Method and device for analyzing loads on a blade of an offshore wind turbine

Abstract

The application provides an offshore wind turbine blade load analysis method and device, and relates to the technical field of wind turbine monitoring. The method comprises the following steps: arranging a load monitoring sensor on a load monitoring point of an offshore wind turbine; acquiring load data collected by the load monitoring sensor, and pre-processing the load data; extracting features of the load data to obtain feature data, and determining a blade load state according to the feature data. The feature data is obtained by extracting features of the load data, and the blade load state is determined according to the feature data, so that the determination of the blade load state is realized, and the accuracy of wind turbine monitoring is improved.

Description

Technical Field

[0001] This application relates to the field of wind turbine monitoring technology, and in particular to a method and apparatus for analyzing the load on offshore wind turbine blades. Background Technology

[0002] Wind energy, as a clean and renewable energy source, is receiving increasing attention from countries worldwide. Its reserves are enormous; global wind energy is ten times greater than the total exploitable hydropower potential on Earth. Therefore, monitoring wind turbines is crucial during wind power generation. While wind power is developing rapidly, problems arising during wind turbine operation are becoming increasingly prominent. In particular, failures in key mechanical components lead to abnormal shutdowns for maintenance, severely reducing power generation efficiency and increasing maintenance costs. The inconvenience of maintaining wind turbines due to malfunctions is further exacerbating application issues.

[0003] In existing technologies, the status of wind turbine units cannot be monitored in a timely manner during use, resulting in a high accident rate and reduced operating efficiency. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a method for analyzing the load on offshore wind turbine blades.

[0006] The second objective of this application is to provide an apparatus.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to provide a computer-readable storage medium.

[0009] The fifth objective of this application is to provide a computer program product.

[0010] To achieve the above objectives, the first aspect of this application proposes a method for analyzing the load on offshore wind turbine blades, comprising:

[0011] Load monitoring sensors are installed at load monitoring points on offshore wind turbines;

[0012] The load data collected by the load monitoring sensor is acquired, and the load data is preprocessed.

[0013] Feature extraction is performed on the load data to obtain feature data, and the blade load state is determined based on the feature data.

[0014] Optionally, the load monitoring points are located at the root of each blade on the wind turbine, with four load monitoring points set at each blade root.

[0015] Optionally, the preprocessing of the load data includes:

[0016] The load data is input into a filter for filtering to remove high-frequency noise;

[0017] For missing data in the load data, interpolation methods are used to fill in the missing data;

[0018] The load data is then normalized.

[0019] Optionally, the step of extracting features from the payload data to obtain feature data includes:

[0020] Set the length of the time window, and determine the corresponding time window starting from each time point;

[0021] Calculate the mean and peak-to-peak values ​​of the load data within each time window;

[0022] The mean and peak-to-peak values ​​are arranged in order of their corresponding time points to generate corresponding mean and peak-to-peak data;

[0023] The peak-to-peak value at each time point is subtracted from the mean value, and the values ​​are arranged in chronological order to generate the feature data.

[0024] Optionally, determining the blade load state based on the feature data includes:

[0025] The time points in the feature data that are greater than a preset feature threshold are identified as abnormal time points;

[0026] Subtract the feature data corresponding to each of the abnormal time points from the feature threshold to obtain the deviation;

[0027] The blade load state is determined based on the sum of the number of abnormal time points and the deviation.

[0028] Optionally, determining the blade load state based on the sum of the number of abnormal time points and the deviation includes:

[0029] If the number of abnormal time points is greater than a preset number threshold, or if the sum of the deviations is greater than a preset deviation threshold, then the blade load state is determined to be abnormal.

[0030] If the number of abnormal time points is less than or equal to a preset number threshold, and the sum of the deviations is less than or equal to a preset deviation threshold, then the blade load state is determined to be normal.

[0031] To achieve the above objectives, a second aspect of this application provides an offshore wind turbine blade load analysis device, comprising:

[0032] The monitoring module is used to install load monitoring sensors at load monitoring points on offshore wind turbines;

[0033] The processing module is used to acquire load data collected by the load monitoring sensor and preprocess the load data;

[0034] The analysis module is used to extract features from the load data to obtain feature data, and to determine the blade load state based on the feature data.

[0035] To achieve the above objectives, a third aspect of this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0038] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0039] To achieve the above objectives, a fifth aspect of this application provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.

[0040] The offshore wind turbine blade load analysis method, apparatus, electronic equipment, and storage medium provided in this application extract features from the load data to obtain feature data, and determine the blade load state based on the feature data, thereby improving the accuracy of wind turbine monitoring. This effectively monitors and analyzes the load on offshore wind turbine blades, providing support for the safe operation and maintenance of offshore wind turbines.

[0041] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0042] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0043] Figure 1A flowchart illustrating a method for analyzing the load on an offshore wind turbine blade provided in this application embodiment;

[0044] Figure 2 This is a schematic diagram of the structure of a marine wind turbine blade load analysis device provided in an embodiment of this application. Detailed Implementation

[0045] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0046] This application provides a method for analyzing the load on offshore wind turbine blades. Figure 1 This is a flowchart illustrating a method for analyzing the load on an offshore wind turbine blade, as provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0047] Step 101: Install load monitoring sensors at load monitoring points on the offshore wind turbine;

[0048] Step 102: Obtain the load data collected by the load monitoring sensor and preprocess the load data;

[0049] Step 103: Extract features from the load data to obtain feature data, and determine the blade load state based on the feature data.

[0050] Optionally, the load monitoring points are located at the root of each blade on the wind turbine, with four load monitoring points set at each blade root.

[0051] The load monitoring sensor is a fiber optic FBG load sensor used to monitor the load on the blades in the swaying and flapping directions. As a passive sensor, the fiber optic sensor requires no external power supply, is easy to integrate into the structure, and has strong resistance to electromagnetic interference, lightning strikes, electrical insulation, corrosion resistance, and a long service life. The fiber optic sensor developed based on fiber optic grating and fiber optic MEMS technology has high sensitivity and reliability, and can provide accurate load and vibration data.

[0052] Optionally, the preprocessing of the load data includes:

[0053] The load data is input into a filter for filtering to remove high-frequency noise;

[0054] For missing data in the load data, interpolation methods are used to fill in the missing data;

[0055] The load data is then normalized.

[0056] In this embodiment, data preprocessing is a collection of techniques and procedures used to refine raw data into an analyzable format. The initial preprocessing steps can significantly impact the final insights or predictive accuracy, whether dealing with structured tabular data, complex text data, time-related temporal data, or even multimedia datasets. Preprocessing these different data types strengthens the foundation of the analysis.

[0057] Structured data consists of well-defined data types and is organized by columns and rows. Its tabular nature makes it a common starting point for many data analysis tasks. Preprocessing structured data typically involves handling missing data and transforming the data into the correct format suitable for analysis.

[0058] First, filtering is performed to remove unwanted noise, peaks, trends, and outliers from the signal to improve data quality. Common filtering methods include:

[0059] Mean filtering: This method filters data by taking the average of the values ​​of the sliding windows preceding and following a point in the original observation data. It's simple, but may sacrifice some of the data.

[0060] Median filtering: Similar to mean filtering, but the median value within a fixed-size sliding window is used as the filtering result. Median filtering can effectively overcome fluctuation noise caused by random factors.

[0061] Low-pass, high-pass, and band-pass filters: These filters are used to block high-frequency signals, low-frequency signals, or retain signals within a specific frequency range, respectively.

[0062] Then, we handle missing and outlier values. For outlier detection, we commonly use two methods: statistical outlier detection and visualization-based outlier detection.

[0063] After finding missing values ​​and outliers using the above methods, there are several processing methods: (1) When the number of outliers and missing values ​​is small and has little impact on the overall data distribution, we can directly delete the outliers and missing values. (2) Fill the data with the mean, median, or mode. (3) Fill the data with interpolation. Fill the data with Lagrange interpolation, Newton interpolation, and cubic bar interpolation.

[0064] Then, data transformation is performed, including normalization: scaling the data to a specific range, such as between 0 and 1, to facilitate comparisons between different features; and standardization: converting the data into a distribution with a mean of 0 and a standard deviation of 1.

[0065] Optionally, the step of extracting features from the payload data to obtain feature data includes:

[0066] Set the length of the time window, and determine the corresponding time window starting from each time point;

[0067] Calculate the mean and peak-to-peak values ​​of the load data within each time window;

[0068] The mean and peak-to-peak values ​​are arranged in order of their corresponding time points to generate corresponding mean and peak-to-peak data;

[0069] The peak-to-peak value at each time point is subtracted from the mean value, and the values ​​are arranged in chronological order to generate the feature data.

[0070] Optionally, determining the blade load state based on the feature data includes:

[0071] The time points in the feature data that are greater than a preset feature threshold are identified as abnormal time points;

[0072] Subtract the feature data corresponding to each of the abnormal time points from the feature threshold to obtain the deviation;

[0073] The blade load state is determined based on the sum of the number of abnormal time points and the deviation.

[0074] Optionally, determining the blade load state based on the sum of the number of abnormal time points and the deviation includes:

[0075] If the number of abnormal time points is greater than a preset number threshold, or if the sum of the deviations is greater than a preset deviation threshold, then the blade load state is determined to be abnormal.

[0076] If the number of abnormal time points is less than or equal to a preset number threshold, and the sum of the deviations is less than or equal to a preset deviation threshold, then the blade load state is determined to be normal.

[0077] To achieve the above embodiments, this application also proposes a device for analyzing the load on offshore wind turbine blades. Figure 2 This is a schematic diagram of a load analysis device for offshore wind turbine blades provided in an embodiment of this application. Figure 2 As shown, the device includes:

[0078] Monitoring module 210 is used to install load monitoring sensors at load monitoring points on offshore wind turbines;

[0079] Processing module 220 is used to acquire load data collected by the load monitoring sensor and preprocess the load data;

[0080] The analysis module 230 is used to extract features from the load data to obtain feature data, and to determine the blade load state based on the feature data.

[0081] To implement the above embodiments, this application also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0082] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0083] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0084] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0085] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0086] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0087] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0088] Furthermore, 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 at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0089] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0090] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0091] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0092] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0093] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0094] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for analyzing the load on offshore wind turbine blades, characterized in that, Includes the following steps: Load monitoring sensors are installed at load monitoring points on offshore wind turbines; The load data collected by the load monitoring sensor is acquired, and the load data is preprocessed. Feature extraction is performed on the load data to obtain feature data, and the blade load state is determined based on the feature data.

2. The method according to claim 1, characterized in that, The load monitoring points are located at the root of each blade on the wind turbine, with four load monitoring points set at each blade root.

3. The method according to claim 2, characterized in that, The preprocessing of the load data includes: The load data is input into a filter for filtering to remove high-frequency noise; For missing data in the load data, interpolation methods are used to fill in the missing data; The load data is then normalized.

4. The method according to claim 3, characterized in that, The step of extracting features from the payload data to obtain feature data includes: Set the length of the time window, and determine the corresponding time window starting from each time point; Calculate the mean and peak-to-peak values ​​of the load data within each time window; The mean and peak-to-peak values ​​are arranged in order of their corresponding time points to generate corresponding mean and peak-to-peak data; The peak-to-peak value at each time point is subtracted from the mean value, and the values ​​are arranged in chronological order to generate the feature data.

5. The method according to claim 4, characterized in that, Determining the blade load state based on the feature data includes: The time points in the feature data that are greater than a preset feature threshold are identified as abnormal time points; Subtract the feature data corresponding to each of the abnormal time points from the feature threshold to obtain the deviation; The blade load state is determined based on the sum of the number of abnormal time points and the deviation.

6. The method according to claim 5, characterized in that, Determining the blade load state based on the sum of the number of abnormal time points and the deviation includes: If the number of abnormal time points is greater than a preset number threshold, or if the sum of the deviations is greater than a preset deviation threshold, then the blade load state is determined to be abnormal. If the number of abnormal time points is less than or equal to a preset number threshold, and the sum of the deviations is less than or equal to a preset deviation threshold, then the blade load state is determined to be normal.

7. A device for analyzing the load on offshore wind turbine blades, characterized in that, include: The monitoring module is used to install load monitoring sensors at load monitoring points on offshore wind turbines; The processing module is used to acquire load data collected by the load monitoring sensor and preprocess the load data; The analysis module is used to extract features from the load data to obtain feature data, and to determine the blade load state based on the feature data.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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