Wind power blade strain built-in sensing system based on distributed fiber grating array

Abstract

The invention discloses a wind power blade strain built-in sensing system based on a distributed fiber bragg grating array, and relates to the technical field of wind power generator blade detection, and the system comprises the distributed fiber bragg grating array which is used for capturing a strain signal in a blade; the packaging module is used for fixing and protecting the distributed grating fiber array so as to ensure that a strain signal is accurately captured when the blade is stressed and deformed; the optical fiber signal processor is used for receiving and processing the strain signals transmitted by the optical fiber grating array; the data analysis unit is used for analyzing the processed strain signal by using a multi-modal data fusion algorithm and calculating strain information of the wind power blade; and the wireless transmission module is used for wirelessly transmitting the strain signal to a monitoring center. The method can accurately detect the internal strain change of the fan blade, analyzes the internal health state of the blade, adapts to complex and changeable environmental conditions, and meets the requirements of the modern wind power generation technology.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine blade detection, and more specifically, to a built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array. Background Art

[0002] With the increasing demand for renewable energy, wind power generation, as a clean energy source, has been widely developed and promoted in terms of technology and application; wind turbine blades are one of the core components of wind turbines, and their performance and safe operation have a decisive impact on the efficiency and stability of the entire wind turbine generator set.

[0003] During the operation of a wind turbine, the wind turbine blades are subjected to complex mechanical loads, including wind pressure, gravity, and centrifugal force, etc. These loads will cause the wind turbine blades to generate strain, which in turn affects their structural integrity and service life; long-term or excessive strain may lead to blade damage or fatigue, and may even cause faults in the entire wind turbine generator set. Therefore, real-time and accurate monitoring of the strain state of wind turbine blades is crucial for ensuring the efficient and safe operation of wind turbine generator sets.

[0004] Existing wind turbine blade strain monitoring technologies mainly include strain gauges, vibration sensors, etc. However, these technologies have certain limitations. For example, strain gauges are relatively complex in installation and maintenance, while vibration sensors may not be able to provide sufficient strain details; in addition, these traditional sensors are usually sensitive to environmental conditions, such as temperature and humidity changes may affect their measurement accuracy; at the same time, currently common sensors are all arranged on the surface of the wind turbine blades, unable to effectively monitor early faults inside the wind turbine blades and are easily affected by the environment, with short service life and insufficient reliability; especially in the offshore wind farm environment, the sensors face more severe working conditions, such as high humidity, high salt mist, and extreme temperature changes, which further reduce the service life and measurement reliability of traditional sensors.

[0005] In addition, with the increase in the structural complexity and size of wind turbine blades, single-point measurement can no longer meet the comprehensive monitoring requirements. Existing technologies are difficult to achieve synchronous monitoring of the strain states at different positions of the blades, lack a comprehensive understanding of the overall strain distribution of the blades, resulting in a low early fault identification rate and a late warning. At the same time, existing data processing methods often ignore the dynamic impact of environmental factors on the measurement results, lack effective noise processing and multi-source data fusion mechanisms, and reduce the accuracy and reliability of strain monitoring.

[0006] For the problems in the related technologies, no effective solutions have been proposed yet. Summary of the Invention

[0007] In view of the problems in the related art, the present invention proposes a built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array, which has the advantages of high precision, real-time monitoring, strong anti-interference ability and low maintenance cost, thereby solving the problems of easy environmental interference, insufficient reliability of the packaging structure and low signal processing precision of the traditional external sensors for wind turbine blades in the prior art.

[0008] For this reason, the specific technical solution adopted by the present invention is as follows:

[0009] A built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array, the built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array includes:

[0010] A distributed fiber Bragg grating array, arranged on the inner side of the wind turbine blade, for capturing the strain signal inside the blade;

[0011] A packaging module, including a flexible substrate and an adhesive, arranged on the top of the inner side of the wind turbine blade, for fixing and protecting the distributed fiber grating array to ensure accurate capture of the strain signal when the blade is stressed and deformed;

[0012] An optical fiber signal processor, connected to the distributed fiber Bragg grating array, for receiving and processing the strain signal transmitted by the fiber Bragg grating array;

[0013] A data analysis unit, connected to the optical fiber signal processor, for analyzing the processed strain signal by using a multi-modal data fusion algorithm and calculating the strain information of the wind turbine blade;

[0014] A wireless transmission module, connected to the data analysis unit, for wirelessly transmitting the strain signal to a monitoring center.

[0015] Specifically, a built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array of the present invention includes:

[0016] Distributed fiber Bragg grating array: Based on the finite element stress analysis results of the blade, grating nodes are arranged in a "zigzag" cross pattern in the high strain gradient areas (blade root, leading edge, airfoil transition area), and the grating density ≥ 3 pieces / m 2 ; In the low strain areas (blade tip, trailing edge), they are sparsely arranged, with a density ≤ 1 piece / m 2 , and the overall number of sensors is reduced by 20% - 30%.

[0017] Encapsulation module: It includes a flexible polyimide substrate, binder, etc., and is divided into a flexible silicone buffer layer (with a thickness of 0.5 - 1 mm and a Shore hardness of A30) to reduce installation stress; a carbon fiber reinforced polymer (CFRP, with a thickness of 2 - 3 mm and a fiber volume fraction of ≥60%) to resist salt spray corrosion and UV aging; a polyurethane waterproof coating (with a thickness of 0.1 mm and a contact angle of ≥110°) to meet the IP68 protection level. The fiber direction of the CFRP layer is consistent with the main load-bearing direction of the blade, and the proportion of the 0° direction ply is ≥70%.

[0018] Optical fiber signal processor: It uses a tunable laser (TLS) and an FPGA high-speed demodulation unit, and integrates the Gaussian-Newton iterative algorithm to fit the peak of the reflected spectrum in real time.

[0019] Data analysis unit: It is connected to the optical fiber signal processor and is used to analyze the processed signal, obtain the strain information of the wind turbine blade, fuse the strain data, the blade SCADA system (wind speed, pitch angle), and the vibration spectrum, and eliminate noise through Kalman filtering.

[0020] Wireless transmission module: It is connected to the data analysis unit and is used to wirelessly transmit the strain information to the monitoring center.

[0021] Specifically, the working principle of the present invention is as follows:

[0022] When the wind turbine blade deforms under the action of wind force, the fiber Bragg grating array distributed on the blade also deforms accordingly, resulting in a change in the grating center wavelength. This change is transmitted to the data analysis unit through the optical fiber signal processor, and the latter processes these signals through a special algorithm to accurately calculate the strain magnitude of the blade. Through the wireless transmission module, these data can be sent to the remote monitoring center in real time to achieve real-time monitoring of the strain state of the wind turbine blade.

[0023] Furthermore, when the data analysis unit analyzes the processed strain signal using the multi-modal data fusion algorithm and calculates the strain information of the wind turbine blade, it includes:

[0024] S1. Based on the strain data collected by the fiber Bragg grating, perform time synchronization and unit standardization processing, and extract the maximum strain value and strain gradient to obtain the first modal feature;

[0025] S2. Process the wind speed and pitch angle data through an interpolation method to ensure time alignment with the strain data, and extract the wind speed change rate and pitch angle adjustment frequency to obtain the second modal feature;

[0026] S3. Use a filtering algorithm to process the vibration spectrum data, and extract the vibration frequency and amplitude as an indirect representation of the dynamic load to obtain the third modal feature;

[0027] S4. Integrate the first-modal features, second-modal features, and third-modal features according to the multimodal data fusion algorithm to obtain the fused strain information of the wind turbine blade;

[0028] S5. Dynamically optimize the filtering algorithm based on the noise covariance matrix update strategy for environmental changes, and denoise the fused strain information of the wind turbine blade.

[0029] Furthermore, integrating the first-modal features, second-modal features, and third-modal features according to the multimodal data fusion algorithm to obtain the fused strain information of the wind turbine blade includes:

[0030] S41. Construct a state vector based on the first-modal features, second-modal features, and third-modal features;

[0031] S42. Construct a strain information weight coefficient according to the nonlinear relationship between wind speed, temperature, and strain, and set a vibration signal weight coefficient based on the change rates of wind speed and vibration frequency;

[0032] S43. Calculate a temperature compensation coefficient based on the real-time temperature change, and introduce the coupling effect of temperature and load by analyzing the change of vibration mode to establish a vibration frequency compensation coefficient;

[0033] S44. Use the strain information weight coefficient, vibration signal weight coefficient, temperature compensation coefficient, and vibration frequency compensation coefficient to perform weighted fusion on the modal data in the state vector to obtain a preliminary fusion result of the wind turbine blade;

[0034] S45. According to the coupling relationships between strain and vibration, and temperature and vibration in the wind turbine blade, optimize the preliminary fusion result of the wind turbine blade by introducing a first cross-coupling coefficient and a second cross-coupling coefficient to obtain the fused strain information of the wind turbine blade.

[0035] Furthermore, according to the coupling relationships between strain and vibration, and temperature and vibration in the wind turbine blade, optimizing the preliminary fusion result of the wind turbine blade by introducing a first cross-coupling coefficient and a second cross-coupling coefficient to obtain the fused strain information of the wind turbine blade includes:

[0036] S451. Calculate the first cross-coupling coefficient based on the product of the strain normalization value and the vibration frequency normalization value, combined with empirical data, to characterize the dynamic coupling effect between strain and vibration;

[0037] S452. Calculate the second cross-coupling coefficient using the product of the temperature difference and the vibration frequency normalization value, combined with an adjustment factor, to characterize the influence of temperature change on the vibration characteristics;

[0038] S453. Nonlinearly correct the preliminary fusion result of the wind turbine blade by weighted integration of the first and second cross-coupling coefficients with each modal data in the state vector to obtain the fused strain information of the wind turbine blade.

[0039] Further, the expression for the fused strain information of the wind turbine blade is:

[0040]

[0041] In the formula, X fusion (t) is the fused strain information of the wind turbine blade; w1(t) is the strain information weight coefficient; w2(t) is the vibration signal weight coefficient; w3(t) is the temperature compensation coefficient; w4(t) is the vibration frequency compensation coefficient; w5(t) is the first cross-coupling coefficient; w6(t) is the second cross-coupling coefficient; ε is the strain, v is the wind speed, β is the pitch angle, f vib is the vibration frequency.

[0042] Further, the distributed fiber optic grating array is conformally attached to the inner curved surface of the blade through a polymer film. The distributed fiber optic grating array includes a number of fiber optic gratings inscribed on a single optical fiber to cover different central wavelength bands; the reflectivity of the fiber optic gratings is controlled within a preset range to avoid crosstalk between adjacent grating signals; based on the finite element stress simulation results of the blade, the distributed fiber optic grating array uses a dense grating arrangement in the high strain sensitive area and a sparse arrangement in the low strain area to optimize the sensor layout and reduce the overall quantity.

[0043] Further, the encapsulation module includes a flexible substrate with a thickness not exceeding a preset value, and a glue layer with a specific thickness is coated on the surface of the flexible substrate; the encapsulation module synchronously cures the glue layer and the blade ply under a preset pressure range and a preset temperature through a vacuum bag pressing process, and the cured guide groove is filled with a flexible sealing material with a preset hardness to ensure that the change in the aerodynamic performance parameters of the wind turbine blade after the sensor is installed does not exceed a preset threshold.

[0044] Further, the fiber optic signal processor includes a wavelength demodulation device for converting the wavelength change of the fiber optic grating into an electrical signal; when receiving and processing the strain signal transmitted by the fiber optic grating array, the fiber optic signal processor includes: based on a broadband light source, emitting an optical signal with a bandwidth that can cover the wavelength ranges of all fiber optic gratings in the distributed fiber optic grating array; guiding the optical signal into the distributed fiber optic grating array through a fiber optic coupler, so that each fiber optic grating reflects a specific wavelength, and the remaining wavelengths are transmitted through to the subsequent gratings or the terminal absorber; using the edge linear region of the filter to convert the wavelength change into an optical intensity change, so that the optical intensity of the reflected light after passing through the filter has a linear relationship with the wavelength shift; according to the linear relationship, converting the voltage signal into a wavelength shift amount through the filter slope.

[0045] Furthermore, based on the noise covariance matrix update strategy for environmental changes, the filtering algorithm is dynamically optimized, and the denoising process of the fused strain information of the wind turbine blade includes:

[0046] S51. Based on the fused strain information of the wind turbine blade, establish a state equation and an observation equation; wherein, the state equation includes a state vector, a state transition matrix, a control input matrix, and process noise, and the observation equation includes an observed value, an observation matrix, and observation noise;

[0047] S52. According to the changes in the operating environment of the wind turbine blade, use a forgetting factor to balance the weights of historical data and current data, so as to dynamically update the process noise covariance matrix and the observation noise covariance matrix;

[0048] S53. Based on the updated noise covariance matrix, perform the prediction and update processes of the filtering algorithm to denoise the fused strain data.

[0049] Furthermore, based on the updated noise covariance matrix, perform the prediction and update processes of the filtering algorithm to denoise the fused strain data, including:

[0050] S531. In the prediction stage, calculate the prior error covariance matrix according to the state transition matrix and the process noise covariance matrix;

[0051] S532. Calculate the Kalman gain based on the prior error covariance matrix and the observation noise covariance matrix;

[0052] S533. In the update stage, use the Kalman gain and the observed value to correct the state estimate, output the filtered state vector, and obtain the denoised fused strain data of the wind turbine blade to monitor the strain state of the wind turbine blade in real time.

[0053] The beneficial effects of the present invention are as follows:

[0054] (1) The present invention proposes a sensing system composed of a carbon fiber reinforced polymer encapsulated and built-in distributed fiber Bragg grating sensor. Aiming at the problems of traditional external sensors of wind turbine blades being vulnerable to environmental interference, insufficient reliability of the encapsulation structure, and low signal processing accuracy, a flexible polyimide substrate is innovatively adopted, and mechanical protection and temperature stress compensation are realized through a multi-layer structure, effectively extending the service life of the sensor; based on the non-uniform fiber Bragg grating array layout of blade finite element stress analysis, the sensor layout is optimized, and the number of sensors is reduced by 20% to 30% overall and the monitoring efficiency is improved; an integrated multi-modal data fusion algorithm and dynamic wavelength demodulation technology are combined with a Kalman filter noise reduction method dynamically optimized based on environmental factors to achieve high-precision early crack warning; at the same time, a low-power dual-mode wireless transmission module is adopted to ensure long-distance stable communication in the high-humidity environment at sea.

[0055] (2) The present invention has developed a new type of strain monitoring technology for wind turbine blades, which can provide high-precision and high-reliability strain monitoring, accurately detect the internal strain changes of the wind turbine blades in real time, effectively analyze the internal health status of the blades, and can adapt to complex and changeable environmental conditions to meet the requirements of modern wind power generation technology.

[0056] (3) The present invention provides a sensing system that can be built into the blade structure, has distributed measurement capabilities, strong environmental adaptability and a long service life, and is equipped with advanced data fusion and processing algorithms, enabling comprehensive and real-time monitoring of the health status of wind turbine blades.

[0057] (4) The sensor of the present invention has the advantages of high precision, real-time monitoring, strong anti-interference ability and low maintenance cost, can detect early crack faults inside the wind turbine blades at an early stage, is applicable to various types of wind turbine generators, especially for strain monitoring of wind turbine blades in large offshore wind farms operating in harsh environments, and is of great significance for improving the operating efficiency and safety of wind turbine generators.

[0058] (5) The present invention realizes high-precision detection of the internal strain of wind turbine blades through the distributed fiber Bragg grating array technology, can accurately capture the strain changes of each part of the blade, especially adopts a dense arrangement strategy in the high-strain gradient area to ensure the monitoring accuracy of key areas; the system uses a wireless transmission module to realize real-time data transmission and monitoring, enabling the operation and maintenance personnel of the wind farm to grasp the blade status at any time. The fiber Bragg grating array naturally has the characteristic of anti-electromagnetic interference, is applicable to the complex electromagnetic environment of wind turbine units, and will not be affected by external interferences such as lightning strikes; at the same time, the fiber material has excellent stability and durability, greatly reducing the maintenance cost of the system and being suitable for long-term deployment and use; more importantly, the present invention can effectively identify early faults inside the wind turbine blades by analyzing the internal strain change law, provide a scientific basis for preventive maintenance, and significantly improve the operating safety and service life of wind turbine generators. Brief Description of the Drawings

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 is a schematic block diagram of a built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to an embodiment of the present invention;

[0061] Figure 2It is a schematic structural diagram of a built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array according to an embodiment of the present invention;

[0062] Figure 3 It is a schematic structural diagram of a packaging module in a built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array according to an embodiment of the present invention;

[0063] Figure 4 It is a schematic flow diagram of a data analysis unit in a built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array according to an embodiment of the present invention. Detailed implementation manners

[0064] To further illustrate the embodiments, the present invention provides accompanying drawings. These drawings are part of the disclosure of the present invention, mainly used to illustrate the embodiments, and can be combined with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and the advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are usually used to represent similar components.

[0065] According to an embodiment of the present invention, a built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array is provided.

[0066] Now, the present invention will be further described in combination with the accompanying drawings and specific implementation manners. As Figure 1 shown, according to an embodiment of the present invention, a built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array is provided. The built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array includes:

[0067] A distributed fiber Bragg grating array, arranged on the inner side of the wind turbine blade, for capturing the strain signals inside the blade;

[0068] A packaging module, including a flexible substrate and an adhesive, arranged on the top of the inner side of the wind turbine blade, for fixing and protecting the distributed fiber Bragg grating array to ensure accurate capture of strain signals when the blade is deformed under force;

[0069] An optical fiber signal processor, connected to the distributed fiber Bragg grating array, for receiving and processing the strain signals transmitted by the fiber Bragg grating array;

[0070] A data analysis unit, connected to the optical fiber signal processor, for analyzing the processed strain signals using a multi-modal data fusion algorithm and calculating the strain information of the wind turbine blade;

[0071] A wireless transmission module, connected to the data analysis unit, for wirelessly transmitting the strain signals to a monitoring center.

[0072] In one embodiment, when the data analysis unit analyzes the processed strain signal using the multimodal data fusion algorithm and calculates the strain information of the wind turbine blade, it includes:

[0073] S1. Based on the strain data collected by fiber Bragg grating, perform time synchronization and unit normalization processing, and extract the maximum strain value and strain gradient to obtain the first modal feature;

[0074] S2. Process the wind speed and pitch angle data through an interpolation method to ensure time alignment with the strain data, and extract the wind speed change rate and pitch angle adjustment frequency to obtain the second modal feature;

[0075] S3. Process the vibration spectrum data using a filtering algorithm, and extract the vibration frequency and amplitude as an indirect representation of the dynamic load to obtain the third modal feature;

[0076] S4. According to the multimodal data fusion algorithm, integrate the first modal feature, the second modal feature, and the third modal feature to obtain the fused strain information of the wind turbine blade;

[0077] S5. Based on the noise covariance matrix update strategy for environmental changes, dynamically optimize the filtering algorithm, and perform denoising processing on the fused strain information of the wind turbine blade.

[0078] In one embodiment, according to the multimodal data fusion algorithm, integrating the first modal feature, the second modal feature, and the third modal feature to obtain the fused strain information of the wind turbine blade includes:

[0079] S41. Based on the first modal feature, the second modal feature, and the third modal feature, construct a state vector;

[0080] S42. According to the non-linear relationship between wind speed, temperature, and strain, construct a strain information weight coefficient, and based on the change rates of wind speed and vibration frequency, set a vibration signal weight coefficient;

[0081] S43. Calculate the temperature compensation coefficient based on the real-time temperature change, and by analyzing the change of the vibration mode, introduce the coupling effect of temperature and load to establish a vibration frequency compensation coefficient;

[0082] S44. Use the strain information weight coefficient, the vibration signal weight coefficient, the temperature compensation coefficient, and the vibration frequency compensation coefficient to perform weighted fusion on each modal data in the state vector to obtain a preliminary fusion result of the wind turbine blade;

[0083] S45. According to the coupling relationship between strain and vibration, and between temperature and vibration in the wind turbine blade, by introducing a first cross-coupling coefficient and a second cross-coupling coefficient, optimize the preliminary fusion result of the wind turbine blade to obtain the fused strain information of the wind turbine blade.

[0084] In one embodiment, according to the coupling relationships between strain and vibration, and between temperature and vibration in a wind turbine blade, by introducing a first cross-coupling coefficient and a second cross-coupling coefficient, the preliminary fusion result of the wind turbine blade is optimized, and the fused strain information of the wind turbine blade includes:

[0085] S451. Calculate the first cross-coupling coefficient based on the product of the strain normalization value and the vibration frequency normalization value, and combine empirical data to characterize the dynamic coupling effect between strain and vibration;

[0086] S452. Calculate the second cross-coupling coefficient using the product of the temperature difference and the vibration frequency normalization value, and combine an adjustment factor to characterize the influence of temperature change on vibration characteristics;

[0087] S453. Nonlinearly correct the preliminary fusion result of the wind turbine blade by weighted integration of the first cross-coupling coefficient and the second cross-coupling coefficient in the state vector to obtain the fused strain information of the wind turbine blade.

[0088] Specifically, the multi-modal data fusion algorithm of the present invention includes strain data, the blade SCADA system (wind speed, pitch angle), and vibration spectrum. As Figure 4 shown, first, data preprocessing is performed. The strain data collected by the fiber Bragg grating is synchronized in time and normalized in units. The SCADA data (wind speed, pitch angle) is interpolated to ensure alignment with the strain data, and the vibration spectrum data is filtered to extract the characteristic frequency. Features such as the maximum strain value and strain gradient are extracted from the strain data, features such as the wind speed change rate and pitch angle adjustment frequency are extracted from the SCADA data, and the main vibration frequency (vibration frequency) and amplitude are extracted from the vibration spectrum as an indirect representation of the dynamic load. A state vector X = [ε, v, β, f vib is constructed, where ε is the strain, v is the wind speed, β is the pitch angle, and f vib is the vibration frequency. The multi-modal data is preliminarily integrated using a weighted fusion method, and the weights are determined according to the contribution degrees of each modal data to the blade strain to obtain the preliminary fusion result of the wind turbine blade, and the expression is:

[0089] X fusion (t) = w1(t)ε + w2(t)v + w3(t)β + w4(t)f vib ;

[0090] In the formula, w1(t), w2(t), w3(t), and w4(t) are the strain information weight coefficient, vibration signal weight coefficient, temperature compensation coefficient, and vibration frequency compensation coefficient, respectively.

[0091] Specifically, the dynamic adjustment of the weighting coefficients in the present invention is determined based on the force characteristics of the blade under different operating conditions. w1(t) is the strain information weighting coefficient. Based on the regression method of machine learning, it describes the non-linear relationship among wind speed, temperature, and strain. The expression is:

[0092] w1(t) = f1(v(t), T(t), ε(t));

[0093] In the formula, v(t) is the wind speed; T(t) is the temperature; ε(t) is the strain; w1(t) calculates the dynamic adjustment value through the wind speed, temperature, and strain response of the blade.

[0094] Specifically, w2(t) is the vibration signal weighting coefficient. The vibration signal reflects the dynamic response of the blade during operation; the adjustment of w2(t) is dynamically adjusted based on the change rate of wind speed and vibration frequency. The expression is:

[0095]

[0096] Δv(t) is the change rate of wind speed at time t, α' and β' are dynamic adjustment factors determined according to the previous operation data, and Δf vib (t) is the vibration change rate at time t.

[0097] w3(t) is the temperature compensation coefficient: Temperature is a key factor affecting the performance of the sensor. The adjustment of w3(t) depends on the real-time temperature change. The expression is:

[0098] w3(t) = 1 + γ(T(t) - T0);

[0099] In the formula, γ is the temperature sensitivity coefficient, T0 is the reference temperature (in this embodiment, it is the standard temperature of the sensor or the laboratory environment temperature), and T(t) is the current ambient temperature.

[0100] w4(t) is the vibration frequency compensation coefficient: The calculation of the vibration frequency compensation coefficient considers the coupling effect among the vibration mode of the blade and factors such as temperature and load. By analyzing the change of the vibration mode, the value of w4 is determined, and the coupling effect of temperature and load is introduced for adjustment. δ and θ are adjustment factors adjusted according to the real-time operation data:

[0101]

[0102] Meanwhile, further considering the coupling relationship among the parameters, by introducing the first cross-term and the second cross-term of strain, vibration, and temperature, the coupling effect among the parameters can be captured more accurately. The formula is extended to obtain the expression of the fused strain information of the wind turbine blade as:

[0103]

[0104] Wherein, X fusion (t) is the integrated strain information of the wind turbine blade; w1(t) is the strain information weight coefficient; w2(t) is the vibration signal weight coefficient; w3(t) is the temperature compensation coefficient; w4(t) is the vibration frequency compensation coefficient; w5(t) is the first cross-coupling coefficient; w6(t) is the second cross-coupling coefficient; ε is the strain, v is the wind speed, β is the pitch angle, and f vib is the vibration frequency.

[0105] Specifically, w5(t) and w6(t) are the first cross-coupling coefficient and the second cross-coupling coefficient respectively, which are used to describe the coupling relationship between strain and vibration, and between temperature and vibration; the expression of the first cross-coupling coefficient is:

[0106]

[0107] Wherein, ε(t) is the strain at the current moment, and ε max (t) is the historical maximum strain, and f vib (t) is the vibration frequency at time t, and f vib,max (t) is the historical maximum vibration frequency, and α5 is the empirical data obtained after actual operation.

[0108] Specifically, the expression of the second cross-coupling coefficient is:

[0109]

[0110] Wherein, T max (t) is the historical maximum temperature, and α6 is the adjustment factor, which is determined according to the operation data of a certain stage.

[0111] In one embodiment, the distributed fiber Bragg grating array is conformally attached to the inner curved surface of the blade through a polymer film. The distributed fiber Bragg grating array includes a plurality of fiber Bragg gratings inscribed on a single optical fiber to cover different central wavelength bands;

[0112] The reflectivity of the fiber Bragg grating is controlled within a preset range to avoid signal crosstalk between adjacent gratings;

[0113] Based on the finite element stress simulation results of the blade, the distributed fiber Bragg grating array adopts a dense grating arrangement in the high-strain sensitive area and a sparse arrangement in the low-strain area to optimize the sensor layout and reduce the overall quantity.

[0114] Specifically, in the present invention, multiple fiber Bragg gratings (FBGs) are inscribed on a single optical fiber. The grating interval is dynamically adjusted according to the strain gradient of the blade (for example, the interval in the root area of the blade is ≤ 10 cm, and the interval in the tip area of the blade is ≤ 30 cm); the grating reflectivity is controlled between 1% and 5% to avoid signal crosstalk between adjacent gratings and ensure that the wavelength demodulation accuracy is ≤ 5 pm.

[0115] Specifically, the present invention supports wavelength division multiplexing (WDM) technology. A single optical fiber can accommodate 50 to 100 gratings, covering different central wavelength bands.

[0116] Specifically, based on the finite element stress simulation results of the blade, dense grating arrangements are adopted in high-strain sensitive areas (such as the blade root, leading edge, and airfoil transition zone), and sparse arrangements are used in low-strain areas, reducing the number of sensors by 20% - 30%. The grating array conformally adheres to the inner curved surface of the blade through a polyimide film, and the adhesion error is ≤0.1 mm, avoiding additional stress introduced by installation.

[0117] In one embodiment, the encapsulation module includes a flexible substrate with a thickness not exceeding a preset value, and the surface of the flexible substrate is coated with an adhesive layer of a specific thickness;

[0118] Specifically, the encapsulation module synchronously cures the adhesive layer and the blade ply under a preset pressure range and a preset temperature through a vacuum bag pressing process, and the cured guide groove is filled with a flexible sealing material of a preset hardness to ensure that the change in the aerodynamic performance parameters of the wind turbine blade after the sensor installation does not exceed a preset threshold.

[0119] Specifically, as Figure 2 shown, the laser emits a stable optical signal, which is primarily amplified by a semiconductor optical amplifier (SOA), and the amplification process is precisely controlled by a single-chip microcomputer through a driving module; an erbium-doped fiber amplifier (EDFA) further enhances the optical signal intensity. The circulator is used to guide the amplified optical signal into the distributed fiber Bragg grating (FBG) array arranged inside the wind turbine blade. The reflected signal of a specific wavelength is then guided back by the circulator to a photodetector (PD) for photoelectric conversion. The obtained electrical signal is digitally processed by an analog-to-digital converter (ADC) and collected by the single-chip microcomputer, and then transmitted to the upper computer (PC).

[0120] Specifically, as Figure 3 shown, the present invention embeds a flexible polyimide substrate (thickness ≤0.2 mm) encapsulating the fiber grating array as the encapsulation module into the wind turbine blade. The surface of the substrate is coated with an epoxy resin adhesive layer as the binder, and the thickness of the adhesive layer is 50 - 100 μm. The vacuum bag pressing process is used to synchronously cure the adhesive layer and the blade ply. The curing pressure is 0.5 - 1 MPa, and the temperature is 120 °C. After curing, the guide groove is filled with a flexible silicone sealant with a Shore hardness of A30 - 50. After the installation is completed, the change in the lift-drag ratio of the blade is verified by wind tunnel testing to be ≤1%.

[0121] In one embodiment, the fiber optic signal processor includes a wavelength demodulation device for converting the wavelength change of the fiber grating into an electrical signal;

[0122] When the fiber optic signal processor receives and processes the strain signals transmitted by the fiber grating array, it includes:

[0123] Based on a broadband light source, an optical signal whose emission bandwidth can cover the wavelength ranges of all fiber Bragg gratings in a distributed fiber Bragg grating array;

[0124] The optical signal is introduced into the distributed fiber Bragg grating array through an optical fiber coupler, so that each fiber Bragg grating reflects a specific wavelength, and the remaining wavelengths are transmitted to subsequent gratings or a terminal absorber through transmission;

[0125] Utilize the edge linear region of the filter to convert the wavelength change into an optical intensity change, so that the optical intensity of the reflected light after passing through the filter has a linear relationship with the wavelength shift;

[0126] According to the linear relationship, convert the voltage signal into a wavelength shift amount through the filter slope.

[0127] Specifically, the optical fiber signal processor includes a wavelength demodulation device for converting the wavelength change of the fiber Bragg grating into an electrical signal. Use a broadband light source to emit an optical signal covering the reflection band of the FBG. The light source bandwidth needs to cover the wavelength ranges of all FBGs. The optical signal enters the distributed FBG array through an optical fiber coupler. Each FBG reflects its specific wavelength λ B , and the remaining wavelengths are transmitted to subsequent gratings or a terminal absorber. Utilize the edge linear region of the filter to convert the wavelength change into an optical intensity change. After the reflected light passes through the filter, the optical intensity I has a linear relationship with the wavelength shift Δλ. The expression of the linear relationship is:

[0128] I = k·Δλ + I0;

[0129] In the formula, k is the filter slope, I is the current signal; I0 is the initial current value; Δλ is the wavelength shift amount;

[0130] Specifically, thus, the Δλ can be converted into the current signal I through the calibration coefficient k (filter slope).

[0131] Specifically, the wireless transmission module supports multiple wireless communication protocols, including but not limited to Wi-Fi, Bluetooth, or a mobile communication network.

[0132] Specifically, the sensor of the present invention has waterproof and dustproof functions and is applicable to harsh environments such as offshore wind farms.

[0133] In one embodiment, based on the noise covariance matrix update strategy of environmental changes, dynamically optimize the filtering algorithm, and perform denoising processing on the fused strain information of the wind turbine blade, including:

[0134] S51. Based on the fused strain information of the wind turbine blade, establish a state equation and an observation equation; wherein, the state equation includes a state vector, a state transition matrix, a control input matrix, and process noise, and the observation equation includes an observed value, an observation matrix, and observation noise;

[0135] S52. According to the changes in the operating environment of the wind turbine blade, use the forgetting factor to balance the weights of historical data and current data, so as to dynamically update the process noise covariance matrix and the observation noise covariance matrix;

[0136] S53. Based on the updated noise covariance matrix, perform the prediction and update processes of the filtering algorithm to denoise the fused strain data.

[0137] In one embodiment, based on the updated noise covariance matrix, performing the prediction and update processes of the filtering algorithm to denoise the fused strain data includes:

[0138] S531. In the prediction stage, calculate the prior error covariance matrix according to the state transition matrix and the process noise covariance matrix;

[0139] S532. Calculate the Kalman gain based on the prior error covariance matrix and the observation noise covariance matrix;

[0140] S533. In the update stage, use the Kalman gain and the observation value to correct the state estimate, output the filtered state vector, and obtain the denoised fused strain data of the wind turbine blade to monitor the strain state of the wind turbine blade in real time.

[0141] Specifically, due to the presence of noise such as wind load randomness and mechanical vibration in the operating environment of the wind turbine blade, the measurement noise of the sensor and the process noise covariance matrix may change over time, and the high humidity, temperature fluctuations of the sea breeze and blade vibration will cause dynamic drift of the noise characteristics. The traditional Kalman filter using fixed noise covariance matrices Q and R cannot adapt to such changes. Therefore, the present invention proposes an adaptive noise covariance matrix update strategy to form a Kalman filtering method dynamically optimized based on environmental factors to denoise the fused data.

[0142] Specifically, the state equation is:

[0143] X k =AX k-1 +Bu k +w k ;

[0144] In the formula, X k is the state vector at the k-th moment, A is the state transition matrix, B is the control input matrix, u k is the external input (such as wind speed change), and w k is the process noise;

[0145] The observation equation is:

[0146] Z k =HX k +v k ;

[0147] In the formula, Z k is the observed value, HX k is the observation matrix, and v k is the observation noise. The process noise covariance matrix is Q k , representing the noise of the system process. The observation noise covariance matrix R k , representing the uncertainty of the noise of the observed data.

[0148] Specifically, the operating environment of the wind turbine blade (such as wind speed, temperature, etc.) will affect the performance of the sensor, and thus affect the characteristics of the noise. Therefore, the present invention designs a noise covariance matrix update strategy based on environmental changes, and the expression is:

[0149]

[0150] In the formula, α1 and β1 are forgetting factors, and the value range is (0,1), which are used to balance the weights of historical data and current data; K k is the Kalman gain; y k is the measurement residual, P k | k-1 and P k | k are the prior and posterior error covariance matrices respectively; H k is the measurement matrix.

[0151] Specifically, during the filtering process, in the prediction stage:

[0152]

[0153] Update stage formula:

[0154] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ;

[0155]

[0156] P k|k =(I-K k H)P k|k-1 ;

[0157] In the formula, K k is the Kalman gain, P is the error covariance matrix, H is the measurement matrix describing the linear relationship between the state variable and the measured value, R is the measurement noise covariance matrix, is the prior state estimate of the current state; the output is the state vector after filtering Obtain the denoised strain data.

[0158] Specifically, the strain sensor of the wind turbine blade of the present invention can monitor the early fault development of the wind turbine blade inside the fan blade by accurately and real-time monitoring the strain state of the wind turbine blade, and can significantly improve the operation efficiency and safety of the wind power generation unit.

[0159] In order to facilitate the understanding of the above technical solutions of the present invention, the following is described by specific embodiments:

[0160] Embodiment 1

[0161] To verify the feasibility of the present invention, a certain type of wind turbine blade was selected as the test object. First, the fiber Bragg grating (FBG) was arranged: based on the finite element stress analysis results of the blade, grating nodes were arranged in a zigzag cross pattern in the high strain gradient areas (blade root, leading edge, airfoil transition area), and the grating density was ≥ 3 per meter 2 ; in the low strain areas (blade tip, trailing edge), it was sparsely arranged, with a density of ≤ 1 per meter 2 , the overall number of sensors was reduced by 20% - 30%, and it was ensured that the key stress areas of the entire blade were covered. The layout density of the distributed fiber Bragg grating array was positively correlated with the high strain gradient area (strain change rate ≥ 0.1 με / mm) of the blade finite element analysis.

[0162] The encapsulation of the distributed fiber Bragg grating adopted a three-layer structure design: the encapsulation mold consisted of carbon fiber reinforced polymer (CFRP), a flexible silicone buffer layer, and a polyurethane waterproof layer, and was divided into an inner layer, a middle layer, and an outer layer. A small groove consistent with the fiber direction was designed in the middle of the upper layer to facilitate the fixation of the fiber and effectively protect the fiber from breaking during the encapsulation process. The fiber direction of the CFRP layer was consistent with the main load-bearing direction of the blade, and the proportion of the 0° direction layup reached more than 70%.

[0163] The signal processing and analysis system consisted of a fiber optic signal processor and a data analysis unit. The fiber Bragg grating was connected to the fiber optic signal processor. The processor used a tunable laser and an FPGA high-speed demodulation unit, and through a highly sensitive wavelength demodulation device, accurately converted the change in the center wavelength of the fiber Bragg grating into an electrical signal. The data analysis unit received these electrical signals, calculated the strain magnitude of the blade through a multi-modal data fusion algorithm, and effectively eliminated the influence of environmental noise by combining the Kalman filtering technology.

[0164] In terms of wireless data transmission, the data analysis unit was connected to the wireless transmission module. This module supported multiple communication protocols and could send the monitored data to the remote monitoring center in real time to achieve real-time monitoring of the strain state of the wind turbine blade.

[0165] In the system testing and optimization stage, comprehensive tests were carried out in actual wind turbine blades and simulated environments. By adjusting the arrangement density of fiber Bragg gratings and signal processing parameters, the accuracy and response speed of the system were optimized to ensure that the strain state of wind turbine blades could be accurately reflected under various working conditions. The test results showed that the system could capture tiny strain changes of 0.1 με and the response time was less than 10 ms.

[0166] Embodiment 2

[0167] Based on Embodiment 1, the environmental adaptability and data processing ability of the system were further improved. To adapt to harsh environments such as offshore wind farms, the sensors were strengthened with waterproof and dustproof treatments. The encapsulation adopted a double-layer waterproof design, with a fluorocarbon coating on the outer layer and modified silicone rubber on the inner layer, and the temperature resistance range reached -40°C to 85°C. At the same time, materials with corrosion resistance and resistance to high and low temperatures were selected to ensure the stability and reliability of the sensors in extreme environments and meet the requirements of IP68 protection level.

[0168] In terms of data processing, a dedicated data processing software was developed, which could not only display the strain data of the blades in real time, but also perform trend analysis based on historical data to timely warn of possible blade damage or failures. The software adopted an adaptive weight adjustment mechanism to dynamically optimize the multi-modal data fusion algorithm according to the operating state, further improving the accuracy of strain monitoring.

[0169] To verify the versatility of the system, compatibility tests of the sensors were carried out on wind turbine generators of different brands and models. The results showed that the present invention could be widely applied to various wind turbine units and had good compatibility and scalability.

[0170] Through the above embodiments, the wind turbine blade strain sensor of the present invention can operate stably under various working conditions, provide accurate and real-time strain monitoring data, and is of great significance for improving the operating efficiency and safety of wind turbine units.

[0171] In summary, by means of the above technical solutions of the present invention, a sensing system composed of carbon fiber reinforced polymer encapsulated and built-in distributed fiber Bragg grating sensors is proposed. Aiming at the problems of traditional external sensors on wind turbine blades being vulnerable to environmental interference, insufficient reliability of the encapsulation structure, and low signal processing accuracy, a flexible polyimide substrate is innovatively adopted, and mechanical protection and temperature stress compensation are realized through a multi-layer structure, effectively extending the service life of the sensor; based on the non-uniform fiber Bragg grating array layout of blade finite element stress analysis, the sensor layout is optimized, reducing the number of sensors by 20% to 30% overall and improving the monitoring efficiency; integrating multi-modal data fusion algorithms and dynamic wavelength demodulation technology, combined with a Kalman filter noise reduction method dynamically optimized based on environmental factors, realizing high-precision early warning of early cracks; at the same time, a low-power dual-mode wireless transmission module is adopted to ensure long-distance stable communication in the high-humidity environment at sea.

[0172] The present invention develops a new strain monitoring technology for wind turbine blades, which can provide high-precision and high-reliability strain monitoring, can accurately detect the internal strain changes of wind turbine blades in real time, effectively analyze the internal health status of the blades, and can adapt to complex and changeable environmental conditions to meet the requirements of modern wind power generation technology.

[0173] The present invention provides a sensing system that can be built into the blade structure, has distributed measurement capabilities, strong environmental adaptability and long life, and is equipped with advanced data fusion and processing algorithms, enabling comprehensive and real-time monitoring of the health status of wind turbine blades.

[0174] The sensors of the present invention have the advantages of high precision, real-time monitoring, strong anti-interference ability and low maintenance cost, can detect early crack faults inside the wind turbine blades at an early stage, are applicable to various types of wind turbine generators, especially for strain monitoring of wind turbine blades in large offshore wind farms operating in harsh environments, which is of great significance for improving the operation efficiency and safety of wind turbine generators.

[0175] The present invention realizes high-precision detection of the internal strain of wind turbine blades through distributed fiber Bragg grating array technology, can accurately capture the strain changes of each part of the blade, especially adopting a dense layout strategy in high-strain gradient areas to ensure the monitoring accuracy of key areas; the system uses a wireless transmission module to realize real-time data transmission and monitoring, enabling wind farm operation and maintenance personnel to always master the blade status. The fiber Bragg grating array naturally has the characteristic of anti-electromagnetic interference and is suitable for the complex electromagnetic environment of wind turbine units and will not be affected by external interferences such as lightning strikes; at the same time, the optical fiber material has excellent stability and durability, greatly reducing the maintenance cost of the system and being suitable for long-term deployment and use; more importantly, the present invention can effectively identify early faults inside the wind turbine blades by analyzing the internal strain change law, providing a scientific basis for preventive maintenance and significantly improving the operation safety and service life of wind turbine generators.

[0176] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array, characterized in that, including: A distributed fiber Bragg grating array, which is arranged on the inner side of the wind turbine blade and is used to capture the strain signals inside the blade; An encapsulation module, including a flexible substrate and an adhesive, which is arranged at the top of the inner side of the wind turbine blade and is used to fix and protect the distributed fiber grating array to ensure accurate capture of strain signals when the blade is stressed and deformed; An optical fiber signal processor, which is connected to the distributed fiber Bragg grating array and is used to receive and process the strain signals transmitted by the fiber Bragg grating array; A data analysis unit, which is connected to the optical fiber signal processor and is used to analyze the processed strain signals by using a multi-modal data fusion algorithm and calculate the strain information of the wind turbine blade; A wireless transmission module, which is connected to the data analysis unit and is used to wirelessly transmit the strain signals to a monitoring center.

2. The built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to claim 1, characterized in that, When the data analysis unit analyzes the processed strain signals by using a multi-modal data fusion algorithm and calculates the strain information of the wind turbine blade, it includes: S1. Based on the strain data collected by the fiber Bragg grating, perform time synchronization and unit normalization processing, and extract the maximum strain value and strain gradient to obtain the first modal feature; S2. Process the wind speed and pitch angle data by using an interpolation method to ensure time alignment with the strain data, and extract the wind speed change rate and pitch angle adjustment frequency to obtain the second modal feature; S3. Process the vibration spectrum data by using a filtering algorithm and extract the vibration frequency and amplitude as an indirect representation of the dynamic load to obtain the third modal feature; S4. According to the multi-modal data fusion algorithm, integrate the first modal feature, the second modal feature, and the third modal feature to obtain the fusion strain information of the wind turbine blade; S5. Based on the noise covariance matrix update strategy for environmental changes, dynamically optimize the filtering algorithm and perform denoising processing on the fusion strain information of the wind turbine blade.

3. The built-in strain sensing system for wind turbine blades based on a distributed fiber grating array according to claim 2, characterized in that, The integration of the first modal feature, the second modal feature, and the third modal feature according to the multi-modal data fusion algorithm to obtain the fusion strain information of the wind turbine blade includes: S41. Based on the first modal feature, the second modal feature, and the third modal feature, construct a state vector; S42. According to the non-linear relationship between wind speed, temperature, and strain, construct a strain information weight coefficient, and based on the change rates of wind speed and vibration frequency, set a vibration signal weight coefficient; S43. Calculate the temperature compensation coefficient based on the real-time temperature change, and by analyzing the change of the vibration mode, introduce the coupling effect of temperature and load to establish a vibration frequency compensation coefficient; S44. Use the strain information weight coefficient, the vibration signal weight coefficient, the temperature compensation coefficient, and the vibration frequency compensation coefficient to perform weighted fusion on each modal data in the state vector to obtain a preliminary fusion result of the wind turbine blade; S45. According to the coupling relationships between strain and vibration, and temperature and vibration in the wind turbine blade, by introducing a first cross-coupling coefficient and a second cross-coupling coefficient, optimize the preliminary fusion result of the wind turbine blade to obtain the fusion strain information of the wind turbine blade.

4. The built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to claim 3, characterized in that, The optimization of the preliminary fusion result of the wind turbine blade by introducing a first cross-coupling coefficient and a second cross-coupling coefficient according to the coupling relationships between strain and vibration, and temperature and vibration in the wind turbine blade to obtain the fusion strain information of the wind turbine blade includes: S451. Calculate the first cross-coupling coefficient based on the product of the strain normalization value and the vibration frequency normalization value, and combine empirical data to characterize the dynamic coupling effect between strain and vibration. S452. Calculate the second cross-coupling coefficient by using the product of the temperature difference and the vibration frequency normalization value, and combine the adjustment factor to characterize the influence of temperature change on vibration characteristics. S453. Nonlinearly correct the preliminary fusion result of the wind turbine blade by weighted integration of the first cross-coupling coefficient and the second cross-coupling coefficient through each modal data in the state vector to obtain the fusion strain information of the wind turbine blade.

5. The built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to claim 4, characterized in that, The expression of the fusion strain information of the wind turbine blade is: X fusion (t) = w1(t)ε + w2(t)v + w3(t)β + w4(t)f vib + w5(t)εv + w6(t)βf vib ; where X fusion (t) is the integrated strain information of the wind turbine blade; w1(t) is the strain information weight coefficient; w2(t) is the vibration signal weight coefficient; w3(t) is the temperature compensation coefficient; w4(t) is the vibration frequency compensation coefficient; w5(t) is the first cross-coupling coefficient; w6(t) is the second cross-coupling coefficient; ε is the strain, v is the wind speed, β is the pitch angle, f vib is the vibration frequency.

6. The built-in sensing system for wind turbine blade strain based on a distributed fiber Bragg grating array according to claim 1, characterized in that, The distributed fiber Bragg grating array is conformally attached to the inner curved surface of the blade through a polymer film. The distributed fiber Bragg grating array includes a plurality of fiber Bragg gratings inscribed on a single optical fiber to cover different central wavelength bands. The reflectivity of the fiber Bragg grating is controlled within a preset range to avoid signal crosstalk between adjacent gratings. Based on the finite element stress simulation results of the blade, the distributed fiber Bragg grating array adopts a dense grating arrangement in the high-strain sensitive area and a sparse arrangement in the low-strain area to optimize the sensor layout and reduce the overall quantity.

7. A built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to claim 1, characterized in that, The encapsulation module includes a flexible substrate with a thickness not exceeding a preset value, and a glue layer with a specific thickness is coated on the surface of the flexible substrate. The encapsulation module synchronously cures the glue layer and the blade ply under a preset pressure range and a preset temperature through a vacuum bag pressing process, and the cured guide groove is filled with a flexible sealing material with a preset hardness to ensure that the change of the aerodynamic performance parameters of the wind turbine blade does not exceed a preset threshold after the sensor installation is completed.

8. The built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to claim 1, characterized in that, The fiber optic signal processor includes a wavelength demodulation device for converting the wavelength change of the fiber Bragg grating into an electrical signal. When the fiber optic signal processor receives and processes the strain signals transmitted by the fiber Bragg grating array, it includes: Based on a broadband light source, emit an optical signal whose emission bandwidth can cover the wavelength ranges of all fiber Bragg gratings in the distributed fiber Bragg grating array. Introduce the optical signal into the distributed fiber Bragg grating array through an optical fiber coupler, so that each fiber Bragg grating reflects a specific wavelength, and the remaining wavelengths are transmitted through to the subsequent gratings or the terminal absorber. Utilize the edge linear region of the filter to convert the wavelength change into an optical intensity change, so that the optical intensity of the reflected light after passing through the filter has a linear relationship with the wavelength shift. According to the linear relationship, convert the voltage signal into a wavelength shift amount through the filter slope.

9. The built-in strain sensing system for wind turbine blades based on a distributed fiber Bragg grating array according to claim 2, characterized in that, The noise covariance matrix update strategy based on environmental changes dynamically optimizes the filtering algorithm and denoises the fusion strain information of the wind turbine blade, including: S51. Based on the fusion strain information of the wind turbine blade, establish a state equation and an observation equation; wherein, the state equation includes a state vector, a state transition matrix, a control input matrix, and process noise, and the observation equation includes an observation value, an observation matrix, and observation noise. S52. According to the change of the operating environment of the wind turbine blade, use a forgetting factor to balance the weights of historical data and current data to dynamically update the process noise covariance matrix and the observation noise covariance matrix. S53. Based on the updated noise covariance matrix, perform the prediction and update processes of the filtering algorithm to denoise the fused strain data.

10. A built-in strain sensing system for a wind turbine blade based on a distributed fiber Bragg grating array according to claim 9, characterized in that, The performing the prediction and update processes of the filtering algorithm based on the updated noise covariance matrix to denoise the fused strain data includes: S531. In the prediction stage, calculate the prior error covariance matrix according to the state transition matrix and the process noise covariance matrix; S532. Calculate the Kalman gain based on the prior error covariance matrix and the observation noise covariance matrix; S533. In the update stage, use the Kalman gain and the observation value to correct the state estimate, output the filtered state vector, and obtain the denoised fused strain data of the wind turbine blade to monitor the strain state of the wind turbine blade in real time.

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