Optical fiber sensing load measurement system and use method thereof

Abstract

The present invention discloses a fiber optic sensing load measurement system and a method for using the same. The system includes a fiber optic signal acquisition module, an optical data processing module, a communication module and a fiber optic sensor group module. The method for using the system includes the following steps: S1, obtaining the basic parameters of each blade of the wind turbine; S2, obtaining the dynamic data of each blade of the wind turbine; S3, dynamically calibrating the load coefficient of each blade according to the load calibration method. The present invention calculates the strain of the measured point through the processing of optical data, thereby calculating the load coefficient, and can write the coefficient into the variable pitch control system to collect the load value in real time. Check the correctness and consistency of the load measurement of the three blades, and then combine the calibration method with the calibration process to increase the calibration accuracy and consistency of the three blades, and measure the real-time load of the blades with higher accuracy. The method can effectively reduce manpower and material resources, and detect the blade load stably for a long time.

Description

Technical Field

[0001] The present invention relates to the technical field of load measurement, and in particular to an optical fiber sensing load measurement system and a use method thereof. Background Art

[0002] Wind energy is a very abundant renewable energy source in nature, and its commercial value is extremely high. Blades are collectors of wind turbines that convert wind energy into electrical energy. Affected by factors such as wind shear, wind turbulence, tower shadow effect, and yaw error, unbalanced loads will be generated on the impeller of the wind turbine, and the larger the diameter of the wind rotor, the more obvious it is. These unbalanced loads will cause great fatigue loads on key components of the wind turbine, such as the hub, main shaft, yaw bearing, and tower. Theoretically, independent variable pitch control can effectively reduce the unbalanced load on the impeller. Reducing the load on key components of the wind turbine unit to reduce the weight of these key components, improve the reliability of the equipment, and extend its service life is an effective way to reduce the cost of wind turbines. The load calculation is a test of the fatigue performance of the blades.

[0003] In order to feedback the bending moment distribution of the blade, it is necessary to install strain sensors on the blade to monitor the load changes of the blade in real time. Resistance strain gauges are usually used for strain measurement, but they are highly sensitive to the environment and are easily interfered by temperature, electromagnetic waves, etc. However, fiber optic sensors are passive sensors that do not require an external power supply and can be easily arranged on the blades. Moreover, optical fiber is a non-metallic product, and electromagnetic interference such as lightning strikes has no effect on optical fiber transmission. The system not only has high reliability, but also has a long service life. Therefore, the present invention provides a fiber optic sensing load measurement system and a method of use thereof for load monitoring of the root portion of a blade. Summary of the invention

[0004] In view of the problems in the related art, the present invention proposes a fiber optic sensing load measurement system and a method of using the same to overcome the above technical problems existing in the existing related art.

[0005] To this end, the specific technical solution adopted by the present invention is as follows:

[0006] According to one aspect of the present invention, there is provided a fiber optic sensing load measurement system, the system comprising a fiber optic signal acquisition module, an optical data processing module, a communication module and a fiber optic sensor group module;

[0007] Wherein, the optical fiber signal acquisition module is used to collect optical signals from the optical fiber sensor;

[0008] The optical data processing module is used to convert the collected optical signals into digital signals to realize subsequent data analysis and processing;

[0009] The communication module is used to transmit and receive optical signals of the optical fiber sensor;

[0010] The optical fiber sensor group module is used to act on the wind turbine blades and measure the real-time strain of the blades through the relationship between the blade strain and the wavelength change;

[0011] The optical fiber sensor group module is composed of a plurality of strain sensors, and strain sensors are installed at the trailing edge, leading edge, windward side and leeward side of the blade root of each wind turbine blade.

[0012] Furthermore, the installation layout of the strain sensor is:

[0013] When installing strain sensors at the leading and trailing edges of the blade roots of the fan blades, stagger them by 20 to 30 degrees;

[0014] Mark 4 points on the circular section 1 to 2 meters away from the blade root flange surface, and the marked points are located at 120° on the leading edge, 210° on the leeward side, 300° on the trailing edge and 30° on the windward side when viewed from the hub.

[0015] According to another aspect of the present invention, a method for using a fiber optic sensing load measurement system is provided, the method comprising the following steps:

[0016] S1. Obtain the basic parameters of each blade of the fan;

[0017] S2, obtaining dynamic data of each blade of the wind turbine;

[0018] S3. Dynamically calibrate the load factor of each blade according to the load calibration method.

[0019] Furthermore, the basic parameters include the mass, center of mass position, cabin elevation angle and blade cone angle of the wind turbine blades.

[0020] Furthermore, the dynamic data includes dynamic real-time wavelength data, central wavelength, pitch angle and azimuth angle.

[0021] Furthermore, the method of dynamically calibrating the load coefficient of each blade according to the load calibration method includes the following steps:

[0022] S31, using blade gravity as a theoretical load, combined with the wavelength change value of the optical fiber sensor, calibrate the flapping load coefficient and the swing load coefficient;

[0023] S32, using a multiple linear regression method based on the linear superposition principle combined with a normalization method to establish a relationship between the blade wavelength change and the load;

[0024] S33, obtaining a load calibration coefficient according to the RANSAC algorithm;

[0025] S34. According to a preset load calibration method, the blade sensor load coefficient is calibrated using the blade's gravity to measure the real-time load change of each blade.

[0026] Furthermore, the calculation formulas for the flapping load and the shimmying load are as follows:

[0027]

[0028] Where M_flap represents the flapping load, M_edge represents the swing load, m represents the blade mass, g represents the acceleration of gravity, r represents the position of the blade center of gravity, C, T, P and A represent the azimuth angle, impeller inclination angle, blade pitch angle and impeller azimuth angle, respectively.

[0029] Furthermore, the relationship between the blade wavelength change and the load is as follows:

[0030]

[0031] In the formula, K 11 , K 12 , K 13 , K 14 Indicates flap load calibration factor; K 21 , K 22 , K 23 , K 24 represents the edge load calibration coefficient; Δλ1, Δλ2, Δλ3, Δλ4 are real-time wavelength data; Δλ 10 , Δλ 20 , Δλ 30 , Δλ 40 Represents the center wavelength data for each sensor.

[0032] Furthermore, obtaining the load calibration coefficient according to the RANSAC algorithm includes the following steps:

[0033] S331. First, 20,000 data samples are required, including 20,000 wavelength data, 20,000 pitch angles, and 20,000 azimuth angles, and 20,000 theoretical loads are calculated to form a load sample.

[0034] S332, using the relationship between the blade wavelength change and the load to determine and solve the flapping and shimmying load calibration model F, that is, to determine the model parameter flapping calibration coefficient and shimmying calibration coefficient K, the minimum number of data points required is 10,000, including (10,000 wavelength data, 10,000 pitch angles, 10,000 azimuth angles), and a subset consisting of 10,000 data points is called a sample of the load calibration model F;

[0035] S333. Randomly select 10,000 points from the 20,000 points of the sample, including theoretical load and wavelength data, to form a small sample S. The first load calibration model F1 is calculated by evaluating the sample. Based on the sample and the load calibration model F1, a load value sample S1 under the sample is calculated.

[0036] S334, filter the above load value samples, and filter out the values ​​of S1 that are smaller than the S samples. Re-estimate the load calibration model F based on the filtered data (theoretical load, wavelength data); then calculate the corresponding load value. If there are still values ​​in the load data that are greater than the theoretical load S, return to S333;

[0037] S335, after multiple random samplings, select samples that fully meet the set threshold S2 of the reconstructed load calibration model F, use the re-estimated load calibration model F, and output the result

[0038] The beneficial effects of the present invention are as follows: by processing optical data, the strain of the measured point is calculated, and a preset load calibration method is used to convert the strain into blade swing load and shimmy load, which are output to the wind turbine pitch control system or the main control system, so that the load coefficient can be calculated, and the coefficient can be written into the pitch control system, and the load values ​​can be collected in real time to check the correctness and consistency of the load measurement of the three blades. Then, the calibration method can be combined with the calibration process to increase the calibration accuracy and consistency of the three blades, and the real-time load of the blade can be measured with high accuracy. Moreover, this method can effectively reduce manpower and material resources, and detect the blade load stably for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 is a structural block diagram of a fiber optic sensing load measurement system according to an embodiment of the present invention;

[0041] Figure 2 is a physical diagram of the layout of a blade root grating sensor in a fiber optic sensing load measurement system according to an embodiment of the present invention;

[0042] Figure 3 is a flow chart of a method for using a fiber optic sensing load measurement system according to an embodiment of the present invention;

[0043] Figure 4is a schematic diagram of blade load in a method for using an optical fiber sensing load measurement system according to an embodiment of the present invention;

[0044] Figure 5 It is a schematic diagram of the cabin inclination angle and the blade cone angle in a method for using a fiber optic sensing load measurement system according to an embodiment of the present invention.

[0045] In the figure:

[0046] 1. Optical fiber signal acquisition module; 2. Optical data processing module; 3. Communication module; 4. Optical fiber sensor group module. DETAILED DESCRIPTION

[0047] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0048] According to an embodiment of the present invention, a fiber optic sensing load measurement system and a method for using the same are provided.

[0049] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1-Figure 2 As shown, according to one embodiment of the present invention, there is provided a fiber optic sensing load measurement system, the system comprising a fiber optic signal acquisition module 1, an optical data processing module 2, a communication module 3 and a fiber optic sensor group module 4;

[0050] The optical fiber signal acquisition module 1 is used to acquire optical signals from optical fiber sensors, and in this embodiment, mainly involves an analog-to-digital acquisition board;

[0051] The optical data processing module 2 is used to convert the collected optical signals into digital signals to realize subsequent data analysis and processing, and in this embodiment mainly involves an analog-to-digital conversion board;

[0052] The communication module 3 is used to transmit and receive optical signals of the optical fiber sensor, and in this embodiment, a polarization-maintaining optical fiber is used;

[0053] The optical fiber sensor module 4 is used to act on the wind turbine blades, and its measurement wavelength changes with the blade stress. The real-time strain of the blade is measured through the relationship between the blade strain and the wavelength change.

[0054] The optical fiber sensor group module 4 is composed of a plurality of strain sensors, and a strain sensor is installed at the trailing edge (TE), leading edge (LE), windward side (CS) and leeward side (SS) of the blade root of each wind turbine blade. The load of the blade root can be calculated by real-time monitoring of the spectrum signal of the grating strain sensor attached to the blade root through the wavelength change of the sensor.

[0055] Wherein, the installation layout of the strain sensor is:

[0056] Independent pitch control is based on the blade root load, which requires accurate sensor installation and high-standard installation technology to ensure the accuracy and precision of blade root load measurement. At the same time, affected by the blade mold seam, considering the uniform force, the leading and trailing edges of the blade roots of the wind turbine blades need to be staggered by 20 to 30 degrees; mark 4 points on the circular cross section about 1 to 2 meters away from the blade root flange surface. From the hub direction, the 4 marked points are at LE120°, SS210°, TE300° and CS30° respectively. The sensor is installed at the blade root as shown in the figure. Figure 2 shown.

[0057] The optical fiber sensing load measurement system of this embodiment calculates the strain of the measured point by processing optical data. The optical fiber load measurement system equipment includes an optical fiber sensor demodulator and 12 strain sensors.

[0058] According to another embodiment of the present invention, Figure 3-Figure 5 As shown, a method for using a fiber optic sensing load measurement system is provided, and the method for using the fiber optic sensing load measurement system comprises the following steps:

[0059] S1. Obtain the basic parameters of each blade of the fan;

[0060] The basic parameters include the mass, center of mass position, cabin pitch angle and blade cone angle of the wind turbine blade.

[0061] S2, obtaining dynamic data of each blade of the wind turbine;

[0062] The dynamic data includes optical fiber sensing data (ie, dynamic real-time wavelength data, central wavelength) and wind turbine installation parameters (ie, pitch angle and azimuth angle).

[0063] Specifically, obtaining the dynamic data of each blade of the wind turbine also includes the following steps:

[0064] At a low wind speed of 5m / s, the blades are rotated to an azimuth angle of 180° and pitch angles of 0°, 45° and 90°. When the wind turbine blades are completely stationary, the real-time sensor measurement wavelength data of the blades is recorded. The three blades are carried out in turn, and the wavelength data of the optical fiber sensor on each blade is read separately. When reading the data, confirm that the pitch angle error is controlled within the range of ±1°, and then read the wavelength data.

[0065] At a low wind speed of 5m / s, rotate the blades to an azimuth angle of 90° and keep them stationary. At the same time, perform independent pitch change of the wind turbine blades, with the pitch angle ranging from 0 to 85°. After the blades are stable, change the pitch from 85° to 0°. In the above manner, the cycle is executed 10 times. When the wind turbine blades are completely stationary, the real-time sensor measurement wavelength data, pitch angle data, and azimuth angle data of the blades are recorded. The three blades are performed in turn, and the wavelength data of the optical fiber sensor on each blade is read separately. When reading the data, confirm that the pitch angle error is controlled within the range of ±1°, and then read the wavelength data. Ensure that the blades are completely stationary and the turbulence intensity of the wind speed is low during the entire process of blade pitch change.

[0066] At a low wind speed of 5m / s, rotate the blades to an azimuth angle of 270°, and perform independent pitch change of the wind turbine blades at the same time, with the pitch angle ranging from 0 to 85°. After the blades are stable, change the pitch from 85° to 0°. In the above manner, the cycle is executed 10 times. When the wind turbine blades are completely stationary, the real-time sensor measurement wavelength data, pitch angle data, and azimuth angle data of the blades are recorded. The three blades are performed in turn, and the wavelength data of the optical fiber sensor on each blade is read separately. When reading the data, confirm that the pitch angle error is controlled within the range of ±1°, and then read the wavelength data. Ensure that the blades are completely stationary and the turbulence intensity of the wind speed is low during the entire process of blade pitch change.

[0067] At a low wind speed of 5m / s, the blades operate normally, the azimuth angle changes from 0 to 360°, and the wind turbine blades are independently pitched, the pitch angle changes from 0 to 85°, and then changes from 85° to 0° after the blades are stable. In the above manner, the cycle is executed 10 times. The sensor wavelength data, pitch angle, and azimuth angle information are recorded in real time. It is ensured that there is no measurement error caused by inertia and jitter during the blade pitch change.

[0068] S3. Dynamically calibrate the load factor of each blade according to the load calibration method.

[0069] In this embodiment, the blade load is mainly in the X and Y directions of the blade root, also known as flapping load and shimmy load. Figure 4 As shown (in the figure, a represents flapping and b represents shimmy), and in this embodiment, only the loads in these two directions are studied. Figure 5 The blade cone angle α and the blade pitch angle β according to an embodiment of the present invention are shown.

[0070] The method of dynamically calibrating the load coefficient of each blade according to the load calibration method comprises the following steps:

[0071] S31. At a low wind speed of 5 m / s, the blade gravity is used as the theoretical load, combined with the wavelength change value of the optical fiber sensor, to calibrate the flapping load coefficient and the swing load coefficient;

[0072] Specifically, the calculation formulas for the swing load and the shimmy load are as follows:

[0073]

[0074] Where M_flap represents the flapping load, M_edge represents the swing load, m represents the blade mass, g represents the acceleration of gravity, r represents the position of the blade center of gravity, C, T, P and A represent the azimuth angle, impeller inclination angle, blade pitch angle and impeller azimuth angle, respectively.

[0075] S32, using a multiple linear regression method based on the linear superposition principle combined with a normalization method to establish a relationship between the blade wavelength change and the load;

[0076] Specifically, the relationship between the blade wavelength change and the load is as follows:

[0077]

[0078] In the formula, K 11 , K 12 , K 13 , K 14 Indicates flap load calibration factor; K 21 , K 22 , K 23 , K 24 represents the edge load calibration coefficient; Δλ1, Δλ2, Δλ3, Δλ4 are real-time wavelength data; Δλ 10 , Δλ 20 , Δλ 30 , Δλ 40 Represents the center wavelength data for each sensor.

[0079] S33, obtaining a load calibration coefficient according to a RANSAC (random sampling consensus) algorithm;

[0080] Specifically, RANSAC is an iterative method for fitting a parametric model to data with outliers. The algorithm performs the following steps:

[0081] S331. First, 20,000 data samples are required, including 20,000 wavelength data, 20,000 pitch angles, and 20,000 azimuth angles, and 20,000 theoretical loads are calculated to form a load sample.

[0082] S332, using the relationship between the blade wavelength change and the load to determine and solve the flapping and shimmying load calibration model F, that is, to determine the model parameter flapping calibration coefficient and shimmying calibration coefficient K, the minimum number of data points required is 10,000, including (10,000 wavelength data, 10,000 pitch angles, 10,000 azimuth angles), and a subset consisting of 10,000 data points is called a sample of the load calibration model F;

[0083] S333. Randomly select 10,000 points from the 20,000 points of the sample, including theoretical load and wavelength data, to form a small sample S. The first load calibration model F1 is calculated by evaluating the sample. Based on the sample and the load calibration model F1, a load value sample S1 under the sample is calculated.

[0084] S334, filter the above load value samples, and filter out the values ​​of S1 that are smaller than the S samples. Re-estimate the load calibration model F based on the filtered data (theoretical load, wavelength data); then calculate the corresponding load value. If there are still values ​​in the load data that are greater than the theoretical load S, return to S333;

[0085] S335. After multiple random samplings, select samples that fully meet the set threshold S2 of the reconstructed load calibration model F, use the re-estimated load calibration model F, and output the results.

[0086] Sampling times The sample consists of a subset uniformly randomly selected from the measurement data set. The number of data points n contained in each sample is the minimum number of data points required to determine the model parameters. For example, a straight line requires at least two data points to be determined, that is, n=2; a circle requires at least three data points, that is, n=3.

[0087] RANSAC is a probabilistic algorithm. In order to ensure a better probability of finding the true set of inliers, sufficient number of experiments must be performed. The following is the calculation process of the number of experiments:

[0088] Assuming that each selected measurement point is independent of each other, and the probability of each measurement point being an interior point is w, the load calibration factor is the overall probability of success after multiple tests;

[0089] Then in a certain experiment, the probability that n random samples are all interior points is w n (Here n represents the minimum number of data required to fit the model);

[0090] Therefore, after p trials, the probability of failure is:

[0091] 1-p=(1-w n ) k

[0092] The minimum required experimental tests are:

[0093]

[0094] In fact, this k value is regarded as the upper limit of selecting non-repeated points, because this result assumes that the n points are selected independently, that is, after a point is selected, it may be repeatedly selected by subsequent iterations. Data points are usually selected sequentially.

[0095] For the final estimation, the model estimate M is obtained from the interior points, and the chi-square test is applied to M to re-divide the interior points and exterior points; this process is continued until the interior point set converges.

[0096] S34. According to a preset load calibration method, the blade sensor load coefficient is calibrated using the blade's gravity to measure the real-time load change of each blade.

[0097] In summary, with the help of the above technical solution of the present invention, the strain of the measured point is calculated through the processing of optical data, and the preset load calibration method is used to convert the strain into the blade swing load and the swing load, and output it to the wind turbine pitch control system or the main control system, so that the load coefficient can be calculated, and the coefficient can be written into the pitch control system, and the load value can be collected in real time to check the correctness and consistency of the load measurement of the three blades. Then, the calibration method can be combined with the calibration process to increase the calibration accuracy and consistency of the three blades, and the real-time load of the blade can be measured with high accuracy. And this method can effectively reduce manpower and material resources, and detect the blade load stably for a long time.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A fiber optic sensing load measurement system, characterized in that: The system comprises an optical fiber signal acquisition module (1), an optical data processing module (2), a communication module (3) and an optical fiber sensor group module (4); Wherein, the optical fiber signal acquisition module (1) is used to acquire optical signals from the optical fiber sensor; The optical data processing module (2) is used to convert the collected optical signals into digital signals to realize subsequent data analysis and processing; The communication module (3) is used to transmit and receive optical signals of the optical fiber sensor; The optical fiber sensor group module (4) is used to act on the fan blades and measure the real-time strain of the blades through the relationship between the blade strain and the wavelength change; The optical fiber sensor group module (4) is composed of a plurality of optical fiber strain sensors, and strain sensors are installed at the trailing edge, leading edge, windward side and leeward side of the blade root of each wind turbine blade; The method for using the optical fiber sensing load measurement system comprises the following steps: S1. Obtain the basic parameters of each blade of the fan; S2, obtaining dynamic data of each blade of the wind turbine; S3. Dynamically calibrate the load factor of each blade according to the load calibration method; According to the load calibration method, dynamically calibrating the load factor of each blade includes the following steps: S31, using blade gravity as a theoretical load, combined with the wavelength change value of the optical fiber sensor, calibrate the flapping load coefficient and the swing load coefficient; S32, using a multiple linear regression method based on the linear superposition principle combined with a normalization method to establish a relationship between the blade wavelength change and the load; S33, obtaining a load calibration coefficient according to the RANSAC algorithm; S34, according to a preset load calibration method, using the gravity of the blade to calibrate the sensor load coefficient of the blade, and measuring the real-time load change of each blade; The method of obtaining the load calibration coefficient according to the RANSAC algorithm includes the following steps: S331, obtaining a preset number of data samples, and calculating a theoretical load to form a load sample, wherein the data sample includes wavelength data, pitch angle and azimuth angle; S332, using the relationship between the blade wavelength change and the load to determine and solve the flapping and shimmying load calibration model F, that is, determining the model parameter flapping calibration coefficient and shimmying calibration coefficient K, and calling a subset consisting of a number of data points a sample of the load calibration model F; S333, randomly extracting a number of points from the sample points, including theoretical load and wavelength data, to form a small sample S, evaluating and calculating the first load calibration model F1 by the sample, and calculating the load value sample S1 under the sample according to the sample and the load calibration model F1; S334, filter the load value samples, filter out the values ​​of S1 that are smaller than the S samples, and re-estimate the load calibration model F based on the filtered data; then calculate the corresponding load value, if there is still a value greater than the theoretical load S in the load data, return to S333; S335, after multiple random samplings, select samples that fully meet the set threshold S2 of the reconstructed load calibration model F, use the re-estimated load calibration model F, and output the result; The method further includes performing a preset number of experiments on the RANSAC algorithm before obtaining the load calibration coefficient according to the RANSAC algorithm to ensure the best probability of finding the true internal point set; The preset number of experiments includes: Analyze the possibility that all data samples are internal points, and calculate the experimental failure probability of the RANSAC algorithm based on the possibility analysis results; Generate the minimum number of experiments for the RANSAC algorithm based on the probability of experimental failure.

2. The optical fiber sensing load measurement system according to claim 1, characterized in that: The installation layout of the strain sensor is: When installing strain sensors at the leading and trailing edges of the blade roots of the fan blades, stagger them by 20 to 30 degrees; Mark 4 points on the circular section 1 to 2 meters away from the blade root flange surface, and the marked points are located at 120° on the leading edge, 210° on the leeward side, 300° on the trailing edge and 30° on the windward side when viewed from the hub.

3. The optical fiber sensing load measurement system according to claim 2, characterized in that: The basic parameters include the mass, center of mass position, cabin elevation angle and blade cone angle of the wind turbine blade.

4. The optical fiber sensing load measurement system according to claim 3, characterized in that: The dynamic data includes dynamic real-time wavelength data, central wavelength, pitch angle and azimuth angle.

5. The optical fiber sensing load measurement system according to claim 4, characterized in that: The calculation formulas for the flapping load and the shimmying load are as follows: Where M_flap represents the flapping load, M_edge represents the swing load, m represents the blade mass, g represents the acceleration of gravity, r represents the position of the blade center of gravity, C, T, P and A represent the azimuth angle, impeller inclination angle, blade pitch angle and impeller azimuth angle, respectively.

6. The optical fiber sensing load measurement system according to claim 5, characterized in that: The relationship between the blade wavelength change and the load is as follows: In the formula, K 11 , K 12 , K 13 , K 14 Indicates flap load calibration factor; K 21 , K 22 , K 23 , K 24 represents the edge load calibration coefficient; Δλ1, Δλ2, Δλ3, Δλ4 are real-time wavelength data; Δλ 10 , Δλ 20 , Δλ 30 , Δλ 40 Represents the center wavelength data for each sensor.

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