Fan tower inherent frequency abnormity identification method, storage medium and electronic equipment

Abstract

The invention provides a fan tower inherent frequency abnormity identification method, a storage medium and electronic equipment, and belongs to the technical field of state monitoring and fault diagnosis of wind power generation equipment, and the method comprises the steps: S1, collecting an acceleration signal of a fan tower, and carrying out the preprocessing of the acceleration signal to eliminate a DC component; s2, performing grouping processing on the preprocessed acceleration signals, performing spectral analysis on each group of acceleration signals in sequence, and identifying alternative frequencies of each group in sequence in a preset frequency band; s3, storing the continuously obtained alternative frequencies by adopting a sliding window mechanism, removing abnormal values in a window based on a statistical anomaly detection method, and calculating to obtain an inherent frequency; and S4, comparing the inherent frequency with a reference inherent frequency, judging the health state of the tower drum structure according to whether the relative error exceeds a preset threshold value or not, and triggering a corresponding alarm. According to the method, the defects of a traditional peak value pickup method are overcome, and the credibility of a result obtained through real-time monitoring is higher.

Description

Technical Field

[0001] This invention relates to the field of wind power equipment condition monitoring and fault diagnosis technology, and in particular to a method for calculating the natural frequency of a wind turbine tower and identifying anomalies, a storage medium, and electronic equipment. Background Technology

[0002] As the global energy structure shifts towards green and low-carbon practices, wind power, as a crucial component of clean energy, is experiencing rapid and continuous growth in installed capacity and unit size. The wind turbine tower, as a key load-bearing structure supporting the rotor and nacelle, directly impacts the operational safety and service life of the entire turbine. The tower's natural frequency is a critical dynamic characteristic parameter, affecting not only the dynamic coupling between the turbine and its aerodynamic and control systems but also serving as a sensitive indicator for assessing the tower's structural integrity (e.g., foundation settlement, weld cracking, tower corrosion, bolt preload loss).

[0003] Currently, the industry primarily uses peak picking to acquire and analyze the natural frequency of wind turbine towers. To obtain the actual frequency, high-precision accelerometers are typically installed on the tower. Vibration response signals from environmental excitations (such as wind loads and turbine start-up / shutdown) or artificial excitations are collected, and spectral analysis (such as Fourier transform) is performed to identify the dominant frequency. While this method is relatively direct, the real-time monitoring and identification of the natural frequency has a large error. Generally, the rotor rotation frequency of a flexible tower is close to the tower's natural frequency. When the wind turbine is operating, the rotor's 1P and 3P frequencies, as well as gearbox operation, all cause tower vibration. Consequently, signals with amplitudes higher than the natural frequency appear in the spectrum near the natural frequency. If peak picking is simply performed to select the frequency with the largest amplitude as the wind turbine tower's natural frequency, there is a probability that other frequencies will be mistaken for the natural frequency when the wind turbine is operating at full load. Summary of the Invention

[0004] In view of the technical defects and drawbacks existing in the prior art, the embodiments of the present invention provide a method, storage medium and electronic device for identifying abnormal natural frequencies of wind turbine towers to overcome the above problems or at least partially solve the above problems. It can reduce the error caused by calculating the natural frequency due to different wind turbine operating conditions, and realize the evaluation of the health status of wind turbine towers based on the natural frequency obtained by real-time calculation. The specific solution is as follows:

[0005] As a first aspect of the present invention, a method for calculating and identifying anomalies in the natural frequency of a wind turbine tower is provided, characterized by comprising the following steps:

[0006] S1: Acquire the acceleration signal of the wind turbine tower and preprocess the acceleration signal to eliminate the DC component;

[0007] S2: The preprocessed acceleration signals are grouped and processed, and the spectrum analysis of each group of acceleration signals is performed in turn. The candidate frequencies of each group are identified in turn within the preset frequency band.

[0008] S3: The continuously obtained candidate frequencies are stored using a sliding window mechanism, and the inherent frequencies are calculated after outliers in the window are removed based on a statistical anomaly detection method.

[0009] S4: Compare the natural frequency with the reference natural frequency, and determine the health status of the tower structure and trigger the corresponding alarm based on whether the relative error exceeds a preset threshold.

[0010] In some embodiments, acquiring the acceleration signal in step S1 specifically includes:

[0011] A triaxial accelerometer is installed at the top of the wind turbine tower, and the sampling frequency is set to 10Hz to collect acceleration signals.

[0012] In some embodiments, step S2 specifically includes:

[0013] S21: A high-pass filter with a cutoff frequency of 0.1Hz is used to filter out the DC component in the acceleration signal;

[0014] S22: For the acceleration signal after filtering out the DC component, take 2048 consecutive data points as a group, and perform a fast Fourier transform on each group of data to calculate the spectrum information;

[0015] S23: Based on the spectral information corresponding to each group of data, select the frequency with the largest peak value within the frequency range of 0~0.5Hz as the candidate frequency for that group.

[0016] In some embodiments, step S3 specifically includes:

[0017] S31. Establish a fixed-length sliding window to store continuously acquired candidate frequency sequences;

[0018] S32. Use the interquartile range method to detect outliers in the candidate frequencies within the window, identify and eliminate non-natural frequency components caused by wind turbine operating condition interference.

[0019] S33. Calculate the arithmetic mean of the candidate frequencies that pass the anomaly detection to obtain the natural frequency that reflects the structural state of the tower.

[0020] In some embodiments, the length of the sliding window is 421, meaning the window can store 421 consecutive calculations of alternative frequencies.

[0021] In some embodiments, the interquartile range (ICM) method is used to detect outliers among candidate frequencies within the window, specifically including:

[0022] The candidate frequencies in the sliding window are sorted by numerical value to obtain an ordered sequence. ;

[0023] Calculate the median of the ordered sequence. and with Based on this, calculate from arrive The median is the first quartile. ,from arrive The median is the third quartile. ;

[0024] According to the formula Calculate the interquartile range;

[0025] Set the lower bound for outlier detection as The upper boundary is ;

[0026] Candidate frequencies whose values ​​exceed the lower and upper bounds are identified as outliers and removed.

[0027] In some embodiments, determining the health status of the tower structure in step S4 specifically includes:

[0028] S41. When the sliding window is full of candidate frequencies for the first time, set the natural frequency calculated in step S3 at this time as the reference natural frequency. ;

[0029] S42. Subsequently, after obtaining each new candidate frequency, update the window and recalculate the corresponding natural frequency according to the method in step S3, which is then used as the real-time natural frequency. ;

[0030] S43, According to the formula Calculate the relative error. When the relative error exceeds the preset safety threshold, determine that the tower structure is abnormal and trigger an inspection alarm.

[0031] In some embodiments, the method further includes a real-time update mechanism, specifically including:

[0032] After completing the reference natural frequency After setting it up, start the real-time update process:

[0033] After acquiring each set of acceleration signals and calculating a corresponding new candidate frequency, an update operation is performed on the sliding window:

[0034] First, remove the earliest entered candidate frequency from the window;

[0035] Then shift all remaining candidate frequencies one position to the left in chronological order;

[0036] Finally, the latest calculated candidate frequency is added to the end of the window;

[0037] Based on the candidate frequencies within the updated window, the natural frequency is recalculated according to the S3 method and used as the real-time natural frequency. .

[0038] As a second aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the methods described above.

[0039] As a third aspect of the present invention, an electronic device is provided, comprising:

[0040] One or more processors;

[0041] Memory, used to store one or more programs;

[0042] When the one or more programs are executed by the one or more processors, the one or more processors implement the methods described above.

[0043] The present invention has the following beneficial effects:

[0044] The peak picking method based on the sliding window approach proposed in this invention has better stability than traditional peak picking methods. Under complex operating conditions, traditional peak picking methods generally struggle to reliably identify the true natural frequency of the wind turbine tower, failing to achieve the goal of real-time monitoring. The method proposed in this invention can improve upon the shortcomings of traditional peak picking methods to a certain extent, while providing more reliable results from real-time monitoring. Compared to other natural frequency identification methods such as the random subspace method, it is simpler, more efficient, and can also be calculated fully automatically. Attached Figure Description

[0045] Figure 1 A flowchart illustrating a method for calculating the natural frequency of a wind turbine tower and identifying anomalies, provided in an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of the process for identifying candidate frequencies provided in an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the process for calculating the natural frequency provided in an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the process for determining the health status of a tower structure according to an embodiment of the present invention;

[0049] Figure 5This is a schematic diagram illustrating the entire process of a method for calculating the natural frequency of a wind turbine tower and identifying anomalies, provided by an embodiment of the present invention.

[0050] Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0052] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0053] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0054] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0055] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0056] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0057] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides a method for calculating the natural frequency of wind turbine towers and identifying anomalies. Figure 1 This is a flowchart illustrating a method for calculating the natural frequency of a wind turbine tower and identifying anomalies, provided in an embodiment of the present invention. The method includes:

[0058] S1: Acquire the acceleration signal of the wind turbine tower and preprocess the acceleration signal to eliminate the DC component;

[0059] S2: The preprocessed acceleration signals are grouped and processed, and the spectrum analysis of each group of acceleration signals is performed in turn. The candidate frequencies of each group are identified in turn within the preset frequency band.

[0060] S3: The continuously obtained candidate frequencies are stored using a sliding window mechanism, and the inherent frequencies are calculated after outliers in the window are removed based on a statistical anomaly detection method.

[0061] S4: Compare the natural frequency with the reference natural frequency, and determine the health status of the tower structure and trigger the corresponding alarm based on whether the relative error exceeds a preset threshold.

[0062] This invention provides a complete method for calculating the natural frequency of wind turbine towers and identifying anomalies, establishing a comprehensive technical solution from data acquisition to health status assessment. Specifically, this method acquires vibration signals, obtains candidate frequencies through spectrum analysis, eliminates outliers using statistical methods, and determines the status by comparing with a reference frequency. This achieves accurate calculation and real-time monitoring of the tower's natural frequency, effectively overcoming the technical shortcomings of traditional peak picking methods that are prone to misjudging the natural frequency under complex operating conditions, and improving the reliability and accuracy of wind turbine tower structural health monitoring.

[0063] In some embodiments, acquiring the acceleration signal in step S1 specifically includes:

[0064] A triaxial accelerometer is installed at the top of the wind turbine tower, and the sampling frequency is set to 10Hz to collect acceleration signals.

[0065] In the above embodiments, since the natural frequency of the wind turbine tower is at Within this range, setting the above sampling rate allows the tower's natural frequency to be acquired without aliasing.

[0066] refer to Figure 2 As shown, in some embodiments, step S2 specifically includes:

[0067] S21: A high-pass filter with a cutoff frequency of 0.1Hz is used to filter out the DC component in the acceleration signal caused by the zero bias of the accelerometer.

[0068] S22: For the acceleration signal after filtering out the DC component, the spectrum information is obtained by performing a Fast Fourier Transform (FFT) on each group of 2048 consecutive data points.

[0069] S23: Based on the spectral information corresponding to each group of data, select the frequency with the largest peak value within the frequency range of 0~0.5Hz as the candidate frequency for that group.

[0070] The above embodiments further optimize the spectrum analysis method, improving the accuracy of natural frequency identification through reasonable signal processing parameter settings. Specifically, a 0.1Hz high-pass filter effectively eliminates DC component interference generated by sensor zero bias, and selecting 2048 data points as a group ensures the spectral resolution of FFT calculation. The 0~0.5Hz characteristic frequency band accurately covers the natural frequency distribution range of the tower. These parameter optimizations make the selection of candidate frequencies more accurate and reliable.

[0071] refer to Figure 3 As shown, in some embodiments, step S3 specifically includes:

[0072] S31. Establish a fixed-length sliding window to store continuously acquired candidate frequency sequences;

[0073] S32. Use the interquartile range (IQR) method to detect outliers in the candidate frequencies within the window, identify and eliminate non-inherent frequency components caused by wind turbine operating condition interference.

[0074] S33. Calculate the arithmetic mean of the candidate frequencies that pass the anomaly detection to obtain the natural frequency that reflects the structural state of the tower.

[0075] The above embodiments improve the stability and accuracy of natural frequency calculation through a sliding window mechanism and the IQR method. Specifically, the method establishes a fixed-length sliding window to store a continuous sequence of candidate frequencies, uses the interquartile range (IQR) method to effectively identify and eliminate non-natural frequency components caused by wind turbine operating condition interference, and finally calculates the arithmetic mean of the detected normal frequencies. Thus, even under complex vibration environments, it can still obtain the real-time natural frequency that reflects the true structural state of the tower, overcoming the technical defects of traditional peak picking methods that are easily affected by interference frequencies.

[0076] In some embodiments, the length of the sliding window is 421. The window size is determined based on the stability characteristics of the tower's natural frequency and the statistical significance requirements, which can provide a sufficient number of samples for reliable outlier detection while ensuring real-time performance.

[0077] In some embodiments, the outlier detection using the interquartile range (IQR) method for candidate frequencies within the window specifically includes:

[0078] The candidate frequencies in the sliding window are sorted by numerical value to obtain an ordered sequence. ;

[0079] Calculate the median of the ordered sequence. and with Based on this, calculate from arrive The median is the first quartile. ,from arrive The median is the third quartile. ;

[0080] According to the formula Calculate the interquartile range;

[0081] Set the lower bound for outlier detection as The upper boundary is ;

[0082] Candidate frequencies whose values ​​exceed the lower and upper bounds are identified as outliers and removed.

[0083] The above embodiments define the execution steps of the IQR method, ensuring the accuracy and repeatability of outlier detection through standardized statistical calculation procedures. Specifically, the method includes steps such as candidate frequency sorting, quartile calculation, IQR value determination, upper and lower bound setting, and outlier judgment. It can stably identify the true intrinsic frequency under complex operating conditions containing multiple interference frequencies, avoiding misjudgments caused by individual abnormal data and improving the reliability of wind turbine tower health status assessment.

[0084] refer to Figure 4 As shown, in some embodiments, determining the health status of the tower structure in step S4 specifically includes:

[0085] S41. When the sliding window is full of candidate frequencies for the first time, set the natural frequency calculated in step S3 at this time as the reference natural frequency. ;

[0086] S42. Subsequently, after obtaining each new candidate frequency, update the window and recalculate the corresponding natural frequency according to the method in step S3, which is then used as the real-time natural frequency. ;

[0087] S43, According to the formula Calculate the relative error. When the relative error exceeds a preset safety threshold of 10%, determine that the tower structure is abnormal and trigger an inspection alarm.

[0088] The above embodiments detail the health status determination mechanism, achieving accurate early warning of tower structure anomalies through scientific benchmark setting and error analysis. Specifically, the method first establishes a reference natural frequency when the sliding window first reaches 421 candidate frequencies. Subsequent calculations in real time And based on the formula Relative error analysis is performed, and an alarm is immediately triggered when the error exceeds the 10% safety threshold. This threshold can be scientifically set based on the normal variation range of the tower's natural frequency, which can effectively distinguish between normal frequency fluctuations and structural abnormalities, thus improving the timeliness and accuracy of wind turbine tower health monitoring.

[0089] In some embodiments, the method further includes a real-time update mechanism, specifically including:

[0090] After completing the reference natural frequency After setting it up, start the real-time update process:

[0091] After acquiring each set of acceleration signals and calculating a corresponding new candidate frequency, an update operation is performed on the sliding window:

[0092] First, remove the earliest entered candidate frequency from the window;

[0093] Then shift all remaining candidate frequencies one position to the left in chronological order;

[0094] Finally, the latest calculated candidate frequency is added to the end of the window;

[0095] Based on the updated window content that still retains 421 candidate frequencies, the inherent frequencies are recalculated according to the S3 method and used as the real-time inherent frequencies. .

[0096] The above embodiments further optimize the real-time update mechanism, achieving continuous and reliable monitoring of the inherent frequency through a first-in-first-out (FIFO) window update strategy. Specifically, the method initiates a real-time update process after the reference frequency is set. Each time a new candidate frequency is obtained, the oldest data in the window is removed, the remaining data is shifted to the left, and the latest data is added, always maintaining a complete window of 421 data points. The real-time inherent frequency is recalculated based on the updated window. This mechanism ensures both the real-time nature of the monitoring and the sample size for statistical analysis, ensuring stable and reliable inherent frequency calculation results can still be obtained under dynamic operating conditions.

[0097] refer to Figure 5 The diagram shown is a schematic diagram of the entire process of a wind turbine tower natural frequency calculation and anomaly identification method provided in an embodiment of the present invention.

[0098] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 6 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the wind turbine tower natural frequency calculation and anomaly identification methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0099] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0100] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0101] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0102] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the wind turbine tower natural frequency calculation and anomaly identification methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0103] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes any of the above-described methods for calculating the natural frequency of a wind turbine tower and identifying anomalies.

[0104] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0105] As is known to those skilled in the art, computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0106] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0107] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0108] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0109] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0110] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0111] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0113] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for calculating the natural frequency of a wind turbine tower and identifying anomalies, characterized in that, Includes the following steps: S1: Acquire the acceleration signal of the wind turbine tower and preprocess the acceleration signal to eliminate the DC component; S2: The preprocessed acceleration signals are grouped and processed, and the spectrum analysis of each group of acceleration signals is performed in turn. The candidate frequencies of each group are identified in turn within the preset frequency band. S3: The continuously obtained candidate frequencies are stored using a sliding window mechanism, and the inherent frequencies are calculated after outliers in the window are removed based on a statistical anomaly detection method. S4: Compare the natural frequency with the reference natural frequency, and determine the health status of the tower structure and trigger the corresponding alarm based on whether the relative error exceeds a preset threshold.

2. The method according to claim 1, characterized in that, The acquisition of acceleration signals in step S1 specifically includes: A triaxial accelerometer is installed at the top of the wind turbine tower, and the sampling frequency is set to 10Hz to collect acceleration signals.

3. The method according to claim 1, characterized in that, Step S2 specifically includes: S21: A high-pass filter with a cutoff frequency of 0.1Hz is used to filter out the DC component in the acceleration signal; S22: For the acceleration signal after filtering out the DC component, take 2048 consecutive data points as a group, and perform a fast Fourier transform on each group of data to calculate the spectrum information; S23: Based on the spectral information corresponding to each group of data, select the frequency with the largest peak value within the frequency range of 0~0.5Hz as the candidate frequency for that group.

4. The method according to claim 1, characterized in that, Step S3 specifically includes: S31. Establish a fixed-length sliding window to store continuously acquired candidate frequency sequences; S32. Use the interquartile range method to detect outliers in the candidate frequencies within the window, identify and eliminate non-natural frequency components caused by wind turbine operating condition interference. S33. Calculate the arithmetic mean of the candidate frequencies that pass the anomaly detection to obtain the natural frequency that reflects the structural state of the tower.

5. The method according to claim 4, characterized in that, The length of the sliding window is 421.

6. The method according to claim 4, characterized in that, The interquartile range method is used to detect outliers among candidate frequencies within the window, specifically including: The candidate frequencies in the sliding window are sorted by numerical value to obtain an ordered sequence. ; Calculate the median of the ordered sequence. and with Based on this, calculate from arrive The median is the first quartile. ,from arrive The median is the third quartile. ; According to the formula Calculate the interquartile range; Set the lower bound for outlier detection as The upper boundary is ; Candidate frequencies whose values ​​exceed the lower and upper bounds are identified as outliers and removed.

7. The method according to claim 1, characterized in that, Step S4, determining the health status of the tower structure, specifically includes: S41. When the sliding window is full of candidate frequencies for the first time, set the natural frequency calculated in step S3 at this time as the reference natural frequency. ; S42. Subsequently, after obtaining each new candidate frequency, update the window and recalculate the corresponding natural frequency according to the method in step S3, which is then used as the real-time natural frequency. ; S43, According to the formula Calculate the relative error. When the relative error exceeds the preset safety threshold, determine that the tower structure is abnormal and trigger an inspection alarm.

8. The method according to claim 7, characterized in that, The method also includes a real-time update mechanism, specifically including: After completing the reference natural frequency After setting it up, start the real-time update process: After acquiring each set of acceleration signals and calculating a corresponding new candidate frequency, an update operation is performed on the sliding window: First, remove the earliest entered candidate frequency from the window; Then shift all remaining candidate frequencies one position to the left in chronological order; Finally, the latest calculated candidate frequency is added to the end of the window; Based on the candidate frequencies within the updated window, the natural frequency is recalculated according to the S3 method and used as the real-time natural frequency. .

9. A computer-readable medium, characterized in that, The computer-readable medium stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-8.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-8.

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