A state monitoring method and device for a new energy wind turbine

Abstract

The application relates to the technical field of wind power generation, and discloses a state monitoring method and device for a new energy wind power generator. The application establishes a direct physical correlation between sound velocity attenuation and icing thickness by analyzing the propagation time delay change of sound waves in blade materials. The axial position of icing occurrence is directly derived by calculating the time delay gradient ratio of different blade sections (blade tip, blade middle and blade root) and utilizing the spatial characteristics of the sound wave propagation path difference. Compared with the traditional monitoring methods based on temperature threshold or vibration amplitude, the sound wave time delay is extremely sensitive to the micro changes of the material boundary layer and can capture the micro structural changes formed in the initial stage of icing.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, and specifically to a method and device for condition monitoring of new energy wind turbines. Background Technology

[0002] Current methods for monitoring icing on wind turbine blades primarily rely on temperature thresholding or vibration spectrum analysis. Temperature-based methods, based on the latent heat released during the phase change of icing, require the ice layer to accumulate to a certain thickness for the sensor to reach a preset threshold, resulting in a response lag. Vibration-based methods detect icing by capturing shifts in the blade's natural frequency or abnormal amplitude; however, minor changes in structural stiffness caused by icing are easily masked by the broadband mechanical vibration noise generated by the rotor's rotation, especially under low wind speed conditions where the signal-to-noise ratio deteriorates sharply. Both methods lack effective detection capabilities for early-stage icing, leading to delays in the activation of de-icing systems and failing to prevent the risk of aerodynamic performance degradation and structural damage caused by icing thickening. Summary of the Invention

[0003] This invention provides a condition monitoring method and device for new energy wind turbines to solve the problem that existing temperature threshold methods and vibration spectrum analysis methods lack effective sensing capabilities for early icing.

[0004] In a first aspect, the present invention provides a condition monitoring method for a new energy wind turbine generator, the method comprising:

[0005] Using the rotational speed signal at the hub as a synchronization reference, the piezoelectric exciter at the leading edge of the blade is triggered to output an excitation signal. At the same time, acoustic wave data captured by the acoustic emission sensor is collected at the corresponding propagation paths from the blade tip, blade middle, blade root to the blade root.

[0006] For each propagation path from the leaf tip, leaf middle, leaf root to the leaf root, the sound wave propagation delay is extracted. Based on continuous rotation period data, the change in sound wave propagation delay of each path within two adjacent rotation periods is calculated.

[0007] Establish a correlation model between sound speed and icing thickness, and calculate the icing location determination coefficient based on the time delay changes of each path from the leaf tip, leaf middle, leaf root to the leaf root.

[0008] The icing state and region are determined based on the time delay change of each propagation path, the time delay change rate per unit rotation period, and the icing location determination coefficient.

[0009] This invention provides a condition monitoring method for new energy wind turbines. By analyzing the propagation time delay of sound waves in the blade material, this application establishes a direct physical correlation between sound velocity attenuation and icing thickness. By calculating the time delay gradient ratios of different blade sections (tip, middle, and root), and utilizing the spatial characteristics of sound wave propagation path differences, the axial location of icing is directly derived. Compared to traditional monitoring methods based on temperature thresholds or vibration amplitudes, sound wave time delay is extremely sensitive to microscopic changes in the material boundary layer, enabling the capture of minute structural changes formed in the early stages of icing.

[0010] In one optional implementation, the icing state and region are determined based on the time delay change of each propagation path, the time delay change rate per unit rotation period, and the icing location determination coefficient, including:

[0011] Determine whether the time delay change of each propagation path exceeds the first preset value, and whether the time delay change rate within a unit rotation period is greater than the second preset value;

[0012] If the time delay change of each propagation path exceeds the first preset value, and the time delay change rate within a unit rotation cycle is greater than the second preset value, then the icing state and region are determined based on the relationship between the icing location determination coefficient and the preset threshold.

[0013] In one optional implementation, the icing state and region are determined based on the relationship between the icing location determination coefficient and a preset threshold, including:

[0014] When the icing location determination coefficient is not less than the first threshold but less than the second threshold, it is determined that ice has formed in the leaf, and the second threshold is greater than the first threshold.

[0015] When the icing location determination coefficient is not less than the second threshold but less than the third threshold, it is determined that the leaf tip is icing, and the third threshold is greater than the second threshold;

[0016] When the icing location determination coefficient is less than the first threshold or not less than the third threshold, the leaf root is determined to be icing.

[0017] In one optional implementation, the icing state and region are determined based on the time delay change of each propagation path, the time delay change rate per unit rotation period, and the icing location determination coefficient. The method further includes:

[0018] If the time delay change of each propagation path does not exceed the first preset value at the same time, or the time delay change rate within a unit rotation cycle is not greater than the second preset value, then the blade is determined to be in normal condition.

[0019] In one optional implementation, the method further includes:

[0020] Preprocess the acoustic wave data captured by the acoustic emission sensor.

[0021] In one optional implementation, the preprocessing of the acoustic wave data captured by the acoustic emission sensor includes:

[0022] For each corresponding propagation path from the leaf tip, leaf middle, leaf root to the root of the leaf blade, adaptive bandpass filtering is applied to the acoustic wave data captured by the acoustic emission sensor;

[0023] Using the rotational speed signal at the wheel hub as a reference, a rotational speed synchronous averaging algorithm is used to align and average the sound wave data of a continuous preset number of revolutions according to the rotation angle.

[0024] In one optional implementation, the method further includes:

[0025] Based on the icing determination results, a heat map of the icing location distribution on the blade surface is generated on the monitoring interface.

[0026] When icing is detected in multiple complete rotation cycles, a graded early warning mechanism is triggered.

[0027] Secondly, the present invention provides a condition monitoring device for a new energy wind turbine generator, the device comprising:

[0028] The acoustic wave acquisition module is used to trigger the piezoelectric exciter at the leading edge of the blade to output an excitation signal, using the rotational speed signal at the hub as a synchronization reference. At the same time, it acquires acoustic wave data captured by the acoustic emission sensor along the corresponding propagation paths from the blade tip, middle of the blade, and blade root to the blade root.

[0029] The first calculation module is used to extract the sound wave propagation delay for each propagation path from the leaf tip, leaf middle, leaf root to the leaf root, and calculate the change in sound wave propagation delay for each path within two adjacent rotation cycles based on continuous rotation cycle data.

[0030] The second calculation module is used to establish a correlation model between sound speed and icing thickness, and to calculate the icing location determination coefficient based on the time delay changes of each path from the leaf tip, leaf middle, leaf root to the leaf root.

[0031] The status judgment module is used to determine the icing status and area based on the time delay change of each propagation path, the time delay change rate within a unit rotation cycle, and the icing location determination coefficient.

[0032] This invention provides a condition monitoring device for new energy wind turbines. By analyzing the propagation time delay of sound waves in the blade material, this application establishes a direct physical correlation between sound velocity attenuation and icing thickness. By calculating the time delay gradient ratios of different blade sections (blade tip, blade middle, and blade root), and utilizing the spatial characteristics of sound wave propagation path differences, the axial location of icing is directly derived. Compared to traditional monitoring methods based on temperature thresholds or vibration amplitudes, sound wave time delay is extremely sensitive to microscopic changes in the material boundary layer, enabling the capture of minute structural changes formed in the early stages of icing.

[0033] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the state monitoring method for new energy wind turbines described in the first aspect or any corresponding embodiment thereof.

[0034] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the condition monitoring method for a new energy wind turbine generator described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating a condition monitoring method for a new energy wind turbine according to an embodiment of the present invention.

[0037] Figure 2 This is a structural block diagram of a condition monitoring device for a new energy wind turbine according to an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0041] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0042] According to an embodiment of the present invention, a method for monitoring the condition of a new energy wind turbine is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0043] This embodiment provides a condition monitoring method for new energy wind turbine generators. Figure 1 This is a flowchart of a condition monitoring method for new energy wind turbines according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0044] Step S1: Using the rotational speed signal at the hub as a synchronization reference, the piezoelectric exciter at the leading edge of the blade is triggered to output an excitation signal. At the same time, acoustic wave data captured by the acoustic emission sensor is collected at the corresponding propagation paths from the blade tip, blade middle, blade root to the blade root.

[0045] In this embodiment, piezoelectric exciters (resonant frequency 200kHz) are embedded at the blade tip, middle, and root positions on the leading edge of the blade to actively emit detection sound waves; three broadband acoustic emission sensors (frequency response range 50-300kHz, sampling rate ≥1MHz) are installed at the blade root (0.5m from the hub) to receive sound wave signals from each propagation path from the blade tip, middle, and root; and a high-precision speed encoder (resolution 0.01°) is installed at the hub to obtain high-precision position signals of the blade rotation.

[0046] Signal acquisition uses the position signal from the speed encoder as the core synchronization reference. The synchronization controller receives this position signal in real time. When the blade rotates one revolution and reaches a specific reference angle, the speed encoder outputs a trigger pulse. After receiving the pulse, the synchronization controller immediately instructs the piezoelectric exciters at the blade tip, middle, and root to synchronously generate short 200kHz acoustic pulses. The acoustic waves propagate inward along the blade structure. At the same time, three broadband acoustic emission sensors at the blade root synchronously start high-speed acquisition, recording the acoustic wave signals after propagation through the blade material along each path, thus completely capturing the acoustic wave data of each corresponding propagation path from the blade tip, middle, and root to the root.

[0047] This step involves precisely deploying the aforementioned core components at key locations on the blades and hub, achieving millisecond-level time synchronization based on the rotation angle, and providing high-precision spatiotemporally aligned raw signals for subsequent signal analysis.

[0048] Step S2: Preprocess the acoustic wave data captured by the acoustic emission sensor.

[0049] In this embodiment, step S2 includes:

[0050] Step S21: For each corresponding propagation path from the leaf tip, leaf middle, leaf root to the root of the leaf blade, perform adaptive bandpass filtering on the acoustic wave data captured by the acoustic emission sensor.

[0051] Specifically, for the collected raw acoustic signals, an adaptive bandpass filter with a passband of 180-220kHz is used to effectively suppress wind field environmental noise and mechanical vibration interference generated by wind turbine operation, initially screen out the acoustic frequency bands related to icing detection, and improve the ability to identify early icing without relying on cumulative effects.

[0052] Step S22: Using the rotational speed signal at the hub as a reference, the rotational speed synchronous averaging algorithm is used to align and average the sound wave data of the continuous preset number of revolutions according to the rotation angle.

[0053] Specifically, a rotational speed synchronization averaging algorithm is used to strictly align and average the acoustic signals from N consecutive rotations (N≥50) according to the blade rotation angle. This further suppresses random noise, ensures the effective extraction of minute time delays, and secures a critical time window for subsequent de-icing decisions. The algorithm expression is as follows:

[0054]

[0055] in, The sound wave signal after averaging. For time variables, The number of times the signal is averaged. For rotation period index, The original acoustic signal acquired during the k-th rotation cycle. This is the angular offset at the kth revolution. This refers to the instantaneous rotational speed of the fan.

[0056] Step S2 uses adaptive filtering to eliminate wind noise and mechanical vibration interference, and employs a speed-synchronous averaging algorithm to enhance the signal-to-noise ratio of acoustic features, ensuring the purity of the signal in subsequent analysis. A dual noise reduction mechanism is adopted, combining a speed-synchronous averaging algorithm and adaptive band filtering. The former strictly aligns and averages the acoustic signals according to the rotation angle, effectively separating the periodic components related to the blade state; the latter avoids broadband vibration interference caused by the rotor rotation by selecting an acoustic frequency band far from the dominant mechanical vibration frequency. This design optimizes the signal generation and transmission process simultaneously, overcoming the bottleneck of insufficient signal-to-noise ratio in strong noise environments with traditional single sensors. The system's adaptability to dynamic conditions such as sudden wind speed changes and turbulence is significantly improved, reducing the false alarm rate.

[0057] Step S3: Extract the sound wave propagation delay for each propagation path from the leaf tip, leaf middle, leaf root to the leaf root, and calculate the change in sound wave propagation delay for each path within two adjacent rotation cycles based on continuous rotation cycle data.

[0058] In this embodiment, for the piezoelectric exciters at the leading edge tip, middle, and root of the blade, the propagation delay (denoted as τ) of the output excitation signal propagating to the broadband acoustic emission sensor at the corresponding path at the blade root is extracted. Based on continuously acquired rotation cycle data, the change in time delay along the same propagation path within two adjacent rotation cycles is calculated using the following formula:

[0059]

[0060] in, This is the change in sound wave propagation time delay. The propagation delay of the sound wave during the nth rotation is... The propagation delay of the sound wave during the (n-1)th rotation.

[0061] Step S4: Establish a sound speed-ice thickness correlation model and calculate the ice location determination coefficient based on the time delay changes of each path from the leaf tip, leaf middle, leaf root to the leaf root.

[0062] In this embodiment, based on the propagation characteristics of sound waves in the blade composite material, a correlation model is first established between the change in sound wave propagation time delay and the thickness of icing on the blade. The specific expression is as follows:

[0063]

[0064] in, The path length of the sound wave. This serves as the reference value for sound velocity in composite blade materials. The change in the speed of sound The sound velocity attenuation coefficient of the ice layer. This refers to the thickness of the ice layer.

[0065] Based on this, the icing location determination coefficient is calculated according to the time delay changes along each path from the leaf tip, middle of the leaf, leaf root to the leaf base, and then the icing area is determined based on the icing location determination coefficient. The formula for calculating the icing location determination coefficient is as follows:

[0066]

[0067] in, The result of determining the location of the ice. This is an operator that takes the maximum value of the parameter. This represents the change in time delay of the blade tip sensor. This represents the change in time delay of the sensor in the leaf. This represents the change in time delay of the leaf root sensor. To prevent the division of zero into minimal constants.

[0068] This step constructs a quantitative model of ice thickness based on the change in sound wave propagation time delay. At the same time, it combines the relative ratio of the time delay changes of multiple paths to complete the spatial positioning of the ice area. Finally, it forms a two-dimensional detection capability of ice thickness and ice location, providing a core judgment basis for subsequent graded early warning.

[0069] This application establishes a direct physical correlation between sound velocity attenuation and icing thickness by analyzing the propagation time delay of sound waves in blade materials. Compared with traditional monitoring methods based on temperature thresholds or vibration amplitudes, sound wave time delay is highly sensitive to microscopic changes in the material boundary layer, and can accurately capture minute structural changes formed in the early stage of icing, overcoming the limitation of traditional methods in detecting early icing.

[0070] Step S5: Determine the icing state and region based on the time delay change of each propagation path, the time delay change rate within a unit rotation period, and the icing location determination coefficient.

[0071] In this embodiment, step S5 includes:

[0072] Step S51: Determine whether the time delay change of each propagation path exceeds the first preset value, and whether the time delay change rate within a unit rotation period is greater than the second preset value.

[0073] Step S52: If the time delay change of each propagation path exceeds the first preset value, and the time delay change rate within a unit rotation period is greater than the second preset value, then the icing state and region are determined according to the relationship between the icing location determination coefficient and the preset threshold.

[0074] Step S53: If the time delay change of each propagation path does not exceed the first preset value at the same time, or the time delay change rate within a unit rotation cycle is not greater than the second preset value, then the blade is determined to be in normal condition.

[0075] Specifically, the system receives the changes in acoustic propagation delay for each propagation path from step S3 and the icing location determination coefficient from step S4. Simultaneously, it combines this with the duration of a single rotation cycle fed back by the wind turbine speed encoder to calculate the rate of change of delay per unit rotation cycle (i.e., the delay gradient). First, it verifies whether the changes in delay for all propagation paths exceed 0.2 microseconds and whether the rate of change of delay per unit rotation cycle is greater than 0.05 microseconds / revolution. If both conditions are met, the icing area on the blade is further located based on the icing location determination coefficient. If either condition is not met (i.e., not all paths have changes in delay exceeding 0.2 microseconds, or the rate of change of delay is not greater than 0.05 microseconds / revolution), the blade is determined and output as being in a normal, ic-free state. The first preset value is 0.2 microseconds, and the second preset value is 0.05 microseconds / revolution.

[0076] In one optional implementation, step S52 includes:

[0077] Step S521: When the icing location determination coefficient is not less than the first threshold but less than the second threshold, it is determined that ice has formed in the leaf. The second threshold is greater than the first threshold.

[0078] Step S522: When the icing location determination coefficient is not less than the second threshold but less than the third threshold, it is determined that the blade tip is icing. The third threshold is greater than the second threshold.

[0079] Step S523: When the icing location determination coefficient is less than the first threshold or not less than the third threshold, the leaf root is determined to be icing.

[0080] In this embodiment, the rules for determining the icing area are as follows: if 0.3 ≤ icing location determination coefficient < 0.7, then the icing is determined to occur in the middle of the leaf; if 0.7 ≤ icing location determination coefficient < 1.3, then the icing is determined to occur at the leaf tip; other cases (i.e., icing location determination coefficient < 0.3 or ≥ 1.3) are all classified as leaf root icing. The first threshold is 0.3, the second threshold is 0.7, and the third threshold is 1.3.

[0081] Step S5 integrates three core features: the change in sound wave propagation time delay along each propagation path, the time delay gradient within a unit rotation period (i.e., the rate of change in time delay), and the icing location determination coefficient. Relying on multi-dimensional threshold determination logic, it accurately determines the icing state (whether or not there is icing) and specific area of ​​the blade, and finally completes the decision transformation from physical detection signal to icing fault classification.

[0082] This application determines the icing region based on the relative relationship of time delay changes in multiple excitation-sensing paths, eliminating the need for a dense sensor network on the blade surface. By calculating the time delay gradient ratio of different blade segments (tip, middle, and root), and utilizing the spatial characteristics of differences in sound wave propagation paths, the axial location of icing is directly derived. This method forms a distributed perception of the overall structural state of the blade and does not rely on auxiliary means such as vision or infrared that are easily affected by the environment. The positioning results and thickness assessment are output simultaneously, providing spatial dimensional basis for precise de-icing strategies.

[0083] This application integrates time delay variation, gradient, and positioning coefficient into hierarchical decision rules to establish a closed-loop response link from icing state to control action. The system triggers progressive early warning based on the consistent results of continuous detection cycles to avoid malfunctions caused by single fluctuations. At the same time, the thickness assessment results derived from the physical model are directly linked to differentiated operation and maintenance instructions. This design deeply couples state recognition with control logic, reduces manual intervention, improves real-time response, and ensures that operation and maintenance actions match the actual icing risk.

[0084] This invention provides a condition monitoring method for new energy wind turbines. By analyzing the propagation time delay of sound waves in the blade material, this application establishes a direct physical correlation between sound velocity attenuation and icing thickness. By calculating the time delay gradient ratios of different blade sections (tip, middle, and root), and utilizing the spatial characteristics of sound wave propagation path differences, the axial location of icing is directly derived. Compared to traditional monitoring methods based on temperature thresholds or vibration amplitudes, sound wave time delay is extremely sensitive to microscopic changes in the material boundary layer, enabling the capture of minute structural changes formed in the early stages of icing.

[0085] In one alternative implementation, the method further includes:

[0086] Step S6: Based on the icing determination result, generate a heat map of the icing location distribution on the blade surface on the monitoring interface.

[0087] In this embodiment, based on the icing determination result output in step S5, a heat map of the icing location distribution on the blade surface is generated in the visualization interface of the wind farm central monitoring platform. This heat map is dynamically overlaid on the three-dimensional model of the wind turbine blade, and the severity of icing in each area is intuitively mapped through different color depths, making it easy for maintenance personnel to quickly and accurately grasp the icing situation of the blade.

[0088] Step S7: When icing is detected in multiple complete rotation cycles, a graded early warning mechanism is triggered.

[0089] In this embodiment, when the system continuously detects icing for 5 complete rotation cycles, it automatically triggers a graded early warning and response mechanism, the specific rules of which are as follows:

[0090] If the average icing thickness is less than 2 mm, a Level 1 response is initiated, adjusting the yaw system to reduce the blade's angle of attack and decrease the rate of icing. If the icing thickness is between 2 mm and 5 mm, a Level 2 response is triggered, reducing the generator's output power to a safe threshold range to ensure equipment stability. If the icing thickness exceeds 5 mm, a Level 3 highest warning is immediately initiated: the power connection is quickly cut off and an emergency shutdown is performed, while the blade heating and de-icing system is activated to prevent safety risks.

[0091] All warning levels, precise icing locations, ice thickness trends, and corresponding response actions are pushed to the wind farm's central control platform in real time and automatically logged. This step visualizes the icing situation using a heat map of the icing location, triggering tiered control strategies such as yaw adjustments, reduced power operation, and emergency shutdowns. Ultimately, this establishes a closed-loop response mechanism from detection to execution, ensuring rapid and accurate handling of icing risks.

[0092] This embodiment also provides a condition monitoring device for a new energy wind turbine generator. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0093] This embodiment provides a condition monitoring device for new energy wind turbine generators, such as... Figure 2 As shown, it includes:

[0094] The acoustic wave acquisition module 21 is used to trigger the piezoelectric exciter at the leading edge of the blade to output an excitation signal, using the rotational speed signal at the hub as a synchronization reference. At the same time, it acquires acoustic wave data captured by the acoustic emission sensor along the corresponding propagation paths from the blade tip, middle of the blade, and blade root to the blade root.

[0095] The first calculation module 22 is used to extract the sound wave propagation delay for each propagation path from the leaf tip, leaf middle, leaf root to the leaf root, and calculate the change in sound wave propagation delay for each path within two adjacent rotation cycles based on continuous rotation cycle data.

[0096] The second calculation module 23 is used to establish a sound speed-ice thickness correlation model and calculate the ice location determination coefficient based on the time delay changes of each path from the leaf tip, leaf middle, leaf root to the leaf root.

[0097] The status judgment module 24 is used to determine the icing status and area based on the time delay change of each propagation path, the time delay change rate within a unit rotation period, and the icing location determination coefficient.

[0098] The condition monitoring device for new energy wind turbines provided in this embodiment of the invention can execute the condition monitoring method for new energy wind turbines provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0099] This invention provides a condition monitoring device for new energy wind turbines. By analyzing the propagation time delay of sound waves in the blade material, this application establishes a direct physical correlation between sound velocity attenuation and icing thickness. By calculating the time delay gradient ratios of different blade sections (blade tip, blade middle, and blade root), and utilizing the spatial characteristics of sound wave propagation path differences, the axial location of icing is directly derived. Compared to traditional monitoring methods based on temperature thresholds or vibration amplitudes, sound wave time delay is extremely sensitive to microscopic changes in the material boundary layer, enabling the capture of minute structural changes formed in the early stages of icing.

[0100] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0101] The following is a detailed reference. Figure 3The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 301, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 302 or a program loaded from memory 308 into random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0102] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0103] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a memory 308, or installed from a ROM 302. When the computer program is executed by the processor 301, it performs the functions defined in the condition monitoring method for new energy wind turbines according to embodiments of the present invention.

[0104] Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0105] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the condition monitoring method for new energy wind turbines shown in the above embodiments is implemented.

[0106] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A condition monitoring method for new energy wind turbine generators, characterized in that, The method includes: Using the rotational speed signal at the hub as a synchronization reference, the piezoelectric exciter at the leading edge of the blade is triggered to output an excitation signal. At the same time, acoustic wave data captured by the acoustic emission sensor is collected at the corresponding propagation paths from the blade tip, blade middle, blade root to the blade root. For each propagation path from the leaf tip, leaf middle, leaf root to the leaf root, the sound wave propagation delay is extracted. Based on continuous rotation period data, the change in sound wave propagation delay of each path within two adjacent rotation periods is calculated. Establish a correlation model between sound speed and icing thickness, and calculate the icing location determination coefficient based on the time delay changes of each path from the leaf tip, leaf middle, leaf root to the leaf root. The icing state and region are determined based on the time delay change of each propagation path, the time delay change rate per unit rotation period, and the icing location determination coefficient.

2. The condition monitoring method for new energy wind turbines according to claim 1, characterized in that, Based on the time delay change of each propagation path, the time delay change rate per unit rotation period, and the icing location determination coefficient, the icing state and region are determined, including: Determine whether the time delay change of each propagation path exceeds the first preset value, and whether the time delay change rate within a unit rotation period is greater than the second preset value; If the time delay change of each propagation path exceeds the first preset value, and the time delay change rate within a unit rotation cycle is greater than the second preset value, then the icing state and region are determined based on the relationship between the icing location determination coefficient and the preset threshold.

3. The condition monitoring method for new energy wind turbines according to claim 2, characterized in that, The icing state and region are determined based on the relationship between the icing location determination coefficient and a preset threshold, including: When the icing location determination coefficient is not less than the first threshold but less than the second threshold, it is determined that ice has formed in the leaf, and the second threshold is greater than the first threshold. When the icing location determination coefficient is not less than the second threshold but less than the third threshold, it is determined that the leaf tip is icing, and the third threshold is greater than the second threshold; When the icing location determination coefficient is less than the first threshold or not less than the third threshold, the leaf root is determined to be icing.

4. The condition monitoring method for new energy wind turbines according to claim 2, characterized in that, Based on the time delay change of each propagation path, the time delay change rate per unit rotation period, and the icing location determination coefficient, the icing state and region are determined, including: If the time delay change of each propagation path does not exceed the first preset value at the same time, or the time delay change rate within a unit rotation cycle is not greater than the second preset value, then the blade is determined to be in normal condition.

5. The condition monitoring method for new energy wind turbines according to claim 1, characterized in that, The method further includes: Preprocess the acoustic wave data captured by the acoustic emission sensor.

6. The condition monitoring method for new energy wind turbines according to claim 5, characterized in that, The preprocessing of the acoustic wave data captured by the acoustic emission sensor includes: For each corresponding propagation path from the leaf tip, leaf middle, leaf root to the root of the leaf blade, adaptive bandpass filtering is applied to the acoustic wave data captured by the acoustic emission sensor; Using the rotational speed signal at the wheel hub as a reference, a rotational speed synchronous averaging algorithm is used to align and average the sound wave data of a continuous preset number of revolutions according to the rotation angle.

7. The condition monitoring method for new energy wind turbine generators according to claim 1, characterized in that, The method further includes: Based on the icing determination results, a heat map of the icing location distribution on the blade surface is generated on the monitoring interface. When icing is detected in multiple complete rotation cycles, a graded early warning mechanism is triggered.

8. A condition monitoring device for new energy wind turbine generators, characterized in that, The device includes: The acoustic wave acquisition module is used to trigger the piezoelectric exciter at the leading edge of the blade to output an excitation signal, using the rotational speed signal at the hub as a synchronization reference. At the same time, it acquires acoustic wave data captured by the acoustic emission sensor along the corresponding propagation paths from the blade tip, middle of the blade, and blade root to the blade root. The first calculation module is used to extract the sound wave propagation delay for each propagation path from the leaf tip, leaf middle, leaf root to the leaf root, and calculate the change in sound wave propagation delay for each path within two adjacent rotation cycles based on continuous rotation cycle data. The second calculation module is used to establish a correlation model between sound speed and icing thickness, and to calculate the icing location determination coefficient based on the time delay changes of each path from the leaf tip, leaf middle, leaf root to the leaf root. The status judgment module is used to determine the icing status and area based on the time delay change of each propagation path, the time delay change rate within a unit rotation cycle, and the icing location determination coefficient.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the condition monitoring method for a new energy wind turbine generator as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the condition monitoring method for a new energy wind turbine generator as described in any one of claims 1 to 7.

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