Calibration method and system for cabin-type laser radar

Abstract

The present disclosure proposes a calibration method and system for a nacelle-type laser radar. In an area with flat terrain, a wind tower and a placement tower are set at a preset distance apart. A sensor group is arranged on the wind tower, and a test platform, an engine, and a nacelle-type laser radar are arranged on the placement tower. The nacelle-type laser radar is set on the test platform, and the engine is located below the test platform, with the engine bracket in contact with the test platform. The calibration method includes: while the engine is running, using the nacelle-type laser radar and the sensor group to collect data respectively to obtain a measured data set and a reference data set; calculating a slope and a correlation coefficient based on the measured data set and the reference data set; if the slope and correlation coefficient meet the requirements, calculating the uncertainty based on the measured data set and the reference data set, and calibrating the nacelle-type laser radar based on the uncertainty. The method according to the present disclosure can improve the accuracy of nacelle-type laser radar calibration.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wind power wind measurement, and in particular to a calibration method and system for a nacelle-type laser radar. Background Art

[0002] With the continuous advancement of technology, intelligentization is a key industry trend, and the intelligent development of wind turbines is an inevitable trend. As we all know, wind is highly uncertain, a major factor hindering the implementation of wind power projects. However, wind is also the key to wind power technology and a source of energy. Wind directly affects the design cost of turbines and threatens their operational safety. Therefore, accurate wind measurement and reduced error are particularly important. The application of LiDAR in the wind power industry has accelerated the development of intelligentization. Nacelle-mounted LiDAR provides more possibilities for accurate wind measurement for wind turbines. It can predict the next wind conditions in advance and adjust control target values ​​in advance, allowing the controller to perform global optimization for wind conditions over a longer period of time. It can also reduce turbine operation frequency, effectively reduce loads, and extend turbine life. Therefore, the accuracy calibration of LiDAR wind measurement is particularly important. However, existing technologies still need to improve the accuracy calibration of nacelle-mounted LiDAR wind measurement. Summary of the Invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of the present disclosure is to propose a calibration method for a cabin-type laser radar to improve the accuracy of the cabin-type laser radar calibration.

[0005] The second objective of the present disclosure is to provide a calibration system for a cabin-type lidar.

[0006] The third objective of the present disclosure is to provide a calibration device for a cabin-type lidar.

[0007] To achieve the above objectives, a first embodiment of the present disclosure provides a calibration method for a nacelle-mounted laser radar. A wind tower and a deployment tower are set at a preset distance in an area with flat terrain. A sensor group is arranged on the wind tower, and a test platform, an engine, and a nacelle-mounted laser radar are arranged on the deployment tower. The nacelle-mounted laser radar is set on the test platform, and the engine is located under the test platform with the engine bracket in contact with the test platform. The calibration method includes:

[0008] When the engine is running, using the nacelle-type laser radar and the sensor group to collect data respectively to obtain a measured data set and a reference data set;

[0009] Calculating a slope and a correlation coefficient based on the measured data set and the reference data set;

[0010] If the slope and the correlation coefficient meet the requirements, the uncertainty is calculated based on the measured data set and the reference data set, and the cabin-type laser radar is calibrated based on the uncertainty.

[0011] In one embodiment of the present disclosure, before calculating the correlation coefficient based on the measured dataset and the reference dataset, the method further includes: performing data screening processing on the measured dataset and the reference dataset respectively in combination with wind direction requirements, temperature thresholds, and humidity thresholds.

[0012] In one embodiment of the present disclosure, the measured data set includes measured wind speed values ​​and measured wind direction values, the reference data set includes measured wind speed values ​​and measured wind direction values, and the calculating of the slope and correlation coefficient based on the measured data set and the reference data set includes: performing a bivariate linear regression process based on the measured wind speed values ​​and the measured wind speed values ​​to calculate a first slope and a first correlation coefficient; performing a bivariate linear regression process based on the measured wind direction values ​​and the measured wind direction values ​​to calculate a second slope and a second correlation coefficient.

[0013] In one embodiment of the present disclosure, the uncertainty includes wind speed uncertainty, and the method for obtaining the wind speed uncertainty includes: obtaining the standard uncertainty, the first wind speed average value of the wind speed measurement value, and the second wind speed average value of the wind speed measurement value, and calculating the wind speed uncertainty based on the standard uncertainty, the first wind speed average value, and the second wind speed average value.

[0014] In one embodiment of the present disclosure, the test platform is arranged on the top of the placement tower, and the sensor group is arranged at the same height as the position of the wind measurement tower and the nacelle-type laser radar.

[0015] In one embodiment of the present disclosure, the slope and correlation coefficient meeting the requirements means that the slope is within a preset slope range and the correlation coefficient is greater than a preset threshold.

[0016] To achieve the above objectives, a second embodiment of the present disclosure provides a calibration system for a cabin-mounted laser radar, comprising:

[0017] The reference data acquisition module includes a sensor group arranged on a wind tower, and the reference data acquisition module is used to collect data using the sensor group to obtain a reference data set;

[0018] The measured data acquisition module includes a test platform, an engine, and a nacelle-type laser radar arranged on a placement tower. The nacelle-type laser radar is arranged on the test platform. The engine is located under the test platform, and the engine bracket is in contact with the test platform. The measured data acquisition module is used to collect data using the nacelle-type laser radar to obtain a measured data set. The wind measurement tower and the placement tower are separated by a preset distance and the terrain in the area where they are located is flat.

[0019] A processing module is used to obtain the measured data set and the reference data set when the engine is running; calculate a slope and a correlation coefficient based on the measured data set and the reference data set; if the slope and the correlation coefficient meet the requirements, calculate the uncertainty based on the measured data set and the reference data set, and calibrate the cabin-mounted lidar based on the uncertainty.

[0020] In one embodiment of the present disclosure, the processing module is further used to: before calculating the correlation coefficient based on the measured data set and the reference data set, perform data screening processing on the measured data set and the reference data set respectively in combination with the wind direction requirement, temperature threshold, and humidity threshold.

[0021] In one embodiment of the present disclosure, the measured data set includes measured wind speed values ​​and measured wind direction values, the reference data set includes measured wind speed values ​​and measured wind direction values, and the processing module is specifically used to: perform bivariate linear regression processing based on the measured wind speed values ​​and the measured wind speed values, and calculate a first slope and a first correlation coefficient; perform bivariate linear regression processing based on the measured wind direction values ​​and the measured wind direction values, and calculate a second slope and a second correlation coefficient.

[0022] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes a calibration device for a cabin-type lidar, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the calibration method for the cabin-type lidar of the first aspect embodiment of the present disclosure.

[0023] In one or more embodiments of the present disclosure, a wind measurement tower and a placement tower are set at a preset distance apart in an area with flat terrain, a sensor group is arranged on the wind measurement tower, and a test platform, an engine and a nacelle-type lidar are arranged on the placement tower. The nacelle-type lidar is set on the test platform, the engine is located under the test platform and the engine bracket is in contact with the test platform. The calibration method includes: when the engine is running, using the nacelle-type lidar and the sensor group to respectively collect data to obtain a measured data set and a reference data set; calculating a slope and a correlation coefficient based on the measured data set and the reference data set; if the slope and the correlation coefficient meet the requirements, calculating the uncertainty based on the measured data set and the reference data set, and calibrating the nacelle-type lidar based on the uncertainty. In this case, the wind measurement tower and the placement tower are set up in a flat terrain area and separated by a preset distance. The sensor group and the nacelle-type lidar are respectively installed on the wind measurement tower and the placement tower to avoid the influence of obstacles on the sensor group or the nacelle-type lidar. When the engine is running, the test platform vibrates, so that the environment of the nacelle-type lidar is closer to the operating state of the wind turbine, thereby improving the accuracy of the data collected by the nacelle-type lidar and providing a more accurate data basis for subsequent calibration. Then, the measured data set and the reference data set are comprehensively used to calculate the slope, correlation coefficient and uncertainty to calibrate the nacelle-type lidar, which further improves the accuracy of the calibration of the nacelle-type lidar.

[0024] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. The above and / or additional aspects and advantages of the present disclosure will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, among which:

[0026] Figure 1 A schematic diagram of a scene of a calibration method for a cabin-type laser radar provided in an embodiment of the present disclosure;

[0027] Figure 2 A partial schematic diagram of a tower placement provided in an embodiment of the present disclosure;

[0028] Figure 3 A schematic diagram of a flow chart of a calibration method for a cabin-mounted laser radar provided in an embodiment of the present disclosure;

[0029] Figure 4 A schematic diagram of the distribution of two groups of wind speeds provided in an embodiment of the present disclosure;

[0030] Figure 5 A schematic diagram for comparing two groups of wind speed intervals provided in an embodiment of the present disclosure;

[0031] Figure 6 A block diagram of a calibration system for a cabin-mounted laser radar provided in an embodiment of the present disclosure;

[0032] Figure 7 It is a block diagram of a calibration device for a cabin-type laser radar used to implement the calibration method for the cabin-type laser radar according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0034] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0035] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. It should also be understood that the term "and / or" used in the present disclosure refers to and includes any or all possible combinations of one or more associated listed items.

[0036] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0037] The present disclosure provides a calibration method and system for a cabin-type laser radar, the main purpose of which is to improve the accuracy of cabin-type laser radar calibration.

[0038] The nacelle-mounted LiDAR calibration method and system disclosed herein utilizes two wind towers. These towers are a wind measurement tower and a deployment tower. The wind measurement tower and deployment tower are separated by a predetermined distance, which can be 50 to 100 meters. The wind measurement tower is equal to or greater than the deployment tower. Both the wind measurement tower and deployment tower are erected in a flat terrain area. This flat terrain area can be a flat area with no surrounding trees.

[0039] In one embodiment, Figure 1 This is a scene diagram of a calibration method for a cabin-type laser radar provided by an embodiment of the present disclosure. Figure 1 As shown in the figure, A is the wind tower (also called the reference wind tower), and B is the deployment tower (also called the nacelle radar platform tower). Wind tower A and deployment tower B are located in a flat area, and the distance between wind tower A and deployment tower B is the preset distance L, which is 50 meters.

[0040] In this embodiment, a sensor group a is arranged on the wind tower (see Figure 1 ). Sensor group a includes but is not limited to wind speed sensor, wind direction sensor, temperature and humidity sensor, air pressure sensor, etc. Among them, the wind speed sensor is used to collect wind speed signals, and the wind speed sensor includes a main anemometer and a reference anemometer. The main anemometer is used to collect the main wind speed signal, and the reference anemometer is used to collect the reference wind speed signal. Therefore, the wind speed signal includes the main wind speed signal and the reference wind speed signal. The main wind speed signal is used to participate in subsequent wind speed comparison calculations and other processing with the cabin-type lidar. The reference wind speed signal is used to monitor and determine whether the main wind speed signal has a sudden change during the calibration period and whether the performance has changed. The wind direction sensor is used to collect wind direction signals, the temperature and humidity sensor is used to collect temperature and humidity signals, and the air pressure sensor is used for air pressure signals.

[0041] In this embodiment, the sensor group needs to be calibrated before being installed on the wind tower. In addition, each sensor in the sensor group is within its validity period. This ensures the accuracy of the data collected by the sensor group to a certain extent.

[0042] In some embodiments, the wind speed sensor is, for example, a cup anemometer, and the uncertainty level of the cup anemometer is not less than 1.7A or 1.7C.

[0043] In some embodiments, the sensor group is arranged at the same height as the wind tower and the nacelle-type laser radar. This can reduce the influence of the height factor on the subsequent correlation calculation. Figure 1 As shown, the height of the sensor group is H1, the height of the cabin-mounted lidar is H2, and H1 = H2 = 50 meters.

[0044] In this embodiment, a test platform, an engine, and a nacelle-mounted laser radar are placed on a tower. The nacelle-mounted laser radar is mounted on the test platform, and the engine is positioned below the test platform, with its bracket in contact with the test platform. A gap exists between the test platform and the tower. When the engine is running, the test platform vibrates.

[0045] In some embodiments, the test platform is disposed on top of a placement tower.

[0046] In one embodiment, Figure 2 A partial schematic diagram of a tower provided in an embodiment of the present disclosure. Figure 2 As shown, a test platform c is mounted on top of a tower B. A cabin-type lidar b is positioned above test platform c. An engine d with a bracket is placed beneath test platform c, with bracket d1 in contact with the bottom surface of test platform c. When engine d is running, bracket d1 rotates in the direction indicated by the arrow, causing test platform c to vibrate continuously. A gap e is also provided between test platform c and tower B to better simulate the vibrations experienced by a wind turbine (or wind turbine) during operation.

[0047] Figure 3 This is a flow chart of a calibration method for a cabin-type laser radar provided by an embodiment of the present disclosure. Figure 3 As shown, the calibration method of the cabin-type laser radar includes the following steps:

[0048] Step S11: When the engine is running, the cabin-mounted laser radar and the sensor group are used to collect data respectively to obtain a measured data set and a reference data set.

[0049] In step S11, a nacelle-mounted lidar is deployed on a tower. The data collected by the nacelle-mounted lidar constitutes a measured dataset. A sensor group is deployed on the wind tower. The sensor group collects various signals, including the main wind speed signal, wind direction signal, temperature and humidity signal, and air pressure signal, to form a reference dataset. The data types collected by the nacelle-mounted lidar are consistent with those in the reference dataset.

[0050] In step S11, when the nacelle-type lidar is used to collect data, the engine is always in operation. At this time, the test platform is always in a vibrating state, thereby better simulating the vibration state of the fan during operation, making the data collected by the nacelle-type lidar more accurate.

[0051] To prevent the laser beam emitted by the nacelle-mounted LiDAR from hitting the ground and causing inaccurate data, step S11 also calculates the collection distance based on the laser beam angle and the altitude of the nacelle-mounted LiDAR. The data collected by the nacelle-mounted LiDAR is the meteorological data at the collection distance from the tower. At the collection distance, the laser beam emitted by the nacelle-mounted LiDAR is completely in the air.

[0052] In some embodiments, before collecting data using the nacelle-mounted LiDAR and sensor group, they must be synchronized using GPRS time. This ensures that the nacelle-mounted LiDAR and sensor group can synchronously collect wind tower meteorological data (i.e., the reference dataset) and nacelle-mounted LiDAR meteorological data (i.e., the measured dataset). Synchronous collection means that the acquisition time error of the nacelle-mounted LiDAR and sensor group must be within a preset ratio (e.g., 1%). Furthermore, the nacelle-mounted LiDAR and sensor group must be verified and tested for time drift at least weekly.

[0053] In step S11, the data collected by the nacelle-mounted lidar and the sensor group include, but are not limited to, meteorological data such as wind speed, wind direction, temperature and humidity, and air pressure. For example, the measured data set includes measured wind speed values, wind direction values, temperature and humidity values, and air pressure values. The main wind speed signal in the reference data set is also referred to as a wind speed measurement value, the wind direction signal in the reference data set is also referred to as a wind direction measurement value, the temperature and humidity signal in the reference data set is also referred to as a temperature and humidity measurement value, and the air pressure signal in the reference data set is also referred to as an air pressure measurement value.

[0054] In step S11, the data storage time collected by the cabin-type lidar and sensor group is recommended to be a statistical value of a preset time (e.g., 10 minutes). The statistical value can be, for example, the maximum value, minimum value, standard deviation value, and average value of each type of meteorological data.

[0055] In some embodiments, before executing step S12 , the method further includes: performing data screening processing on the measured data set and the reference data set respectively in combination with the wind direction requirement, the temperature threshold, and the humidity threshold.

[0056] Specifically, the screening conditions include a) data on the meteorological equipment (such as the sensor group) on the wind tower and the detection volume of the nacelle-type lidar placed on the tower are not affected by surrounding buildings, trees, running wind turbines, etc.; b) data on the cup anemometer are not affected by the wind tower, lead wires or lightning rods, etc.; c) data on the cup anemometer are not affected by freezing; d) data on the failure of the nacelle-type lidar or the reference wind tower meteorological equipment.

[0057] The affected data in conditions a) and b) are mainly based on the wind direction measured by the wind tower to eliminate invalid data, that is, the sectors where the wind tower and the cabin radar are affected by surrounding buildings, trees, running wind turbines, and sectors affected by wind towers, leads or lightning rods, etc. are calculated, and invalid data is determined based on the sectors. Specifically, the unavailable sectors (i.e., the affected sectors) include the first unavailable sector between the obstacle and the wind tower A and the second unavailable sector between the obstacle and the placement tower B. Obstacles can be, for example, buildings, trees, and stopped adjacent wind turbines. Taking the stopped adjacent wind turbine as an obstacle and calculating the first unavailable sector between the obstacle and the wind tower A as an example, the parameters that need to be considered when calculating the first unavailable sector are the actual horizontal distance Le (i.e., the distance from the obstacle to the wind tower A) and the equivalent rotor diameter De of the obstacle. The stopped adjacent wind turbine can be regarded as a cylinder with a diameter equal to the diameter of the tower base and a height equal to the height of the top of the tower. The equivalent rotor diameter of the obstacle is defined as: Among them, l h is the obstacle height; l w is the width of the obstacle seen from the wind turbine or wind measuring equipment under test. At this time, the first unusable sector between the obstacle and the wind tower A passes through 1.3*arctan(2.5*D e / Le+0.15)+10 is calculated.

[0058] In addition, the data of the cup anemometer in condition b) that is not affected by the wind tower, lead or lightning rod can be obtained based on the correspondence between the main wind speed signal collected by the main anemometer and the reference wind speed signal collected by the reference anemometer. When certain angles change, it means that the cup anemometer is affected.

[0059] Condition c) The cup anemometer may freeze due to factors such as low temperature. Therefore, when the temperature is lower than the temperature threshold (for example, 2°C) and the humidity is higher than the humidity threshold (for example, 80%), the data under this temperature and humidity environment is discarded; Condition d) When any device sends a fault signal and obtains a device fault signal, all data at the moment the device fault signal is obtained are discarded.

[0060] Through the above data screening process, invalid data that does not meet the screening conditions are eliminated, and valid data that meets the screening conditions are retained.

[0061] In some embodiments, after the data screening process, it is necessary to determine whether the filtered measured data set and the reference data set meet the data volume requirements. If so, the process proceeds to step S12; if not, the process continues to collect data using the cabin-mounted laser radar and sensor group.

[0062] In some embodiments, the data volume requirements may include, for example, a) the data collected by the sensor group is divided into intervals centered on integer multiples of 0.5 m / s, with an interval width of 0.5 m / s; b) each wind speed interval between 4 m / s and 12 m / s contains at least 3 pairs of valid data; c) the data volume from 12 m / s to 16 m / s is at least 1 hour; d) the data volume from 4 m / s to 16 m / s should be at least 180 hours.

[0063] Step S12: Calculate the slope and correlation coefficient based on the measured data set and the reference data set.

[0064] In some embodiments, step S12 calculates the slope and correlation coefficient based on the measured data set and the reference data set, including: performing bivariate linear regression processing based on the measured wind speed value and the wind speed measurement value, and calculating the first slope and the first correlation coefficient; performing bivariate linear regression processing based on the measured wind direction value and the wind direction measurement value, and calculating the second slope and the second correlation coefficient.

[0065] In some embodiments, slopes and correlation coefficients are calculated for the filtered measured data set and the reference data set (ie, valid data).

[0066] In some embodiments, the slope and correlation coefficient satisfy the requirements, which means that the slope is within a preset slope range and the correlation coefficient is greater than a preset threshold. The correlation coefficient can be represented by the symbol R 2 express.

[0067] In some embodiments, the two-variable linear regression equation is, for example, y=kx+b, where y represents the measured value, k represents the slope, x represents the measured value, and b represents the intercept. When performing a two-variable linear regression on wind speed, y represents the measured wind speed value, k represents the first slope, x represents the measured wind speed value, and b represents the first intercept. When performing a two-variable linear regression on wind direction, y represents the measured wind direction value, k represents the second slope, x represents the measured wind direction value, and b represents the second intercept. A first correlation coefficient is calculated based on the measured wind speed value and the measured wind speed value, and a second correlation coefficient is calculated based on the measured wind direction value and the measured wind direction value.

[0068] In some embodiments, the first preset slope range corresponding to the first slope is 2% (0.98-1.02), where 2% (0.98-1.02) is equal to (1.96%-2.04%), and the first preset threshold corresponding to the first correlation coefficient is 0.97. The second preset slope range corresponding to the second slope is 2% (0.98-1.02), and the second preset threshold corresponding to the second correlation coefficient is 0.97.

[0069] In some embodiments, the intercept also needs to meet the requirements, where the intercept meeting the requirements means that the intercept is less than a preset intercept threshold, wherein the first preset intercept threshold corresponding to the first intercept is 0.1 m / s, and the second preset intercept threshold corresponding to the second intercept is 5°.

[0070] Step S13: If the slope and the correlation coefficient meet the requirements, the uncertainty is calculated based on the measured data set and the reference data set, and the cabin-type laser radar is calibrated based on the uncertainty.

[0071] In step S13, the uncertainty can be calculated by comparing the average deviation of each interval of the wind speed measurement value obtained by the nacelle-type laser radar and the wind speed measurement value obtained by the wind speed sensor on the wind measurement tower with the standard uncertainty.

[0072] In some embodiments, the uncertainty includes wind speed uncertainty, and a method for obtaining the wind speed uncertainty includes: obtaining a standard uncertainty, a first wind speed average value of a wind speed measurement value, and a second wind speed average value of a wind speed measurement value, and calculating the wind speed uncertainty based on the standard uncertainty, the first wind speed average value, and the second wind speed average value. Specifically, the wind speed uncertainty is obtained by Calculated, where represents the first average wind speed of each interval, which is calculated based on the actual wind speed values ​​of each interval obtained by the nacelle-type laser radar. Indicates the second average wind speed of each interval, which is calculated based on the wind speed measurement values ​​of each interval obtained by the wind speed sensor on the wind tower. 2 ver，i Indicates the standard uncertainty of each interval. The units of wind speed related parameters are consistent.

[0073] Taking valid data for wind speeds ranging from 4 m / s to 16 m / s as an example, the wind speed measurements obtained by the nacelle-mounted lidar and the wind speed sensors on the wind tower are plotted, along with bivariate linear regression and uncertainty.

[0074] Figure 4 A schematic diagram of the distribution of two groups of wind speeds provided in an embodiment of the present disclosure; Figure 5A schematic diagram comparing two sets of wind speed intervals provided in an embodiment of the present disclosure. A scatter plot is drawn based on the wind speed measured values ​​from the filtered nacelle-type lidar and the measured values ​​obtained by the wind speed sensor on the wind tower, and a bivariate linear regression process is performed. At the same time, the relative relationship between the deviation between the measured values ​​of the nacelle-type lidar and the measured values ​​obtained by the wind speed sensor on the wind tower and the measured values ​​is plotted, thereby obtaining Figure 4 The relationship between the first wind speed average value obtained by the nacelle-type laser radar and the second wind speed average value obtained by the wind speed sensor on the wind tower is plotted to obtain Figure 5 The schematic diagram shown.

[0075] in Figure 4 The horizontal axis is the wind speed measurement value obtained by the wind speed sensor on the wind tower, in m / s. The vertical axis on the left is the wind speed measurement value obtained by the cabin-mounted lidar (i.e., RSD wind speed), in m / s. The vertical axis on the right is the degree of deviation (i.e., comparative deviation), in %. The blue represents the distribution of wind speed measurement values ​​and wind speed measurement values, and the red represents the deviation of wind speed measurement values ​​and wind speed measurement values. The bivariate linear regression equation based on wind speed measurement values ​​and wind speed measurement values ​​is y = 0.99849x + 0.048775, with a correlation coefficient R 2 The average deviation is 0.04m / s, 0.52%, and the standard deviation is 0.21m / s, 2.51%. Figure 5 The horizontal axis is the second wind speed average (unit: m / s), and the vertical axis on the left is the first wind speed average (unit: m / s). The vertical axis on the right is the degree of deviation (i.e., contrast deviation), in %. The black curve is the range of standard uncertainty (i.e., uncertainty after deducting the average deviation), the red curve is the degree of deviation between the second wind speed average and the first wind speed average, and the blue curve is the bivariate linear regression equation obtained based on the second wind speed average and the first wind speed average. The bivariate linear regression equation is y=1.0002x+0.033565, and the correlation coefficient R 2 is 0.99967.

[0076] For wind speed uncertainty, the main considerations are the uncertainty of the main wind speed signal: the calibration uncertainty, classification uncertainty, and installation uncertainty of the main anemometer; the average deviation of the measurements of the nacelle-mounted lidar and the reference equipment; and the standard uncertainty of the nacelle-mounted lidar measurement, which is calculated as the standard deviation of the measurement divided by the square root of the number of data records in each interval (e.g., the Class A uncertainty of the calibration test).

[0077] In some embodiments, the uncertainty also includes the uncertainty of the nacelle-mounted lidar due to installation effects during calibration testing and the uncertainty of the nacelle-mounted lidar due to non-uniform flow within the measurement volume. These different uncertainties are independent of each other and should be orthogonally added to obtain the combined uncertainty, thereby achieving calibration of the nacelle-mounted lidar.

[0078] In some embodiments, when calibrating a nacelle-type lidar based on uncertainty, the uncertainty is compared with the standard uncertainty. When the uncertainty exceeds the range of the standard uncertainty, for example, when the results measured by the nacelle-type lidar are well correlated with the results measured by the sensor group arranged on the wind tower, the uncertainty range may be exceeded due to the coefficient problem of the wind speed correction of the radar manufacturer. At this time, the wind speed measurement results of the nacelle-type lidar can be corrected based on the calibration test results to eliminate the deviation as much as possible. After the correction, the uncertainty can be reduced and continued to be used. If the average deviation within the interval exceeds the above expression (for example, within at least one interval), correction is performed. The wind speed correction formula can be calculated based on the linear regression formula obtained by fitting.

[0079] In the calibration method of the nacelle-type lidar in the embodiment of the present disclosure, a wind measurement tower and a placement tower are set at a preset distance apart in an area with flat terrain, a sensor group is arranged on the wind measurement tower, and a test platform, an engine and a nacelle-type lidar are arranged on the placement tower. The nacelle-type lidar is set on the test platform, the engine is located under the test platform and the engine bracket is in contact with the test platform. The calibration method includes: when the engine is running, using the nacelle-type lidar and the sensor group to respectively collect data to obtain a measured data set and a reference data set; calculating the slope and correlation coefficient based on the measured data set and the reference data set; if the slope and the correlation coefficient meet the requirements, calculating the uncertainty based on the measured data set and the reference data set, and calibrating the nacelle-type lidar based on the uncertainty. In this case, the wind tower and the placement tower are set in a flat terrain area and separated by a preset distance. The sensor group and the nacelle-type laser radar are respectively installed on the wind tower and the placement tower to avoid the influence of obstacles on the sensor group or the nacelle-type laser radar. When the engine is running, the test platform vibrates, so that the environment of the nacelle-type laser radar is closer to the operating state of the wind turbine, thereby improving the accuracy of the data collected by the nacelle-type laser radar and providing a more accurate data basis for subsequent calibration. Then, the slope, correlation coefficient and uncertainty are calculated by comprehensively utilizing the measured data set and the reference data set to calibrate the nacelle-type laser radar, further improving the accuracy of the calibration of the nacelle-type laser radar. In addition, the calibration method disclosed in the present invention utilizes the erected wind tower and the placement tower, the meteorological equipment of the wind tower that has been installed and calibrated and is within the validity period, and the data collected by the nacelle-type laser radar, and the data are screened and compared in performance to obtain uncertainty, thereby calibrating the nacelle-type laser radar, which can be used to standardize how to calibrate the nacelle-type laser radar, which is of great significance for the future use of the nacelle-type laser radar.

[0080] The following are system embodiments of the present disclosure, which can be used to implement the method embodiments of the present disclosure. For details not disclosed in the system embodiments of the present disclosure, please refer to the method embodiments of the present disclosure.

[0081] See Figure 6 , Figure 6 This is a block diagram of a calibration system for a cabin-mounted laser radar provided by an embodiment of the present disclosure. The cabin-mounted laser radar calibration system 10 includes a reference data acquisition module 11, a measured data acquisition module 12, and a processing module 13, wherein:

[0082] The reference data acquisition module 11 includes a sensor group arranged on a wind tower, and the reference data acquisition module 11 is used to collect data using the sensor group to obtain a reference data set;

[0083] The measured data acquisition module 12 includes a test platform, an engine, and a nacelle-type laser radar arranged on the placement tower. The nacelle-type laser radar is set on the test platform, and the engine is located under the test platform with the engine bracket in contact with the test platform. The measured data acquisition module 12 is used to collect data using the nacelle-type laser radar to obtain a measured data set. The wind measurement tower and the placement tower are separated by a preset distance and the terrain in the area is flat.

[0084] The processing module 13 is used to obtain a measured data set and a reference data set when the engine is running; calculate the slope and correlation coefficient based on the measured data set and the reference data set; if the slope and the correlation coefficient meet the requirements, calculate the uncertainty based on the measured data set and the reference data set, and calibrate the cabin-mounted lidar based on the uncertainty.

[0085] Optionally, the processing module 13 is further configured to: before calculating the correlation coefficient based on the measured dataset and the reference dataset, perform data screening processing on the measured dataset and the reference dataset respectively in combination with the wind direction requirement, the temperature threshold, and the humidity threshold.

[0086] Optionally, the measured data set includes measured wind speed values ​​and measured wind direction values, and the reference data set includes measured wind speed values ​​and measured wind direction values. The processing module 13 is specifically used to: perform bivariate linear regression processing based on the measured wind speed values ​​and the measured wind speed values, and calculate the first slope and the first correlation coefficient; perform bivariate linear regression processing based on the measured wind direction values ​​and the measured wind direction values, and calculate the second slope and the second correlation coefficient.

[0087] Optionally, the uncertainty includes wind speed uncertainty, and the processing module 13 is specifically used to: obtain the standard uncertainty, the first wind speed average value of the wind speed measurement value, and the second wind speed average value of the wind speed measurement value, and calculate the wind speed uncertainty based on the standard uncertainty, the first wind speed average value, and the second wind speed average value.

[0088] Optionally, the slope and the correlation coefficient meeting the requirements means that the slope is within a preset slope range and the correlation coefficient is greater than a preset threshold.

[0089] It should be noted that the above explanation of the embodiment of the calibration method of the cabin-type laser radar is also applicable to the calibration system of the cabin-type laser radar of this embodiment, and will not be repeated here.

[0090] In the calibration system of the nacelle-type lidar of the embodiment of the present disclosure, the reference data acquisition module includes a sensor group arranged on a wind measurement tower, and the reference data acquisition module uses the sensor group to collect data to obtain a reference data set; the measured data acquisition module includes a test platform, an engine and a nacelle-type lidar arranged on a placement tower, the nacelle-type lidar is set on the test platform, the engine is located under the test platform and the engine bracket is in contact with the test platform, the measured data acquisition module uses the nacelle-type lidar to collect data to obtain a measured data set, the wind measurement tower and the placement tower are separated by a preset distance and the terrain in the area is flat; the processing module obtains the measured data set and the reference data set when the engine is running; calculates the slope and the correlation coefficient based on the measured data set and the reference data set; if the slope and the correlation coefficient meet the requirements, calculates the uncertainty based on the measured data set and the reference data set, and calibrates the nacelle-type lidar based on the uncertainty. In this case, the wind tower and the placement tower are set in a flat terrain area and separated by a preset distance. The wind tower and the placement tower are respectively equipped with a sensor group and a nacelle-type laser radar to avoid the influence of obstacles on the sensor group or the nacelle-type laser radar. When the engine is running, the test platform vibrates, so that the environment of the nacelle-type laser radar is closer to the operating state of the wind turbine, thereby improving the accuracy of the data collected by the nacelle-type laser radar and providing a more accurate data basis for subsequent calibration. Then, the slope, correlation coefficient and uncertainty are calculated by comprehensively utilizing the measured data set and the reference data set to calibrate the nacelle-type laser radar, further improving the accuracy of the calibration of the nacelle-type laser radar. In addition, the calibration system disclosed herein utilizes the erected wind tower and the placement tower, the meteorological equipment of the wind tower that has been installed and calibrated and is within the validity period, and the data collected by the nacelle-type laser radar, and the data is screened and compared for performance to obtain uncertainty, thereby calibrating the nacelle-type laser radar, which can be used to standardize how to calibrate the nacelle-type laser radar, which is of great significance for the future use of the nacelle-type laser radar.

[0091] According to an embodiment of the present disclosure, the present disclosure also provides a calibration device for a cabin-type laser radar, a readable storage medium, and a computer program product.

[0092] Figure 7It is a block diagram of a calibration device for a cabin-type laser radar used to implement the calibration method of the cabin-type laser radar of an embodiment of the present disclosure. The calibration device for the cabin-type laser radar is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The calibration device for the cabin-type laser radar can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable cabin-type laser radar calibration devices and other similar computing devices. The components, connections and relationships of the components, and functions of the components shown in this disclosure are merely examples and are not intended to limit the implementation of the present disclosure described and / or required in this disclosure.

[0093] like Figure 7 As shown, the calibration device 20 for the nacelle-type laser radar includes a computing unit 21, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 22 or a computer program loaded from a storage unit 28 into a random access memory (RAM) 23. Various programs and data required for the operation of the calibration device 20 for the nacelle-type laser radar can also be stored in the RAM 23. The computing unit 21, the ROM 22, and the RAM 23 are connected to each other via a bus 24. An input / output (I / O) interface 25 is also connected to the bus 24.

[0094] Multiple components in the nacelle-type lidar calibration device 20 are connected to the I / O interface 25, including: an input unit 26, such as a keyboard, mouse, etc.; an output unit 27, such as various types of displays, speakers, etc.; a storage unit 28, such as a magnetic disk, optical disk, etc., which is communicatively connected to the computing unit 21; and a communication unit 29, such as a network card, modem, wireless communication transceiver, etc. The communication unit 29 allows the nacelle-type lidar calibration device 20 to exchange information / data with other nacelle-type lidar calibration devices via a computer network such as the Internet and / or various telecommunication networks.

[0095] The computing unit 21 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 21 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 21 performs the various methods and processes described above, such as executing the calibration method for the nacelle-mounted lidar. For example, in some embodiments, the calibration method for the nacelle-mounted lidar can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 28. In some embodiments, part or all of the computer program can be loaded and / or installed into the calibration device 20 for the nacelle-mounted lidar via the ROM 22 and / or the communication unit 29. When the computer program is loaded into the RAM 23 and executed by the computing unit 21, one or more steps of the calibration method for the nacelle-mounted lidar described above can be performed. Alternatively, in other embodiments, the computing unit 21 may be configured to execute the calibration method of the nacelle-type lidar in any other appropriate manner (for example, by means of firmware).

[0096] Various embodiments of the systems and techniques described above in the present disclosure can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), calibration devices for programmable logic cabin-mounted laser radars (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0098] In the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, an apparatus, or a calibration device for a nacelle-type laser radar. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or calibration device for a nacelle-type laser radar, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage nacelle-type laser radar calibration device, a magnetic storage nacelle-type laser radar calibration device, or any suitable combination of the foregoing.

[0099] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0100] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0101] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0102] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This disclosure is not limited here.

[0103] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A calibration method for a cabin-type laser radar, characterized in that: A wind tower and a placement tower are set up in an area with flat terrain at a preset distance from each other, a sensor group is arranged on the wind tower, and a test platform, an engine, and a nacelle-type laser radar are arranged on the placement tower. The nacelle-type laser radar is set on the test platform, the engine is located under the test platform, and the engine bracket is in contact with the test platform. The calibration method includes: When the engine is running, using the nacelle-type laser radar and the sensor group to collect data respectively to obtain a measured data set and a reference data set; Calculating a slope and a correlation coefficient based on the measured data set and the reference data set; If the slope and the correlation coefficient meet the requirements, calculating the uncertainty based on the measured data set and the reference data set, and calibrating the cabin-type laser radar based on the uncertainty; The measured data set includes measured wind speed values ​​and measured wind direction values, the reference data set includes measured wind speed values ​​and measured wind direction values, and calculating the slope and the correlation coefficient based on the measured data set and the reference data set includes: Performing a bivariate linear regression process based on the wind speed actual value and the wind speed measurement value to calculate a first slope and a first correlation coefficient; performing a bivariate linear regression process based on the wind direction actual value and the wind direction measurement value to calculate a second slope and a second correlation coefficient; The uncertainty includes wind speed uncertainty, and a method for obtaining the wind speed uncertainty includes: A standard uncertainty, a first wind speed average value of the wind speed measurement value, and a second wind speed average value of the wind speed measurement value are obtained, and the wind speed uncertainty is calculated based on the standard uncertainty, the first wind speed average value, and the second wind speed average value.

2. The calibration method of the cabin-type laser radar according to claim 1, characterized in that: Before calculating the correlation coefficient based on the measured data set and the reference data set, the method further includes: The measured data set and the reference data set are respectively subjected to data screening processing in combination with the wind direction requirement, the temperature threshold, and the humidity threshold.

3. The calibration method of the cabin-type laser radar according to claim 1, characterized in that: Also includes: The test platform is arranged on the top of the placement tower, and the sensor group is arranged at the same height as the position of the wind measurement tower and the cabin-type laser radar.

4. The calibration method of a cabin-type laser radar according to claim 1, wherein: The slope and correlation coefficient meeting the requirements means that the slope is within a preset slope range and the correlation coefficient is greater than a preset threshold.

5. A calibration system for a cabin-type laser radar, characterized in that: include: The reference data acquisition module includes a sensor group arranged on a wind tower, and the reference data acquisition module is used to collect data using the sensor group to obtain a reference data set; The measured data acquisition module includes a test platform, an engine, and a nacelle-type laser radar arranged on a placement tower. The nacelle-type laser radar is arranged on the test platform. The engine is located under the test platform, and the engine bracket is in contact with the test platform. The measured data acquisition module is used to collect data using the nacelle-type laser radar to obtain a measured data set. The wind measurement tower and the placement tower are separated by a preset distance and the terrain in the area where they are located is flat. A processing module, configured to obtain the measured data set and the reference data set when the engine is running; Calculating a slope and a correlation coefficient based on the measured data set and the reference data set; if the slope and the correlation coefficient meet the requirements, calculating an uncertainty based on the measured data set and the reference data set, and calibrating the cabin-mounted laser radar based on the uncertainty; The measured data set includes measured wind speed values ​​and measured wind direction values, the reference data set includes measured wind speed values ​​and measured wind direction values, and the processing module is specifically configured to: Performing a bivariate linear regression process based on the wind speed actual value and the wind speed measurement value to calculate a first slope and a first correlation coefficient; performing a bivariate linear regression process based on the wind direction actual value and the wind direction measurement value to calculate a second slope and a second correlation coefficient; The uncertainty includes wind speed uncertainty, and a method for obtaining the wind speed uncertainty includes: A standard uncertainty, a first wind speed average value of the wind speed measurement value, and a second wind speed average value of the wind speed measurement value are obtained, and the wind speed uncertainty is calculated based on the standard uncertainty, the first wind speed average value, and the second wind speed average value.

6. The calibration system for a cabin-mounted laser radar according to claim 5, wherein: The processing module is further configured to: Before calculating the correlation coefficient based on the measured dataset and the reference dataset, data screening processing is performed on the measured dataset and the reference dataset respectively in combination with the wind direction requirement, the temperature threshold, and the humidity threshold.

7. A calibration device for a cabin-type laser radar, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the calibration method of the cabin-type laser radar according to any one of claims 1 to 4.

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