation examples: The flowchart of the method for detecting the accuracy of wind speed measurement using an anemometer for wind turbine units is as follows: Figure 1 As shown. The example data comes from the actual SCADA data of two 2.0MW wind turbine units (Unit 1 and Unit 9), with a data sampling interval of 1 minute. Data from July 1, 2024 to December 31, 2024 (six months) is used for analysis. Unit 1 is a healthy operating unit, while Unit 9 has an anemometer measurement anomaly. The duration of normal power generation, the duration of waiting-for-wind operation, and power generation statistics are as follows. Figure 2 As shown in the figure, it can be seen that the wind speed measured by the anemometer is too low, resulting in a significantly shorter power generation time for Unit 9 than for Unit 1 in six months, a significantly longer waiting time for wind than for Unit 1, and a lower total power generation for Unit 9 in six months than for Unit 1.

[0040] S1: Extract SCADA data for the healthy operation period of wind turbines at the 1-minute level and clean up data from abnormal power generation states. Step 1: Clean up shutdown data with wind speed ≤ 0, generator speed ≤ 0, or active power ≤ 0. Step 2: Clean up curtailment data with active power less than 0.85 times the rated power and a pitch angle greater than 4°. Step 3: Divide the data into multiple wind speed intervals at 0.1 m / s intervals, statistically analyze the first quantile Q1 and the third quantile Q3 of the power sequence for each wind speed interval, and calculate the quantile interval I = Q3 - Q1. Based on the calculated Q1, Q3, and I, remove discrete data points with power less than Q1 - 1.5I and greater than Q3 + 1.5I. S2: Based on the operating mechanism of wind turbine units, the power generation state of wind turbine units is divided into four stages: the start-up stage, the maximum wind energy capture stage, the constant speed stage, and the rated power generation stage. S3: Collect the manufacturer's power curve for wind turbines under local air density conditions, consisting of wind speed and the corresponding active power output. Figure 3As shown. Wind speed data (3m / s ≤ wind speed ≤ 9m / s, minimum grid-connected speed ≤ generator speed ≤ rated speed, 0 ≤ active power ≤ rated power, pitch angle = 0) from the startup phase, maximum wind energy capture phase, and constant speed phase are used for analysis. Based on the manufacturer's power curve, the following 7 power points are selected as interval centers: p1=24kW, p2=133kW, p3=300kW, p4=548kW, p5=887kW, p6=1297kW, and p7=1702kW. Based on each center point, the single-sided width of the power interval is set to Δ≈25kW, ultimately constructing 7 power intervals as follows: (0, 50], [100, 150], [275, 325], [525, 575], [855, 905], [1275, 325], and [1675, 1725]. S4: Divide the processed SCADA data into power intervals listed in S3. All intervals contain more than 300 data points and do not require filtering. S5: Statistically analyze the wind speed distribution within each effective interval and calculate the mean wind speed μ within each interval. n With σ n The values ​​{(2.11, 0.48), (3.12, 0.41), (4.39, 0.38), (5.49, 0.38), (6.41, 0.39), (7.39, 0.38), (8.33, 0.37)} show that the manufacturer's theoretical wind speed is outside the severely deviated threshold line in each interval. Figure 4 As shown, it can be determined that the wind speed measured by the anemometer is too low, and based on the threshold, it can be concluded that the unit has a large measurement deviation, and wind speed measurement correction and maintenance should be carried out immediately. The calculated results of the mean wind speed μn and σn in the section of Unit 1 (healthy unit) are: {(3.09, 0.39), (3.96, 0.31), (5.11, 0.28), (6.15, 0.31), (7.05, 0.33), (7.95, 0.32), (8.92, 0.32)}. The anemometer measurement deviation analysis results are as follows: Figure 5 As shown in the figure, the comparison shows that the manufacturer's theoretical wind speed is within the normal wind speed measurement deviation range in each interval, which indicates that the wind speed measured by its anemometer is relatively accurate. S6: Analyze three sets of startup phase data for Unit 9, showing its recent transition from a constant wind state (wind turbine status value 9 in the SCADA system) to a grid-connected power generation state (wind turbine status value 3 in the SCADA system). Figure 6 to Figure 8 As shown in the three graphs, it can be seen that in each startup phase, the actual operating power is higher than the manufacturer's theoretical power value at the same wind speed. Therefore, it can be determined that the wind speed measured by the anemometer is too low. Example of the startup phase of Unit 1 (healthy unit). Figure 9 As shown, the actual operating power is close to the manufacturer's theoretical power value at the same wind speed, which further confirms that the wind speed measured by its anemometer is relatively accurate.

[0041] S7: For the faulty Unit 9, if wind speed measurement calibration and maintenance cannot be performed in a timely manner, a monotonic piecewise linear calibration method can be used to temporarily correct the measured wind speed. From S3 and S5, the calibration nodes are: (2.11, 3), (3.12, 4), (4.39, 5), (5.49, 6), (6.41, 7), (7.39, 8), (8.33, 9). According to the formula... The calculation was corrected, and the fitting results of the wind power curves before and after the correction are as follows: Figure 10 As shown, the corrected wind power curve is more consistent with the standard wind power curve provided by the manufacturer, providing a more reliable source of wind speed data for wind turbine control.

[0042] A system for detecting the accuracy of wind speed measurement using an anemometer on a wind turbine unit, comprising: The first acquisition module is used to acquire SCADA data within a preset time period; The cleaning module is used to clean SCADA data to obtain cleaned data; The grouping module is used to acquire the working stages of the wind turbine, which include the startup stage, the maximum wind energy capture stage, the constant speed stage, and the rated power generation stage. The cleaning data is grouped according to the working stages to obtain grouped data. The grouped data of the startup stage, the maximum wind energy capture stage, and the constant speed stage are selected as the analysis data. The second acquisition module is used to acquire the manufacturer's power curve of the wind turbine under local air density conditions, and construct several power intervals based on the manufacturer's power curve. The filtering module is used to statistically analyze the number of data points falling into different power intervals, obtain the number of data points in each power interval, and eliminate power intervals based on the number of data points to obtain the effective intervals; The calculation module is used to calculate the mean and standard deviation of wind speed in each effective interval based on the cleaning data. Based on the mean and standard deviation of wind speed and the theoretical wind speed from the manufacturer's power curve, the wind speed measurement status measured by the anemometer is obtained. The wind speed measurement status includes a healthy status, a small measurement deviation status that suggests planned maintenance, and a large measurement deviation status that suggests immediate maintenance. If the wind speed and direction meter measures the wind speed in a healthy state, then the wind speed and direction meter measurement is accurate. If the wind speed and direction meter measures wind speed in a state of small or large measurement deviation, then the wind speed and direction meter measurement is inaccurate.

[0043] This application also discloses a terminal device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs a method for detecting the accuracy of wind speed measurement by an anemometer of a wind turbine.

[0044] The terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server. The terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0045] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0046] The memory can be an internal storage unit of the terminal device, such as a hard disk or RAM of the terminal device, or an external storage device of the terminal device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the terminal device. Furthermore, the memory can be a combination of internal storage units and external storage devices of the terminal device. The memory is used to store computer programs and other programs and data required by the terminal device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0047] In this terminal device, the method for detecting the accuracy of wind speed measurement by the wind speed and direction instrument of a wind turbine in the above embodiment is stored in the memory of the terminal device and loaded and executed on the processor of the terminal device for convenient use.

[0048] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it employs a method for detecting the accuracy of wind speed measurement by an anemometer of a wind turbine in the above embodiments.

[0049] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0050] The method for detecting the accuracy of wind speed measurement by an anemometer of a wind turbine in the above embodiments is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0051] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of protection of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of one or more embodiments of this application as described above, which are not provided in detail for the sake of brevity.

[0052] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

Claims

1. A method of detecting the accuracy of wind speed measurements by a wind speed and direction sensor of a wind turbine, characterized in that The method comprises the following steps: acquiring SCADA data in a preset time level; cleaning the SCADA data to obtain cleaned data; acquiring a working stage of the wind turbine, the working stage comprising a starting stage, a maximum wind energy capture stage, a constant speed stage and a rated power generation stage, grouping the cleaned data according to the working stage to obtain grouped data, and selecting the grouped data of the starting stage, the maximum wind energy capture stage and the constant speed stage as analysis data; acquiring a manufacturer's power curve of the wind turbine under local air density conditions, and constructing a plurality of power small intervals according to the manufacturer's power curve; counting the number of data points of the analysis data falling in different power small intervals to obtain the number of data points of each power small interval, and removing the power small intervals according to the number of data points to obtain effective intervals; calculating the mean and standard deviation of the wind speed of each effective interval according to the cleaned data, and obtaining a wind speed measurement state of a wind speed and direction anemometer according to the mean and standard deviation of the wind speed, the standard deviation and the theoretical wind speed of the manufacturer's power curve, the wind speed measurement state comprising a healthy state, a small measurement deviation state for which planned maintenance is recommended, and a large measurement deviation state for which immediate maintenance is recommended; if the wind speed measurement state of the wind speed and direction anemometer is the healthy state, it is determined that the wind speed and direction anemometer measures accurately; if the wind speed measurement state of the wind speed and direction anemometer is the small measurement deviation state or the large measurement deviation state, it is determined that the wind speed and direction anemometer measures inaccurately.

2. The method of claim 1 wherein, The cleaning of the SCADA data to obtain cleaned data comprises the following steps: obtaining first data by removing shutdown data with wind speed ≤0, generator speed ≤0 and active power ≤0 in the SCADA data; obtaining second data by cleaning abandoned wind power and limited power data with active power less than 0.85 times rated power and pitch angle greater than 4° in the first data; dividing the second data into a plurality of wind speed small intervals according to a preset interval, counting the first quantile Q1 and the third quantile Q3 of the power sequence of each wind speed small interval, calculating the quantile interval I = Q3-Q1, and removing discrete data points with power less than Q1-1.5I and greater than Q3+1.5I to obtain cleaned data.

3. The method of claim 1 wherein, The acquisition of the working stage of the wind turbine comprises the following steps: the starting stage, in which the wind turbine starts from a stationary state, the wind speed and the active power are both less than the corresponding threshold value, the pitch angle is 0, and the generator speed gradually increases from 0 and is maintained at the minimum grid-connected speed; the maximum wind energy capture stage, in which the wind speed gradually increases, the tip speed ratio gradually decreases, the wind energy utilization coefficient gradually increases to the Betz limit, the wind turbine maintains the maximum wind energy utilization coefficient Cp by continuously increasing the speed to the rated speed, the speed gradually increases from the minimum grid-connected speed to the rated speed, the active power gradually increases but is less than the rated power, and the pitch angle is 0; the constant speed stage, in which the wind turbine speed no longer increases and is maintained at the rated speed, the active power of the wind turbine is increased to the rated power by increasing the torque, and the pitch angle is also 0; the rated power generation stage, in which the wind speed exceeds the rated wind speed, and the wind turbine generates power at the rated power.

4. The method of claim 1 wherein, The manufacturer power curve of the wind turbine under local air density conditions is acquired, and a plurality of power small intervals are constructed according to the manufacturer power curve, including: Acquiring the manufacturer power curve of the wind turbine under local air density conditions; According to the manufacturer power curve, the corresponding active power under different wind speeds is acquired; Setting the interval length; According to the corresponding active power under different wind speeds and the interval length, a plurality of power small intervals are constructed.

5. The method of claim 1 wherein, The number of data points of the cleaning data falling in different power small intervals is counted to obtain the number of data points of each power small interval, and the power small intervals are removed according to the number of data points to obtain effective intervals, including: Counting the number of data points of the cleaning data falling in different power small intervals to obtain the number of data points of each power small interval; Judging whether the number of data points of each power small interval is less than a preset number threshold; If the number of data points of each power small interval is less than the preset number threshold, the power small interval with the number of data points less than the preset number threshold is removed to obtain the effective interval.

6. The method of claim 1 wherein, The mean and standard deviation of the wind speed of each effective interval are calculated according to the cleaning data, and the wind speed measurement state measured by the wind speed and direction instrument is obtained according to the mean, standard deviation, and theoretical wind speed of the manufacturer power curve, including: According to the cleaning data, the mean and standard deviation of the wind speed of each effective interval are calculated; If the theoretical wind speed of the manufacturer power curve is in the wind speed distribution in the effective range[ μ-0.5σ, μ+0.5σ ], the wind speed measurement state of the wind speed and direction instrument is a healthy state; If the theoretical wind speed of the manufacturer power curve is in the wind speed distribution in the effective interval [ μ-σ, μ-0.5σ ]∪[ μ+ 0.5σ, μ+σ ], then the wind speed measurement state of the wind speed and direction instrument measurement is a small measurement deviation state; If the theoretical wind speed of the manufacturer power curve is in the wind speed distribution in the effective range[ -∞, μ-σ ]∪[ μ+σ,+ ∞ ], then the wind speed measurement state of the wind speed and direction instrument measurement is a large measurement deviation state; wherein μ is the mean, σ is the standard deviation.

7. The method of claim 6 wherein, The SCADA data includes wind speed, active power, pitch angle, and rotating speed.

8. A system for detecting the accuracy of wind speed measurements by a wind speed and direction sensor of a wind turbine, characterized in that Including: The first acquisition module is configured to acquire SCADA data in a preset time level; The cleaning module is configured to clean the SCADA data to obtain cleaning data; The grouping module is configured to acquire the working stages of the wind turbine, including the starting stage, the maximum wind energy capture stage, the constant speed stage, and the rated power generation stage, group the cleaning data according to the working stages to obtain grouped data, and select the grouped data of the starting stage, the maximum wind energy capture stage, and the constant speed stage as analysis data; The second acquisition module is configured to acquire the manufacturer power curve of the wind turbine under local air density conditions, and construct a plurality of power small intervals according to the manufacturer power curve; The screening module is configured to count the number of data points of the analysis data falling in different power small intervals to obtain the number of data points of each power small interval, and remove the power small intervals according to the number of data points to obtain effective intervals; The calculation module is configured to calculate the mean and standard deviation of the wind speed of each effective interval according to the cleaning data, and obtain the wind speed measurement state measured by the wind speed and direction instrument according to the mean, standard deviation, and theoretical wind speed of the manufacturer power curve, including the healthy state, the small measurement deviation state suggesting to perform planned maintenance, and the large measurement deviation state suggesting to perform immediate maintenance; If the wind speed measurement state measured by the wind speed and direction instrument is the healthy state, the wind speed and direction instrument is measured accurately. If the wind speed and direction meter measurement state is a small measurement deviation state or a large measurement deviation state, it is determined that the wind speed and direction meter measurement is inaccurate.

9. A terminal device comprising a memory and a processor, characterized in that, The memory stores a computer program capable of running on the processor, and the processor loads and executes the computer program, and adopts the method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored therein a computer program, characterized in that, The computer program is loaded and executed by the processor, and the method in any one of claims 1 to 7 is adopted.

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