Fan blade condition monitoring system and method for use in production transport

Abstract

The application provides a kind of fan blade state monitoring system and method for production in transport process, belong to wind turbine fan blade online monitoring field, the system includes multidimensional sensing data acquisition terminal and blade state early warning terminal;Multidimensional sensing data acquisition terminal includes vibration acceleration acquisition module, multi-node strain monitoring module, first control module and first wireless transmission module;Multi-node strain monitoring module includes a plurality of strain monitoring nodes distributed in blade vulnerable loss site, each strain monitoring node includes two groups of mutually perpendicular strain monitoring components, each group of strain monitoring components includes monitoring strain sensor and compensation strain sensor, monitoring strain sensor is arranged on blade, and compensation strain sensor is arranged on compensation plate of same material with blade.By the monitoring system provided in the application, the strain signal can be compensated, the influence of environmental temperature and other factors on strain monitoring can be effectively inhibited, and the blade state estimation accuracy can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology for wind turbine blades, specifically to a wind turbine blade condition monitoring system and a wind turbine blade condition monitoring method for production and transportation processes. Background Technology

[0002] With the rapid development of new energy technologies, wind power generation, represented by wind turbine generators, occupies an important position. Wind turbine blades are the components of a wind turbine generator that absorb wind energy, and their operating condition directly affects the power generation efficiency of the wind turbine generator and the service life of other components. As wind turbines operate over time, defects such as cracks, bulges, breaks, and pits begin to appear on the blade surface, and in severe cases, breakage may occur. These defects are directly or indirectly related to the condition of the blades during production and transportation. During the production process, such as mold closing, flipping, hoisting, transfer, and grinding, as well as during transportation, the blades are subjected to external forces, which can easily cause subtle damage and are important factors leading to further defects. Due to the limitations of wind resources and environment, wind turbine generators are mostly located far from urban areas, at sea, in mountains, and in deserts, resulting in high maintenance costs. Therefore, monitoring and early warning of wind turbine blade condition during production and transportation, and early detection of subtle damage to prevent further defects, is of great significance for reducing wind turbine operation and maintenance costs and extending the service life of wind turbines.

[0003] Existing blade condition monitoring technologies primarily monitor the blade condition during wind turbine operation. These technologies involve numerous and complex variables and do not consider the influence of environmental factors when measuring strain signals. Furthermore, stress sensors and strain gauges are highly susceptible to environmental temperature fluctuations, leading to significant errors in the measured strain signals. Additionally, existing monitoring methods mainly reflect blade condition through time-domain characteristics, but the indicative power of time-domain characteristics is limited and cannot comprehensively reflect the blade condition. Therefore, this application proposes a wind turbine blade condition monitoring and early warning system for use during production and transportation, enabling early detection of damage and reducing the likelihood of further defects. Summary of the Invention

[0004] To address the technical problem that existing blade condition monitoring technologies do not consider the influence of environmental factors on strain signals, this invention provides a wind turbine blade condition monitoring and early warning system for use during production and transportation. This system can monitor the actual strain and vibration states of the blades, effectively improving the accuracy of blade condition estimation.

[0005] To achieve the above objectives, the present invention provides a wind turbine blade condition monitoring and early warning system for use during production and transportation. This system includes a multi-dimensional sensor data acquisition terminal and a blade condition early warning terminal. The multi-dimensional sensor data acquisition terminal includes a vibration acceleration acquisition module, a multi-node strain monitoring module, a first control module, and a first wireless transmission module. The vibration acceleration acquisition module includes multiple triaxial vibration acceleration sensors mounted on the SS surface of the blade for acquiring vibration acceleration signals at different locations on the blade. The multi-node strain monitoring module is used to acquire strain signals of the blade considering environmental factors, and includes multiple strain monitoring nodes distributed at vulnerable parts of the blade. Each strain monitoring node... The system includes two sets of mutually perpendicular strain monitoring components to monitor the stress state in the length and width directions of the blade. Each set of strain monitoring components includes a monitoring strain sensor and a compensating strain sensor. The monitoring strain sensor is installed on the blade to measure the strain generated during the production and / or transportation process. The compensating strain sensor is installed on a compensation plate made of the same material as the blade to compensate for strain errors caused by ambient temperature. The first control module controls the first wireless transmission module to transmit multidimensional sensor data to the blade condition early warning terminal. The multidimensional sensor data includes strain signals and vibration acceleration signals. The blade condition early warning terminal analyzes and processes the multidimensional sensor data to obtain blade condition characteristic values.

[0006] In an exemplary embodiment of the present invention, the strain monitoring sensor can be a strain gauge, the strain compensation sensor can be a strain compensation gauge, and the strain monitoring gauge, the strain compensation gauge, and the two resistors can be located on the four arms of the Wheatstone bridge to form a strain monitoring circuit.

[0007] In an exemplary embodiment of the present invention, the multidimensional sensing data acquisition terminal may further include a multi-channel data synchronous acquisition unit connected to the multi-node strain monitoring module; the multi-channel data synchronous acquisition unit may include a signal conditioning circuit and a multi-channel synchronous acquisition module, the signal conditioning circuit being used to condition the strain signal to -3.2V to +4.2V, and the multi-channel synchronous acquisition module being used to realize the synchronous acquisition of multiple sets of strain signals.

[0008] In an exemplary embodiment of the present invention, the blade state early warning terminal may include a digital signal processing unit, a positioning module, a second wireless transmission module, and a second control module; the digital signal processing unit includes a preprocessing module, a time-domain conversion module, and a blade state determination module; the preprocessing module is used to perform supplementary processing and full-phase processing on multi-dimensional sensing data to obtain normalized full-phase strain signals and vibration acceleration signals; the time-domain conversion module is used to perform fast Fourier transform on the normalized full-phase strain signals and vibration acceleration signals to obtain multiple frequency domain features of strain signals and frequency domain features of acceleration signals; the blade state determination module is used to input the effective frequency domain features of strain signals and vibration acceleration signals into a joint Kalman filter to obtain blade state feature values; the positioning module is used to acquire the position information of the blade; and the second control module is used to control the communication between the second wireless transmission module and the first wireless transmission module.

[0009] In an exemplary embodiment of the present invention, the blade state determination module may include an optimal estimation submodule and an optimal state joint estimation submodule. The joint Kalman filter includes two sub-Kalman filters and a main Kalman filter. The optimal estimation submodule is used to input the effective frequency domain features of the strain signal and the vibration acceleration signal into their respective sub-Kalman filters, and perform time and state updates to obtain the optimal estimation features of strain and vibration acceleration. The optimal state joint estimation submodule is used to input the optimal estimation features of strain and the optimal estimation features of vibration acceleration into the main Kalman filter, and perform time updates and optimal fusion to obtain blade state feature values.

[0010] In an exemplary embodiment of the present invention, the blade status early warning terminal may further include an early warning unit, which is used to compare the blade status characteristic value and the blade early warning threshold, and generate early warning information when the blade status characteristic value is greater than or equal to the blade early warning threshold.

[0011] In an exemplary embodiment of the present invention, the blade status early warning terminal may further include a display module and a voice broadcast module. The display module is used to display blade status information and / or alarm information, and the voice broadcast module is used to play the blade status information and / or alarm information. The blade status information includes blade status feature values ​​and location information.

[0012] In an exemplary embodiment of the present invention, the blade status early warning terminal may further include a storage module for storing multidimensional sensing data, blade status feature values ​​and location information.

[0013] In an exemplary embodiment of the present invention, the system may further include a cloud server, which is connected to the blade condition monitoring unit for remotely storing blade condition characteristic values ​​and location information.

[0014] In an exemplary embodiment of the present invention, the system may further include a client connected to a cloud server, the client being used to acquire and display blade state characteristic values ​​and position information.

[0015] Another aspect of the present invention provides a method for monitoring the condition of wind turbine blades during production and transportation. The monitoring method includes: acquiring multidimensional sensing data, which includes vibration acceleration signals at different locations on the blade and strain signals at vulnerable parts of the blade considering environmental factors. The strain signal is calculated using the following formula: Wherein, ΔU O U is the output voltage of the strain monitoring component. ref As the reference voltage, R S1 To monitor the resistance value of the strain sensor, R S1 To compensate for the resistance value of the strain sensor, the multidimensional sensing data is preprocessed to obtain normalized full-phase strain and vibration acceleration signals. The preprocessing includes supplementary processing and full-phase processing. The normalized full-phase strain and vibration acceleration signals are then subjected to Fast Fourier Transform to obtain multiple frequency domain features of the strain and acceleration signals. The effective frequency domain features of the strain and vibration acceleration signals are then input into a joint Kalman filter to obtain blade state feature values.

[0016] In another exemplary embodiment of the present invention, the step of inputting the effective frequency domain features of the strain signal and the vibration acceleration signal into a joint Kalman filter to obtain blade state feature values ​​may include: inputting the effective frequency domain features of the strain signal and the vibration acceleration signal into their respective sub-Kalman filters, performing time and state updates to obtain optimal estimated features of strain and optimal estimated features of vibration acceleration; inputting the optimal estimated features of strain and optimal estimated features of vibration acceleration into a main Kalman filter, performing time updates and optimal fusion to obtain blade state feature values.

[0017] In another exemplary embodiment of the present invention, the monitoring method may further include: comparing the leaf state characteristic value with the leaf warning threshold; if the leaf state characteristic value is less than the leaf warning threshold, then continuing to monitor the leaf state; if the leaf state characteristic value is greater than or equal to the leaf warning threshold, then generating warning information.

[0018] The present invention has at least the following technical effects through the technical solution provided by the present invention:

[0019] (1) The strain monitoring circuit used in this invention can compensate for the strain signal and effectively suppress the influence of environmental temperature and other factors on the strain gauge; the improved fast Fourier transform algorithm is used to extract the frequency domain features of the strain signal and vibration acceleration signal, which can effectively solve the spectrum leakage and picket fence effect of the traditional fast Fourier transform. By fusing the strain signal and acceleration signal, the full phase information monitoring of the blade state is realized. Compared with the existing monitoring system, the blade state estimation accuracy can be effectively improved, thereby realizing comprehensive monitoring and accurate early warning of the blade state during the stages of mold closing, flipping, hoisting, transfer and grinding in the blade production process and during transportation.

[0020] (2) This invention fully considers the performance and cost of the system microprocessor. The multi-dimensional sensor data acquisition terminal and the blade status early warning terminal are independent of each other. The control commands and digital signal processing are separated, and data interaction is achieved by wireless communication, which effectively improves the system's working efficiency.

[0021] (3) By setting different levels of warning thresholds and adopting a graded audio-visual dual warning method, this invention can effectively improve the human-computer interaction experience and achieve accurate response to abnormal blade conditions.

[0022] (4) The present invention uses a combination of local storage and cloud storage to store blade status information. The blade status stored locally is the raw data and abnormal status information collected by the sensor module, which facilitates the subsequent analysis of the blade transfer and transportation status and the formation of a blade transportation route guidance report, thereby formulating corresponding measures for locations prone to abnormalities to effectively reduce blade damage. The data stored in the cloud is the blade status abnormality alarm level and location information, which facilitates remote monitoring of blade abnormal status.

[0023] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0024] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0025] Figure 1 This is a schematic diagram of the structure of the wind turbine blade condition monitoring system provided in an embodiment of the present invention;

[0026] Figure 2 This is a structural diagram of the strain monitoring circuit provided in an embodiment of the present invention;

[0027] Figure 3 An equivalent circuit diagram of the strain monitoring circuit provided in the embodiments of the present invention;

[0028] Figure 4A This is a topology diagram of the signal conditioning circuit provided in an embodiment of the present invention;

[0029] Figure 4B A topology diagram of a reference voltage circuit provided in an embodiment of the present invention;

[0030] Figure 5 This is a longitudinal sectional view of a wind turbine blade provided in an embodiment of the present invention;

[0031] Figure 6 A distribution diagram of the triaxial vibration acceleration sensor provided in an embodiment of the present invention;

[0032] Figure 7 A data processing flowchart provided for an embodiment of the present invention.

[0033] Explanation of reference numerals in the attached figures

[0034] 1-Multi-dimensional sensor data acquisition terminal; 2-Blade status early warning terminal; 3-Cloud server; 4-Client; 101-Microprocessor; 102-Multi-dimensional sensor data acquisition unit; 103-LoRa wireless transmission module 1; 104-Memory 1; 105-Multi-node strain monitoring module; 106-Multi-channel data synchronous acquisition module; 107-Vibration acceleration acquisition module; 108-Signal conditioning circuit; 109-Multi-channel high-speed ADC synchronous acquisition module; 201-Multi-core embedded microcontroller; 202-LoRa wireless data transmission module 2; 203-Digital signal processor; 204-LCD liquid crystal display module; 205-Voice broadcast module; 206-NB-IoT data transmission module; 207-GPS module; 208-Memory 2; 401-PC host computer; 402-Mobile client. Detailed Implementation

[0035] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0036] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0037] In this invention, unless otherwise stated, directional terms such as "upper," "lower," "top," and "bottom" are generally used to describe the relative positional relationships of components in relation to the directions shown in the accompanying drawings or in relation to vertical, perpendicular, or gravitational directions. Terms such as "first" and "second" are used merely for ease of description and distinction and should not be construed as indicating or implying relative importance.

[0038] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integrated connection; they can refer to a direct connection or an indirect connection; they can refer to a wired connection or a wireless connection. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0039] It should be noted that a blade is the main component with an aerodynamic shape that receives wind energy and causes the wind energy to rotate around its axis; the blade root is the component in the wind turbine that connects the blade and the hub; the blade tip is the point on the blade that is farthest from the wind energy rotation axis; the leading edge is the frontmost point of the airfoil in the direction of rotation; the trailing edge is the rearmost point of the airfoil in the direction of rotation; and the blade length is the maximum length of the blade measured along the line connecting the pressure centers in the spanwise direction.

[0040] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0041] Example 1

[0042] The first embodiment of the present invention provides a method for monitoring the condition of wind turbine blades during production and transportation, the method comprising the following steps:

[0043] Step S101: Collect multidimensional sensing data, which includes vibration acceleration signals at different locations on the blade and strain signals of vulnerable parts of the blade under environmental factors.

[0044] The formula for calculating the strain signal is:

[0045] Wherein, ΔU O U is the output voltage of the strain monitoring component. ref As the reference voltage, R S1 To monitor the resistance value of the strain sensor, R S1 To compensate for the resistance value of the strain sensor.

[0046] Step S102: Preprocess the multidimensional sensing data to obtain the normalized full-phase strain signal and vibration acceleration signal.

[0047] The preprocessing includes supplementary processing and full-phase processing; the full-phase processing includes weighted processing, shift-addition and normalization processing.

[0048] Step S103: Perform a fast Fourier transform on the normalized full-phase strain signal and vibration acceleration signal to obtain multiple frequency domain features of the strain signal and the acceleration signal.

[0049] Step S104: Input the effective frequency domain characteristics of the strain signal and vibration acceleration signal into the joint Kalman filter to obtain the blade state characteristic value.

[0050] For example, the process of inputting the effective frequency domain characteristics of the strain signal and vibration acceleration signal into a joint Kalman filter to obtain the blade state characteristic value includes, but is not limited to, the following sub-steps S1041 to S1042.

[0051] Sub-step S1041: Input the effective frequency domain features of the strain signal and vibration acceleration signal into their respective sub-Kalman filters, and perform time and state updates to obtain the optimal estimated features of strain and vibration acceleration.

[0052] Sub-step S1042: Input the optimal estimated features of strain and vibration acceleration into the main Kalman filter, perform time update and optimal fusion to obtain blade state feature values.

[0053] Furthermore, the monitoring method may also include: comparing the leaf state characteristic value with the leaf warning threshold; if the leaf state characteristic value is less than the leaf warning threshold, then continue monitoring the leaf state; if the leaf state characteristic value is greater than or equal to the leaf warning threshold, then generate warning information.

[0054] Example 2

[0055] A second embodiment of the present invention provides a wind turbine blade condition monitoring system for use during production and transportation. The system consists of a multi-dimensional sensor data acquisition terminal and a blade condition early warning terminal.

[0056] The multidimensional sensing data acquisition terminal includes a vibration acceleration acquisition module, a multi-node strain monitoring module, a first control module, and a first wireless transmission module.

[0057] The vibration acceleration acquisition module includes multiple triaxial vibration acceleration sensors mounted on the SS surface of the blade, used to acquire vibration acceleration signals at different locations on the blade.

[0058] The multi-node strain monitoring module is used to acquire strain signals of the blade under environmental factors, including multiple strain monitoring nodes distributed in vulnerable areas of the blade. Each strain monitoring node includes two sets of mutually perpendicular strain monitoring components to monitor the stress state in the length and width directions of the blade. Each set of strain monitoring components includes a monitoring strain sensor and a compensation strain sensor. The monitoring strain sensor is installed on the blade to measure the strain generated during the production and / or transportation process; the compensation strain sensor is installed on a compensation plate made of the same material as the blade to measure the strain error caused by ambient temperature. To achieve the best compensation effect, the material of the compensation plate should be the same as the monitoring target and should be in a stress-free state; for example, the compensation plate can be made of fiberglass.

[0059] For example, the strain monitoring sensor is a strain gauge, and the compensating strain sensor is a compensating strain gauge. Each strain monitoring assembly may include one strain gauge, one compensating strain gauge, and two resistors. The strain gauge, compensating strain gauge, and two resistors are respectively located on the four arms of a Wheatstone bridge. The strain gauge is opposite to one resistor, and the compensating strain gauge is opposite to the other resistor, forming a strain monitoring circuit. The output voltage of the strain monitoring circuit is the strain signal.

[0060] Of course, the present invention is not limited to this. Other strain measurement sensors or stress measurement sensors can be selected to monitor the strain sensor, as long as they can be used to monitor the blade deformation. Similarly, other strain measurement sensors or stress measurement sensors can be selected to compensate for the blade deformation.

[0061] The first wireless transmission module is connected to the vibration acceleration acquisition module and the multi-node strain monitoring module respectively, and is used to transmit strain signals and vibration acceleration signals.

[0062] The first control module is used to control the first wireless transmission module to transmit strain signals and vibration acceleration signals to the blade condition early warning terminal.

[0063] The blade condition early warning terminal is connected to a multi-dimensional sensor data acquisition terminal to analyze and process the multi-dimensional sensor data to obtain blade condition characteristic values. The multi-dimensional sensor data includes strain signals and vibration acceleration signals.

[0064] For example, the blade condition early warning terminal may include a digital signal processing unit, a second wireless transmission module, and a second control module. The second wireless transmission module is wirelessly connected to the first wireless transmission module and is used to receive multi-dimensional sensor data. The second control module controls the communication between the second and first wireless transmission modules to achieve data interaction between the multi-dimensional sensor data acquisition terminal and the blade condition early warning terminal. The digital signal processing unit processes the multi-dimensional sensor data to obtain blade condition characteristic values.

[0065] For example, a digital signal processing unit may include a preprocessing module, a time-domain conversion module, and a blade state determination module.

[0066] The preprocessing module is connected to the second wireless transmission module and is used to supplement and process the multidimensional sensing data in full phase to obtain normalized full-phase strain signals and vibration acceleration signals.

[0067] The time-domain transformation module is connected to the preprocessing module and is used to perform fast Fourier transform on the normalized full-phase strain signal and vibration acceleration signal to obtain multiple frequency domain features of strain signal and acceleration signal.

[0068] The blade state determination module is connected to the time-domain transformation module and is used to input the effective frequency domain characteristics of the strain signal and vibration acceleration signal into the joint Kalman filter to obtain the blade state characteristic values. The joint Kalman filter includes two sub-Kalman filters and one main Kalman filter. The blade state determination module may include an optimal estimation submodule and an optimal state joint estimation submodule. The optimal estimation submodule is used to input the effective frequency domain characteristics of the strain signal and vibration acceleration signal into their respective sub-Kalman filters, perform time and state updates, and obtain the optimal estimated characteristics of strain and vibration acceleration. The optimal state joint estimation submodule is connected to the optimal estimation submodule and is used to input the optimal estimated characteristics of strain and vibration acceleration into the main Kalman filter, perform time updates and optimal fusion, and obtain the blade state characteristic values.

[0069] In this embodiment, the blade status early warning terminal is a mobile terminal, mainly provided for use by operators during blade production and transportation.

[0070] Furthermore, the multi-dimensional sensing data acquisition terminal may also include a multi-channel data synchronous acquisition unit connected to the multi-node strain monitoring module. The multi-channel data synchronous acquisition unit may include a signal conditioning circuit and a multi-channel synchronous acquisition module. The signal conditioning circuit conditions the strain signal to -3.2V to +4.2V to meet the acquisition requirements of the multi-channel synchronous acquisition module. The multi-channel synchronous acquisition module is used to achieve synchronous acquisition of multiple sets of strain signals.

[0071] Furthermore, the blade condition early warning terminal may also include a positioning module, which is used to acquire the blade's location information. Acquiring location information helps to pinpoint the location where the blade anomaly occurs, allowing operators to develop corresponding measures for areas prone to anomalies during transportation to effectively reduce blade damage.

[0072] Compared with existing technologies, this invention makes technical improvements to both the multi-dimensional sensing data acquisition terminal and the blade condition early warning terminal. Specifically, the strain monitoring component in the multi-dimensional sensing data acquisition terminal considers the influence of environmental factors, performs error compensation on the measured strain signal, effectively suppresses the influence of environmental factors such as temperature on the strain gauge, and improves the measurement accuracy of the true strain signal. The digital signal processing unit in the blade condition early warning terminal uses an improved Fast Fourier Transform algorithm to extract the frequency domain features of the strain signal and vibration acceleration signal, which can effectively solve the spectral leakage and picket fence effect existing in the traditional Fast Fourier Transform. This digital signal processing unit achieves full-phase information monitoring of blade condition by fusing the strain signal and acceleration signal, which can effectively improve the accuracy of blade condition estimation.

[0073] Example 3

[0074] The third embodiment of the present invention provides a wind turbine blade condition monitoring and early warning system for use in the production and transportation process.

[0075] Based on the structure of the second embodiment, the monitoring and early warning system also includes an early warning unit in the blade status early warning terminal.

[0076] The early warning unit is connected to the digital signal processing unit to compare the blade state characteristic value with the blade early warning threshold. When the blade state characteristic value is greater than or equal to the blade early warning threshold, an early warning message is generated. The early warning unit can be set with a single blade early warning threshold to indicate blade damage; alternatively, multiple blade early warning thresholds can be set for tiered early warning to specifically indicate the level of blade damage, helping operators generate appropriate control measures.

[0077] Furthermore, the blade status early warning terminal may also include a display module and a voice broadcast module. The display module is used to display blade status information and / or alarm information, and the voice broadcast module is used to play the blade status information and / or alarm information. The blade status information includes blade status characteristic values ​​and location information. For example, the display module may only display alarm information, or it may display blade status information while simultaneously displaying an alarm; the voice broadcast module may only play alarm information, or it may play alarm information while simultaneously displaying blade status information. The display module and the voice broadcast module achieve dual visual and auditory early warning, which can effectively improve the human-computer interaction experience.

[0078] Furthermore, the blade status early warning terminal may also include a storage module, which is used to save multi-dimensional sensor data, blade status characteristic values ​​and location information.

[0079] Example 4

[0080] The fourth embodiment of the present invention provides another wind turbine blade condition monitoring and early warning system for use in the production and transportation process.

[0081] Based on the structure of the third embodiment, this monitoring and early warning system also includes a cloud server and a client. The cloud server is connected to the blade condition monitoring unit and is used to remotely store blade condition characteristic values ​​and location information. The client is connected to the cloud server and is used to acquire and display blade condition characteristic values ​​and location information.

[0082] The data stored on the cloud server mainly consists of blade status anomaly alarm levels and location information, facilitating remote monitoring of blade anomalies. Users can download the blade status anomaly alarm levels and location information from the cloud server via a client, allowing them to check the blade status at any time.

[0083] Example 5

[0084] The fifth embodiment of the present invention provides a wind turbine blade condition monitoring and early warning system for production and transportation processes (hereinafter referred to as the system, see [link]). Figures 1-6 The system includes a multi-dimensional sensor data acquisition terminal 1, a blade status early warning terminal 2, a cloud server 3, and a client 4. The multi-dimensional sensor data acquisition terminal 1 interacts with the blade status early warning terminal 2, and the blade status early warning terminal 2 is connected to the cloud server 3.

[0085] Multi-dimensional sensor data acquisition terminal 1 collects multi-dimensional sensor data from the blades and transmits the data to blade status early warning terminal 2. Blade status early warning terminal 2 analyzes and processes the data to obtain blade status characteristic values, and compares these values ​​with preset blade early warning thresholds to provide early warnings for the wind turbine blades. Simultaneously, blade status early warning terminal 2 transmits the processed data to cloud server 3 for storage. Blade status early warning terminal 2 is a mobile terminal, primarily used by operators during blade production and transportation. Client 4 establishes a connection with cloud server 3 for remotely viewing blade status.

[0086] The multidimensional sensor data acquisition terminal 1 includes a microprocessor 101, a multidimensional sensor data acquisition unit 102, a No. 1 LoRa wireless transmission module 103, and a No. 1 memory 104.

[0087] Specifically, the microprocessor 101 is connected to the multidimensional sensing data acquisition unit 102 and the first memory 104 respectively. While storing the multidimensional sensing data acquired by the multidimensional sensing data acquisition unit 102 in the memory 104, the microprocessor 101 transmits the data to the blade status early warning terminal 2 through the first LORA wireless transmission module 103 (that is, the first wireless transmission module).

[0088] The multidimensional sensing data includes strain signals and vibration acceleration signals. The multidimensional sensing data acquisition unit 102 includes a multi-node strain monitoring module 105, a multi-channel data synchronous acquisition module 106, and a vibration acceleration acquisition module 107. The multi-node strain monitoring module 105 is connected to the multi-channel data synchronous acquisition module 106, and both the multi-channel data synchronous acquisition module 106 and the vibration acceleration acquisition module 107 are connected to the microprocessor 101 (i.e., the first control module). The multi-node strain monitoring module 105 includes multiple strain monitoring nodes (14 in this embodiment), which are distributed in easily damaged parts of the blade and used to collect the blade's strain signals. The strain signals monitored by the multi-node strain monitoring module 105 are processed and acquired by the multi-channel data synchronous acquisition module 106, stored in the first memory 104, and simultaneously transmitted to the blade status early warning terminal 2. The vibration acceleration acquisition module 107 includes multiple triaxial vibration acceleration sensors (4 in this embodiment) distributed on the SS surface of the blade, used to collect the blade's vibration acceleration signals to monitor the blade's vibration state.

[0089] Please refer to Figure 2 Each strain monitoring node includes a first monitoring strain gauge, a second monitoring strain gauge, a first compensation strain gauge, a second compensation strain gauge, and resistors R1 to R4. The first and second monitoring strain gauges are perpendicularly attached to the blade to monitor strain signals in the blade's length and width directions. Two compensation strain gauges serve as reference points, each attached to a 5cm x 5cm fiberglass plate of the same material as the blade. These fiberglass plates with the compensation strain gauges are placed near their respective nodes and are not subjected to stress, ensuring that the compensation strain gauges are only affected by environmental factors and not by external forces.

[0090] The first monitoring strain gauge, the first compensation strain gauge, and resistors R1 to R2 are located on the four arms of a Wheatstone bridge. The first monitoring strain gauge is opposite to resistor R2, and the first compensation strain gauge is opposite to resistor R1, forming a strain monitoring circuit. The intersection of the arm containing resistor R1 and the arm containing the first monitoring strain gauge is connected to pins 2 and 3 of transistor U3 in a reference voltage circuit via a shielded wire. The intersection of the arm containing resistor R2 and the arm containing the first compensation strain gauge is connected to the ground terminal of the reference voltage circuit via a shielded wire. The reference voltage circuit generates a reference voltage for calculating the output voltage of the strain monitoring circuit, and the output voltage of the strain monitoring circuit is the strain signal.

[0091] The second monitoring strain gauge, the second compensation strain gauge, and resistors R3-R4 are located on the four arms of another Wheatstone bridge. The second monitoring strain gauge is opposite to resistor R4, and the second compensation strain gauge is opposite to resistor R3, forming another strain monitoring circuit. The intersection of the bridge arm containing resistor R3 and the bridge arm containing the second monitoring strain gauge is connected to pins 2 and 3 of transistor U3 in another reference voltage circuit through a shielded wire. The intersection of the bridge arm containing resistor R4 and the bridge arm containing the second compensation strain gauge is connected to the ground terminal of this reference voltage circuit through a shielded wire, thus generating a reference voltage. Since strain gauges are easily affected by ambient temperature, compensation strain gauges are used to compensate for ambient temperature, avoiding strain errors caused by ambient temperature. The strain signal measured by the strain monitoring circuit has eliminated the influence of factors such as ambient temperature.

[0092] The specific principle of the strain monitoring circuit is as follows:

[0093] Please refer to Figure 3 The monitoring strain gauge and the compensation strain gauge are respectively equivalent to a variable resistor R. S1 and R S2 The resistance values ​​of resistors R1 and R2 are fixed; the reference voltage is denoted as... The output voltage is denoted as Since the resistances of R1 and R2 remain constant and are unaffected by factors such as ambient temperature, changes in ambient temperature or stress will affect the resistance R. S1 and R S2 The change in resistance value will affect resistors R1, R2, and R S1 and R S2 The changes in resistance are denoted as ΔR1, ΔR2, and ΔR, respectively. S1 and ΔR S2 The change in resistance causes a change in the output voltage of the strain monitoring circuit. The change in output voltage can be expressed as: Since ΔR1 and ΔR2 are always equal to 0, the output voltage change can be simplified to: The compensating strain gauge is not subjected to force and is only affected by ambient temperature. The monitoring strain gauge and the compensating strain gauge are affected by the same ambient temperature; therefore, the resistance changes of the two strain gauges can be considered the same. In the formula… This can be considered as monitoring the resistance change of the strain gauge caused by factors such as ambient temperature, therefore, in the formula... This item monitors the resistance change of the strain gauge caused by stress, and the influence of factors such as ambient temperature has been eliminated.

[0094] The multi-channel data synchronous acquisition module 106 includes a signal conditioning circuit 108 and a multi-channel high-speed ADC synchronous acquisition module 109. Since the strain signal acquired by the strain gauge is in the millivolt range, the signal conditioning circuit conditions the strain signal to -3.2V to +4.2V to meet the acquisition requirements of the multi-channel high-speed ADC synchronous acquisition module 109. Two signal conditioning circuits 108 are provided at each strain monitoring node. The intersection of the bridge arm containing the first monitoring strain gauge and the bridge arm containing the first compensation strain gauge, as well as the intersection of the bridge arm containing resistor R1 and the bridge arm containing resistor R2, are connected to the positive and negative input terminals of one of the signal conditioning circuits via shielded wires, respectively. Similarly, the intersection of the bridge arm containing the second monitoring strain gauge and the bridge arm containing the second compensation strain gauge, as well as the intersection of the bridge arm containing resistor R3 and the bridge arm containing resistor R4, are connected to the positive and negative input terminals of the other signal conditioning circuit via shielded wires.

[0095] Please refer to Figure 4A The signal conditioning circuit 108 includes two operational amplifiers U1-U2, a capacitor C1, and resistors R5-R12. One end of resistor R5 is connected to the positive input terminal of the signal, and the other ends of resistor R5 and one end of resistor R7 are both connected to the non-inverting input terminal of operational amplifier U1. The other end of resistor R7 is grounded. One end of resistor R6 is connected to the negative input terminal of the signal, and the other ends of resistor R6 and one end of resistor R8 are both connected to the inverting input terminal of operational amplifier U1. The other end of resistor R8 is connected to the output terminal of operational amplifier U1. The acquired strain signal is amplified by operational amplifier U1. The output terminal of operational amplifier U1 is also connected to one end of resistor R9, and the other end of resistor R9 is connected to one end of capacitor C1. The other end of capacitor C1 is grounded. Resistor R9 and capacitor C1 together form a first-order RC low-pass filter to filter the amplified strain signal. The other end of resistor R9 is connected to the non-inverting input of operational amplifier U2. The inverting input of operational amplifier U2 is connected to one end of resistor R10 and one end of resistor R11, respectively. The other end of resistor R10 is grounded. The other end of resistor R11 and one end of resistor R12 are both connected to the output of operational amplifier U2. The other end of resistor R12 serves as the signal output terminal, connected to the multi-channel high-speed ADC synchronous acquisition module. The signal conditioning circuit and the reference potential circuit are integrated on the same PCB board, with the interface led out from terminal P1 on the PCB board.

[0096] Please refer to Figure 4BThe reference voltage circuit includes transistor U3, resistor R13, and capacitor C2. Pin 1 of transistor U3 is grounded. Pins 2 and 3 of transistor U3 are connected to the intersection of the bridge arm containing resistor R1 and the bridge arm containing strain gauge 1, or the intersection of the bridge arm containing resistor R3 and the bridge arm containing strain gauge 2. Pin 3 of transistor U3 is connected to one end of resistor R13 and one end of capacitor C2. The other end of resistor R13 is connected to the power supply, and the other end of capacitor C2 is grounded.

[0097] The blade status early warning terminal 2 includes a multi-core embedded microcontroller 201, a second LoRa wireless data transmission module 202, a digital signal processor 203, an LCD display module 204, a voice broadcast module 205, an NBIOT data transmission module 206, a GPS module 207, and a second memory 208. The multi-core embedded microcontroller 201 (i.e., the second control module) acts as the main controller of the blade status early warning terminal 2, controlling the communication between the second LoRa wireless data transmission module 202 (i.e., the second wireless transmission module) and the first LoRa wireless transmission module 103, enabling data interaction between the multi-dimensional sensor data acquisition terminal 1 and the blade status early warning terminal 2. The blade status early warning terminal 2 transmits data to the cloud server 3 for storage via the NBIOT data transmission module 206. The digital signal processor 203 (i.e., the digital signal processing unit) processes the multi-dimensional sensor data and provides early warnings of the blade status. The GPS module 207 (i.e., the positioning module) acquires the blade position, and the second memory 208 serves as local storage for storing blade status and position information. The LCD display module 204 and the voice broadcast module 205 are used to achieve dual visual and auditory warnings.

[0098] Client 4 includes a PC host computer 401 and a mobile client 402. It connects to the API interface of cloud server 3 through an application or mini-program to realize cloud connection between client 4 and cloud server 3, so as to facilitate remote viewing of blade status.

[0099] The locations of blades prone to damage are determined based on experience. For example, the most vulnerable areas are near the tooling, which is mainly located at a distance of 53m to 55m from the blade root on the leading and trailing edge webs. Taking a 95m long blade as an example, two strain monitoring nodes are installed at positions of 55m, 54m, and 53m from the blade root on the leading and trailing edge webs, respectively. One strain monitoring node is installed on each of the upper and lower main beams at distances of 44m, 34m, 24m, and 14m from the blade root. (See [reference needed]). Figure 5 Triaxial vibration acceleration sensors were installed at positions 95m, 55m, 34m, and 14m from the blade root on the SS surface of the blade, respectively. (See [reference needed]). Figure 6 .

[0100] As the main controller of the multi-dimensional sensing data acquisition terminal 1, the microprocessor 101 uses an FPGA. The FPGA has unique parallel processing capabilities and is equipped with a multi-channel high-speed ADC synchronous acquisition module, which can achieve strain signal acquisition of up to 65MSPS.

[0101] Please refer to Figure 7 The digital signal processor processes data according to the following steps:

[0102] Step 1: Preprocess the multidimensional sensing data, including supplementary processing and full-phase processing.

[0103] Since the number of signal sampling points is limited, interpolation is used to supplement the multidimensional sensing data, thereby effectively suppressing the picket fence effect of the fast Fourier transform and obtaining the supplemented strain signal and vibration acceleration signal. Full-phase processing is then performed on the supplemented strain signal and vibration acceleration signal, including weighting, shifting and adding, and normalization.

[0104] Taking strain signals as an example, the sampling frequency and the original frequency of the signal are not synchronized during the sampling process, which causes the phase of the periodic sampled signal to be discontinuous at the beginning and end. This results in the presence of energy spectrum around the main spectrum, i.e., spectral leakage, which will have a significant impact on the signal calculation results. Therefore, the supplemented strain signal is weighted using a window function. The window function is a scalar multiplication in the time domain and a convolution in the frequency domain. Here, the Hanning window is used to suppress the influence of spectral leakage.

[0105] The weighted strain signals are shifted and added at intervals of N to achieve full-phase processing of the strain signals. Specifically, the 1st strain signal is added to the 1025th strain signal, the 2nd strain signal is added to the 1026th strain signal, the 3rd strain signal is added to the 1024th strain signal, and so on, until 1024 full-phase strain signals are obtained; where N is the number of Fourier transform points.

[0106] Since the strain signals after full-phase processing have large numerical differences, which will affect the effect of fast Fourier transform, normalization processing is performed to obtain normalized full-phase strain signals; similarly, normalized full-phase vibration acceleration signals are obtained.

[0107] Step 2: Perform Fast Fourier Transform on the normalized full-phase strain signal and vibration acceleration signal respectively to obtain multiple frequency domain features of the strain signal and the acceleration signal, realizing the conversion of the signal from the time domain to the frequency domain; extract the effective frequency domain features of the strain signal and the vibration acceleration signal from the multiple frequency domain features of the strain signal and the vibration acceleration signal respectively, that is, retain the frequency domain features of the strain signal and the vibration acceleration signal at a certain frequency; the effective frequency domain features of the strain signal and the vibration acceleration signal refer to the frequency domain features that reflect the true strain and vibration state of the blade respectively.

[0108] Step 3: Input the effective frequency domain features of the strain signal and vibration acceleration signal into the joint Kalman filter to realize blade state estimation and obtain the blade state feature value, which is a numerical value.

[0109] The blade condition characteristic value is compared with a preset blade warning threshold. If the blade condition characteristic value is greater than or equal to the preset threshold, it indicates blade damage. A dual visual and auditory warning is then issued via an LCD display module and a voice broadcast module to alert the operator. If the blade condition characteristic value is less than the preset threshold, it indicates no blade damage, and blade condition monitoring continues. Different levels of blade warning thresholds can be set to classify blade condition. For example, levels could be defined as normal, potentially damaged, slightly damaged, and severely damaged.

[0110] The joint Kalman filter comprises two sub-Kalman filters and one master Kalman filter. The effective frequency domain features of the strain and vibration acceleration signals are input into their respective sub-Kalman filters for time and state updates, achieving optimal estimates of strain and vibration acceleration, resulting in optimal strain and vibration acceleration estimation features. These two optimal estimation features are then input into the master Kalman filter for time updates and optimal fusion, achieving joint estimation of the blade's optimal state and obtaining the blade state feature values.

[0111] The interpolation operations, fast Fourier transform, and Kalman filter estimation described above are all existing technologies and are well known to those skilled in the art, so they will not be elaborated upon here.

[0112] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0113] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0114] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A wind turbine blade condition monitoring system for use in production and transportation processes, characterized in that, The system includes a multi-dimensional sensor data acquisition terminal and a blade status early warning terminal; The multi-dimensional sensing data acquisition terminal includes a vibration acceleration acquisition module, a multi-node strain monitoring module, a first control module, and a first wireless transmission module; wherein... The vibration acceleration acquisition module includes multiple triaxial vibration acceleration sensors mounted on the SS surface of the blade, used to acquire vibration acceleration signals at different positions on the blade; The multi-node strain monitoring module is used to collect the strain signal of the blade under environmental factors. It includes multiple strain monitoring nodes distributed in the vulnerable parts of the blade. Each strain monitoring node includes two sets of strain monitoring components arranged perpendicularly to each other to monitor the stress state in the length and width directions of the blade. Each set of strain monitoring components includes a monitoring strain sensor and a compensation strain sensor. The monitoring strain sensor is set on the blade to measure the strain generated by the blade during the production process and / or transportation. The compensation strain sensor is set on a compensation plate of the same material as the blade to compensate for the strain error caused by the ambient temperature. The first control module is used to control the first wireless transmission module to transmit multi-dimensional sensor data to the blade status early warning terminal. The multi-dimensional sensor data includes strain signals and vibration acceleration signals. The blade status early warning terminal is used to analyze and process multi-dimensional sensor data to obtain blade status characteristic values; the blade status early warning terminal includes a digital signal processing unit, a positioning module, a second wireless transmission module, and a second control module. The digital signal processing unit includes a preprocessing module, a time-domain transformation module, and a blade state determination module. The preprocessing module performs supplementary and full-phase processing on the multi-dimensional sensing data to obtain normalized full-phase strain and vibration acceleration signals. The time-domain transformation module performs fast Fourier transform on the normalized full-phase strain and vibration acceleration signals to obtain multiple frequency domain features of the strain and acceleration signals. The blade state determination module inputs the effective frequency domain features of the strain and vibration acceleration signals into a joint Kalman filter to obtain blade state feature values. The module includes an optimal estimation submodule and an optimal state joint estimation submodule. The joint Kalman filter includes two sub-Kalman filters and one main Kalman filter. The optimal estimation submodule is used to input the effective frequency domain features of the strain signal and the vibration acceleration signal into their respective sub-Kalman filters, perform time and state updates, and obtain the optimal estimation features of strain and vibration acceleration. The optimal state joint estimation submodule is used to input the optimal estimation features of strain and vibration acceleration into the main Kalman filter, perform time updates and optimal fusion, and obtain the blade state feature value. The positioning module is used to obtain the position information of the blade; The second control module is used to control the communication between the second wireless transmission module and the first wireless transmission module.

2. The wind turbine blade condition monitoring system for production and transportation processes according to claim 1, characterized in that, The strain monitoring sensor is a strain gauge, and the compensation strain sensor is a compensation strain gauge. The strain gauge, the compensation strain gauge, and the two resistors are located on the four arms of the Wheatstone bridge to form a strain monitoring circuit.

3. The wind turbine blade condition monitoring system for production and transportation processes according to claim 1, characterized in that, The blade status early warning terminal also includes an early warning unit, which is used to compare the blade status characteristic value with the blade early warning threshold, and generate early warning information when the blade status characteristic value is greater than or equal to the blade early warning threshold.

4. The wind turbine blade condition monitoring system for production and transportation processes according to claim 3, characterized in that, The blade status early warning terminal also includes a display module and a voice broadcast module. The display module is used to display blade status information and / or early warning information, and the voice broadcast module is used to play blade status information and / or early warning information. The blade status information includes blade status feature values ​​and location information.

5. The wind turbine blade condition monitoring system for production and transportation processes according to claim 1, characterized in that, The system also includes a cloud server and a client. The cloud server is connected to the blade status early warning terminal and is used to remotely store blade status characteristic values ​​and location information. The client connects to the cloud server to obtain and display blade status characteristic values ​​and location information.

6. A method for monitoring the condition of wind turbine blades during production and transportation, characterized in that, The monitoring method is implemented using the wind turbine blade condition monitoring system according to any one of claims 1 to 5, comprising: Multidimensional sensor data is collected, including vibration acceleration signals at different locations on the blade and strain signals of vulnerable parts of the blade considering environmental factors. The strain signal is calculated using the following formula: , where Δ U O The output voltage of the strain monitoring component, U ref For reference voltage, R S1 To monitor the resistance value of the strain sensor, R S2 To compensate for the resistance value of the strain sensor; for R S1 The change in resistance; for R S2 The change in resistance; The multidimensional sensing data is preprocessed to obtain normalized full-phase strain signals and vibration acceleration signals. The preprocessing includes supplementary processing and full-phase processing. Fast Fourier transform is performed on the normalized full-phase strain signal and vibration acceleration signal to obtain multiple frequency domain features of strain signal and acceleration signal. The effective frequency domain characteristics of the strain signal and vibration acceleration signal are input into the joint Kalman filter to obtain the blade state characteristic values.

7. The method for monitoring the condition of wind turbine blades during production and transportation as described in claim 6, characterized in that, The step of inputting the effective frequency domain characteristics of the strain signal and vibration acceleration signal into a joint Kalman filter to obtain blade state characteristic values ​​includes: The effective frequency domain features of the strain signal and the vibration acceleration signal are input into their respective sub-Kalman filters, and time and state updates are performed to obtain the optimal estimated features of strain and vibration acceleration. The optimal estimated features of strain and vibration acceleration are input into the main Kalman filter for time updating and optimal fusion to obtain the blade state feature values.

8. The method for monitoring the condition of wind turbine blades during production and transportation according to claim 6, characterized in that, The monitoring method also includes: Compare leaf condition characteristic values ​​with leaf warning thresholds; If the leaf condition characteristic value is less than the leaf warning threshold, the leaf condition will continue to be monitored. If the leaf condition characteristic value is greater than or equal to the leaf warning threshold, a warning message will be generated.

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