Health degree evaluation method and system for operation state of offshore wind turbine generator
By using the ridge distribution function model and parameter optimization, the accuracy and speed issues of health assessment for offshore wind turbines were resolved, enabling timely evaluation and early warning of offshore wind turbines and improving the operational efficiency and safety of wind farms.
Patent Information
- Application Number
- CN202410611531.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-16
- Publication Date
- 2025-11-18
AI Technical Summary
Traditional methods for evaluating the health of offshore wind turbines suffer from problems such as high subjectivity, low accuracy, slow response speed, and difficulty in quickly processing massive amounts of data.
By adopting the ridge distribution function model, the health trend of wind turbines is determined by acquiring their operating status data, a health index is constructed and evaluated in combination with thresholds, and the parameters are optimized using gradient descent or genetic algorithm to achieve the health assessment of offshore wind turbines.
It enables timely assessment of the health of offshore wind turbines, improves the accuracy and response speed of evaluation, reduces misjudgments, optimizes the operation strategy of wind farms, and enhances operational efficiency and reliability.
Smart Images
Figure CN120969063A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of wind turbine operating state health degree evaluation, and relates to a health degree evaluation method and system for an offshore wind turbine operating state. BACKGROUND
[0002] With the increasing seriousness of climate change and environmental problems, wind energy as a clean and renewable energy form has received more and more attention and favor. Offshore wind power as an important field of wind energy development has advantages such as abundant resources, stable wind speed, and small impact on land environment, so it has become a key direction of wind energy development.
[0003] However, the offshore wind turbine operating environment is harsh, and is affected by many factors such as marine climate, salt spray corrosion, and wave impact. Its key components such as blades, gearboxes, and generators are prone to wear, fatigue, and fracture. These problems not only affect the power generation efficiency of the wind turbine, but also may cause safety accidents, bringing great risks to the operation of the wind farm. Therefore, accurately evaluating the health degree of offshore wind turbines and key components and timely discovering potential safety hazards are the key to ensuring the safe, stable, and efficient operation of the wind farm.
[0004] Traditional wind turbine health degree evaluation methods are mostly based on periodic maintenance and experience judgment, and have problems such as strong subjectivity, low accuracy, and slow response. With the rapid development of sensor technology, data analysis technology, and artificial intelligence technology, new means and methods are provided for the health degree evaluation of offshore wind turbines and key components. By monitoring the operating state of the wind turbine in real time, collecting the operating data of the key components, and using data analysis technology and machine learning algorithms to process and analyze the data, the health degree of the wind turbine can be accurately evaluated and predicted. However, due to the need to process massive data, the model complexity is high, and it is difficult to quickly evaluate the health degree of the wind turbine. SUMMARY
[0005] The purpose of the present application is to solve the problems in the prior art and provide a health degree evaluation method and system for an offshore wind turbine operating state.
[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:
[0007] The health degree evaluation method for an offshore wind turbine operating state provided by the present application comprises the following steps:
[0008] Obtain preprocessed wind turbine operating state data, and obtain the change trend of the health degree of the wind turbine according to the preprocessed wind turbine operating state data;
[0009] Based on the changing trend of wind turbine health, the form of the ridge distribution function is determined, and the parameters of the ridge distribution function are determined by fitting the preprocessed wind turbine operating status data.
[0010] Based on the parameters of the ridge distribution function, a health index for wind turbines is constructed. The health status of wind turbines is judged by combining the health index and the health threshold, thereby realizing the health evaluation of the operating status of wind turbines.
[0011] Preferably, the trend of the obtained wind turbine health status is as follows:
[0012] The preprocessed wind turbine operating status data first rises and then falls, forming a descending ridge shape; the preprocessed wind turbine operating status data first falls and then rises, forming an ascending ridge shape; the preprocessed wind turbine operating status data has a non-linear distribution, forming an intermediate ridge shape.
[0013] Preferably, the ridge distribution function is defined in the following form:
[0014] If the preprocessed wind turbine operating status data shows a descending ridge trend, then the ridge distribution function is determined to be a descending ridge distribution function; if the preprocessed wind turbine operating status data shows an ascending ridge trend, then the ridge distribution function is determined to be an ascending ridge distribution function; if the preprocessed wind turbine operating status data shows a nonlinear trend, then the ridge distribution function is determined to be an intermediate ridge distribution function.
[0015] Preferably, the descending ridge distribution function is expressed in the following form:
[0016]
[0017] Here, a1 and a2 are both fuzzy dividing points for risk levels, and the range of values for a1 and a2 are different.
[0018] Preferably, the ascending ridge distribution function is expressed in the following form:
[0019]
[0020] Here, a1 and a2 are both fuzzy dividing points for risk levels, and the range of values for a1 and a2 are different.
[0021] Preferably, the intermediate ridge distribution function is expressed in the following form:
[0022]
[0023] Among them, a1, a2, a3 and a4 are all fuzzy dividing points of risk level, and the range of values for a1, a2, a3 and a4 are different.
[0024] Preferably, the parameters of the ridge distribution function are determined using gradient descent or a genetic algorithm.
[0025] This invention proposes a health assessment system for the operating status of offshore wind turbines, comprising:
[0026] The data preprocessing module is used to acquire preprocessed wind turbine operating status data and to obtain the trend of wind turbine health status changes based on the preprocessed wind turbine operating status data.
[0027] The function form determination module is used to determine the form of the ridge distribution function based on the changing trend of the wind turbine health status, and to determine the parameters of the ridge distribution function by fitting the preprocessed wind turbine operating status data.
[0028] The health status assessment module is used to construct a health index for wind turbines based on the parameters of the ridge distribution function, and to determine the health status of wind turbines by combining the health index and the health threshold, thereby achieving a health evaluation of the operating status of wind turbines.
[0029] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a health assessment method for the operating status of an offshore wind turbine.
[0030] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a health assessment method for the operating status of an offshore wind turbine.
[0031] Compared with the prior art, the present invention has the following beneficial effects:
[0032] This invention proposes a method for evaluating the health status of offshore wind turbines. Firstly, by continuously monitoring the operating data of the wind turbines, trends in turbine health can be detected promptly. Once an anomaly is detected, the ridge distribution function can react quickly, triggering an early warning mechanism. This helps maintenance personnel identify potential faults in advance, avoiding downtime losses due to escalating faults and improving the operational efficiency of the wind farm. Secondly, this method improves the accuracy of wind turbine health evaluation. The ridge distribution function can more accurately describe the changing patterns of wind turbine health, avoiding misjudgments caused by data fluctuations or outliers in traditional methods. Furthermore, by continuously adjusting and optimizing the parameters of the ridge distribution function, the accuracy of the evaluation can be further improved, providing a more reliable basis for wind farm decision-making. Finally, the implementation of this method also helps improve the overall operational level of the wind farm. Through a comprehensive evaluation of wind turbine health, problems in operation can be identified and resolved in a timely manner, optimizing the wind farm's operational strategies and improving its power generation efficiency and reliability. Therefore, this method is of great significance for improving the operational efficiency of offshore wind turbines, reducing maintenance costs, and enhancing the safety and reliability of wind farms.
[0033] This invention proposes a health assessment system for the operating status of offshore wind turbines. By dividing the system into a data preprocessing module, a function form determination module, and a health status judgment module, it achieves the health assessment of the wind turbine's operating status. The modular approach ensures that each module is independent, facilitating unified management of all modules. Attached Figure Description
[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 This is a flowchart of the health evaluation method for the operating status of offshore wind turbines according to the present invention.
[0036] Figure 2 This is a graph of the membership functions at each level of the present invention.
[0037] Figure 3 This is a diagram of the health evaluation system for the operating status of offshore wind turbines according to the present invention.
[0038] Figure 4 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation
[0039] The present invention will now be described in further detail with reference to the accompanying drawings:
[0040] This invention proposes a health assessment method for the operating status of offshore wind turbines, such as... Figure 1 Includes the following steps:
[0041] S1. Obtain pre-processed wind turbine operating status data, and obtain the trend of wind turbine health status changes based on the pre-processed wind turbine operating status data.
[0042] The changing trend of the obtained wind turbine health status is as follows:
[0043] The preprocessed wind turbine operating status data first rises and then falls, forming a descending ridge shape; the preprocessed wind turbine operating status data first falls and then rises, forming an ascending ridge shape; the preprocessed wind turbine operating status data has a non-linear distribution, forming an intermediate ridge shape.
[0044] Before acquiring preprocessed wind turbine operating status data, the data is cleaned and organized to remove outliers and missing values, ensuring the accuracy and completeness of the data.
[0045] S2. Based on the changing trend of the wind turbine health status, determine the form of the ridge distribution function, and determine the parameters of the ridge distribution function by fitting the preprocessed wind turbine operating status data;
[0046] The ridge distribution function is defined in the following form:
[0047] If the preprocessed wind turbine operating status data shows a descending ridge trend, then the ridge distribution function is determined to be a descending ridge distribution function; if the preprocessed wind turbine operating status data shows an ascending ridge trend, then the ridge distribution function is determined to be an ascending ridge distribution function; if the preprocessed wind turbine operating status data shows a nonlinear trend, then the ridge distribution function is determined to be an intermediate ridge distribution function.
[0048] The descending ridge distribution function is expressed as follows:
[0049]
[0050] Here, a1 and a2 are both fuzzy dividing points for risk levels, and the range of values for a1 and a2 are different.
[0051] The ascending ridge distribution function is expressed as follows:
[0052]
[0053] Here, a1 and a2 are both fuzzy dividing points for risk levels, and the range of values for a1 and a2 are different.
[0054] The intermediate ridge distribution function is expressed as follows:
[0055]
[0056] Among them, a1, a2, a3 and a4 are all fuzzy dividing points of risk level, and the range of values for a1, a2, a3 and a4 are different.
[0057] The parameters of the ridge distribution function can be determined using gradient descent or a genetic algorithm.
[0058] S3. Construct a health index for wind turbines based on the parameters of the ridge distribution function, and combine the health index and health threshold to determine the health status of wind turbines, thereby achieving a health evaluation of the operating status of wind turbines.
[0059] Based on actual needs, health thresholds can be set to determine the health status of devices or systems, such as normal, warning, or danger.
[0060] By following the above logical steps, the health of a device or system can be evaluated using descending ridge distribution functions, ascending ridge distribution functions, and intermediate ridge distribution functions. It should be noted that in practical applications, the logical steps may need to be adjusted and optimized according to specific circumstances.
[0061] The detailed implementation of this health assessment method is as follows:
[0062] Based on the actual management needs of wind turbine operation status assessment, the wind turbine operation status set L includes four evaluation levels: "Excellent", "Good", "Average", and "Warning", denoted as l1, l2, l3, l4 respectively; that is, L = {l1, l2, l3, l4}. Since the normalized values of each indicator parameter have been obtained through degradation analysis, and the values are distributed between [0, 1], it is necessary to select a membership function that reasonably covers the degradation value range of each indicator parameter. A descending ridge distribution function, an intermediate ridge distribution function, and an ascending ridge distribution function are selected to calculate the membership degree of each evaluation level. The descending ridge distribution is mainly suitable for handling data with smaller intervals; the intermediate ridge distribution is mainly suitable for data in the middle range of the interval; and the ascending ridge distribution is mainly suitable for handling data with larger intervals.
[0063] Because the degradation values for each evaluation level—"Excellent," "Good," "Average," and "Warning"—are distributed sequentially from 0 to 1, and based on the characteristics of various ridge distributions, a descending ridge distribution is used when analyzing the membership degree of the index to the "Excellent" evaluation level; an intermediate ridge distribution is used for the membership degree analysis of the "Good" and "Average" evaluation levels; and an ascending ridge distribution is used for the membership degree analysis of the "Warning" evaluation level. The membership functions should have a certain degree of crossover, and the crossover rate is best controlled within the range of 0.2-0.6. Furthermore, based on the rules governing the change in membership degree with degradation degree, the membership functions for each level are analyzed as follows:
[0064]
[0065]
[0066]
[0067]
[0068] Among them, the variable d in the function i The degree of degradation of the i-th factor, This represents the membership degree of the i-th factor corresponding to the evaluation level j. The graphs of the membership functions for each level are shown below. Figure 2 As shown.
[0069] Example 2
[0070] This invention proposes a health assessment system for the operating status of offshore wind turbines, such as... Figure 3 As shown, it includes a data preprocessing module, a function form determination module, and a health status judgment module;
[0071] The data preprocessing module is used to acquire preprocessed wind turbine operating status data and to obtain the changing trend of wind turbine health based on the preprocessed wind turbine operating status data.
[0072] The function form determination module is used to determine the ridge distribution function form based on the changing trend of the wind turbine health status, and to determine the parameters of the ridge distribution function by fitting the pre-processed wind turbine operating status data;
[0073] The health status assessment module is used to construct a health index for wind turbines based on the parameters of the ridge distribution function, and to assess the health status of wind turbines by combining the health index and the health threshold, thereby achieving a health evaluation of the operating status of wind turbines.
[0074] Example 3
[0075] Please see Figure 4As shown, the present invention also provides an electronic device 100 for a method of evaluating the health status of offshore wind turbine operation; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0076] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the health evaluation method for the operating status of offshore wind turbines described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.
[0077] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.
[0078] The memory 101 in the electronic device 100 stores multiple instructions to implement a health assessment method for the operating status of an offshore wind turbine, and the processor 102 can execute the multiple instructions to achieve the following:
[0079] Acquire pre-processed wind turbine operating status data, and obtain the changing trend of wind turbine health based on the pre-processed wind turbine operating status data;
[0080] Based on the changing trend of wind turbine health, the form of the ridge distribution function is determined, and the parameters of the ridge distribution function are determined by fitting the preprocessed wind turbine operating status data.
[0081] Based on the parameters of the ridge distribution function, a health index for wind turbines is constructed. The health status of wind turbines is judged by combining the health index and the health threshold, thereby realizing the health evaluation of the operating status of wind turbines.
[0082] Example 4
[0083] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).
[0084] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0085] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will 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 program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0088] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for evaluating the health of offshore wind turbine operation, characterized in that, Includes the following steps: Acquire pre-processed wind turbine operating status data, and obtain the changing trend of wind turbine health based on the pre-processed wind turbine operating status data; Based on the changing trend of wind turbine health, the form of the ridge distribution function is determined, and the parameters of the ridge distribution function are determined by fitting the preprocessed wind turbine operating status data. Based on the parameters of the ridge distribution function, a health index for wind turbines is constructed. The health status of wind turbines is judged by combining the health index and the health threshold, thereby realizing the health evaluation of the operating status of wind turbines.
2. The method for evaluating the health of offshore wind turbine operation status according to claim 1, characterized in that, The changing trend of the obtained wind turbine health status is as follows: The preprocessed wind turbine operating status data first rises and then falls, forming a descending ridge shape; the preprocessed wind turbine operating status data first falls and then rises, forming an ascending ridge shape; the preprocessed wind turbine operating status data has a non-linear distribution, forming an intermediate ridge shape.
3. The method for evaluating the health of offshore wind turbine operation status according to claim 2, characterized in that, The ridge distribution function is defined in the following form: If the preprocessed wind turbine operating status data shows a descending ridge trend, then the ridge distribution function is determined to be a descending ridge distribution function; if the preprocessed wind turbine operating status data shows an ascending ridge trend, then the ridge distribution function is determined to be an ascending ridge distribution function; if the preprocessed wind turbine operating status data shows a nonlinear trend, then the ridge distribution function is determined to be an intermediate ridge distribution function.
4. The method for evaluating the health of offshore wind turbine operation status according to claim 3, characterized in that, The descending ridge distribution function is expressed as follows: Here, a1 and a2 are both fuzzy dividing points for risk levels, and the range of values for a1 and a2 are different.
5. The method for evaluating the health of offshore wind turbine operation status according to claim 3, characterized in that, The ascending ridge distribution function is expressed as follows: Here, a1 and a2 are both fuzzy dividing points for risk levels, and the range of values for a1 and a2 are different.
6. The method for evaluating the health of offshore wind turbine operation status according to claim 3, characterized in that, The intermediate ridge distribution function is expressed as follows: Among them, a1, a2, a3 and a4 are all fuzzy dividing points of risk level, and the range of values for a1, a2, a3 and a4 are different.
7. The method for evaluating the health of offshore wind turbine operation status according to claim 1, characterized in that, The parameters of the ridge distribution function can be determined using gradient descent or a genetic algorithm.
8. A health assessment system for the operating status of offshore wind turbine units, characterized in that, include: The data preprocessing module is used to acquire preprocessed wind turbine operating status data and to obtain the trend of wind turbine health status changes based on the preprocessed wind turbine operating status data. The function form determination module is used to determine the form of the ridge distribution function based on the changing trend of the wind turbine health status, and to determine the parameters of the ridge distribution function by fitting the preprocessed wind turbine operating status data. The health status assessment module is used to construct a health index for wind turbines based on the parameters of the ridge distribution function, and to determine the health status of wind turbines by combining the health index and the health threshold, thereby achieving a health evaluation of the operating status of wind turbines.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the health evaluation method for the operating status of offshore wind turbines as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the health evaluation method for the operating status of offshore wind turbines as described in any one of claims 1 to 7.