Life Prediction Method Based on Structural Fatigue Analysis of Floating Offshore Wind Power Equipment

Abstract

The present invention provides a life prediction method based on the structural fatigue analysis of floating offshore wind power equipment, which relates to the technical field of wind power generation. By obtaining the equipment structure data of the floating offshore wind power equipment, connecting a sensor network to collect environmental load information, establishing a multi-body coupling simulation model based on the equipment structure data, inputting the environmental load information into the multi-body coupling simulation model for stress amplitude and load variation, calculating the fatigue damage accumulation data based on the multi-body fatigue load spectrum, and predicting the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data. It solves the technical problem in the prior art that the unique force characteristics and dynamic response of the floating offshore wind power equipment are ignored, resulting in inaccurate life prediction of the wind power equipment. It achieves the technical effect of accurately calculating the fatigue accumulation process of the floating offshore wind power equipment, improving the operation safety and life prediction accuracy of the floating wind power equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and particularly to a life prediction method based on structural fatigue analysis of floating offshore wind power equipment. Background Art

[0002] Offshore wind farms, especially floating offshore wind power systems, have attracted more and more attention from global wind energy developers because they can be applied to deep - sea areas. However, floating wind power equipment is prone to structural fatigue damage due to being in a complex marine environment for a long time and being affected by multiple environmental loads such as wind, waves, and tides, which seriously affects the operation safety and service life of the equipment.

[0003] Traditional wind power equipment life prediction methods are mainly based on empirical data of onshore or fixed offshore wind power equipment, ignoring the unique force characteristics and dynamic responses of floating wind power equipment. Therefore, they lack an accurate description of the equipment fatigue accumulation process. In order to improve the operation safety and life prediction accuracy of floating wind power equipment, a life prediction method based on structural fatigue analysis is urgently needed.

[0004] There is a technical problem in the prior art that the unique force characteristics and dynamic responses of floating offshore wind power equipment are ignored, resulting in inaccurate life prediction of wind power equipment. Summary of the Invention

[0005] The present application provides a life prediction method based on structural fatigue analysis of floating offshore wind power equipment, which is used to solve the technical problem in the prior art that the unique force characteristics and dynamic responses of floating offshore wind power equipment are ignored, resulting in inaccurate life prediction of wind power equipment.

[0006] In view of the above problems, the present application provides a life prediction method based on structural fatigue analysis of floating offshore wind power equipment. The method includes: obtaining equipment structure data of the floating offshore wind power equipment, where the equipment structure data includes structural design and material properties; connecting a sensor network to collect environmental load information; establishing a multi - body coupling simulation model based on the structural design and the material properties, where the multi - body coupling simulation model is used to simulate the force conditions and motion responses of the floating offshore wind power equipment in a dynamic environment; inputting the environmental load information into the multi - body coupling simulation model to perform stress amplitude and load changes, generating a multi - body fatigue load spectrum; and calculating fatigue damage accumulation data based on the multi - body fatigue load spectrum, and predicting the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data.

[0007] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0008] The method provided by the embodiments of the present application obtains the equipment structure data of the floating offshore wind power equipment, where the equipment structure data includes structural design and material properties; connects a sensor network to collect environmental load information; establishes a multi-body coupling simulation model based on the structural design and the material properties, where the multi-body coupling simulation model is used to simulate the force conditions and motion responses of the floating offshore wind power equipment in a dynamic environment; inputs the environmental load information into the multi-body coupling simulation model for stress amplitude and load changes to generate a multi-body fatigue load spectrum; calculates the fatigue damage accumulation data based on the multi-body fatigue load spectrum, and predicts the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data. It achieves the technical effect of accurately calculating the fatigue accumulation process of the floating offshore wind power equipment and improving the operation safety and life prediction accuracy of the floating wind power equipment. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0010] Figure 1 It is a schematic flowchart of the life prediction method based on the structural fatigue analysis of the floating offshore wind power equipment provided by the present application;

[0011] Figure 2 It is a schematic flowchart of generating a multi-body coupling simulation model in the life prediction method based on the structural fatigue analysis of the floating offshore wind power equipment provided by the present application. Detailed Embodiments

[0012] The present application provides a life prediction method based on the structural fatigue analysis of the floating offshore wind power equipment, which is used to solve the technical problem in the prior art that the unique force characteristics and dynamic responses of the floating offshore wind power equipment are ignored, resulting in inaccurate life prediction of the wind power equipment. It achieves the technical effect of accurately calculating the fatigue accumulation process of the floating offshore wind power equipment and improving the operation safety and life prediction accuracy of the floating wind power equipment.

[0013] Next, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention. Additionally, it should be noted that for the sake of description, only the parts related to the present invention rather than all are shown in the drawings.

[0014] As Figure 1 shown, the present application provides a life prediction method based on the structural fatigue analysis of floating offshore wind power equipment, and the method includes:

[0015] Obtain the equipment structure data of the floating offshore wind power equipment, wherein the equipment structure data includes structural design and material properties.

[0016] Specifically, first, according to the overall and local structural design drawings, CAD models, analysis reports, etc. of the floating offshore wind power equipment, obtain the structural design of the floating offshore wind power equipment, including the shape, size of the wind power equipment and the relative positions of its various components, etc. Then, by referring to material manuals and technical specifications provided by suppliers, obtain the material properties of the floating offshore wind power equipment. The material properties refer to the mechanical properties of the materials used in the equipment, including but not limited to the mechanical property parameters of steel, composite materials, and connecting components, such as elastic modulus, yield strength, tensile strength, and fatigue limit and other data. After obtaining the structural design and material properties of the wind power equipment, integrate these data to comprehensively and accurately obtain the equipment structure data of the floating offshore wind power equipment, providing accurate data support for the structural fatigue analysis of the floating offshore wind power equipment.

[0017] Connect the sensor network to collect environmental load information.

[0018] Furthermore, the sensor network includes multi-point sensors installed in preset areas of the floating offshore wind power equipment, and the multi-point sensors are used to monitor marine meteorological data and generate the environmental load information; wherein, the sensor network collects data according to a preset prediction period.

[0019] Specifically, according to the design characteristics and operating environment of the floating offshore wind power equipment, the meteorological parameters to be monitored are determined, such as wind speed, wind direction, wave height, and temperature. Then, according to the meteorological parameters to be monitored, multi-point sensors are installed in the preset area of the wind power equipment. The types of sensors include, but are not limited to, anemometers, wind vanes, wave buoys, current meters, etc. For example, an anemometer calculates the wind speed by measuring the rotational speed generated by the wind on the sensor blades, while a wave buoy estimates the wave height and period by measuring the displacement of the floating body as it rises and falls with the waves. These sensors are distributed at key positions in the preset area of the equipment, such as around the floating foundation, at different heights of the tower barrel, and on the blade surface. Moreover, these sensors have high sensitivity, high stability, and the ability to resist harsh environments, ensuring that they can comprehensively monitor the offshore meteorological data. The multi-point sensors are interconnected through a wireless network to form a sensor network, making data collection and transmission more efficient. The sensor network collects data regularly according to the preset prediction period, ensuring real-time monitoring of environmental changes without wasting resources due to overly frequent data collection. The data collected by the sensors is preliminarily processed to generate environmental load information. The preliminary processing includes data cleaning to remove noise and outliers. The environmental load information refers to various external forces and influence information exerted on the floating offshore wind power equipment under offshore environmental conditions, such as wind load, wave load, and current load. By collecting and generating environmental load information through sensors, it is possible to help analyze the force conditions and motion responses of the floating offshore wind power equipment in a dynamic environment, thereby improving the fatigue life of the wind power equipment structure and the accuracy of the reliability assessment results.

[0020] Based on the structural design and the material properties, a multi-body coupling simulation model is established, where the multi-body coupling simulation model is used to simulate the force conditions and motion responses of the floating offshore wind power equipment in a dynamic environment.

[0021] Specifically, according to the structural design of the floating offshore wind power equipment, a geometric model of the offshore wind power equipment is constructed using CAD software or professional modeling tools. During the construction process, the relative positions and connection methods between components are ensured to ensure that the geometric model can accurately reflect the spatial layout of the actual structure. Then, according to the material properties, such as elastic modulus, density, and fatigue strength, material attributes are assigned to ensure that the simulation can truly reflect the performance of the materials under different loads. After completing the geometric model, finite element analysis technology is used, combined with offshore meteorological data, to create a calculation model that can simulate the force conditions and motion responses of the floating offshore wind power equipment in a dynamic environment, namely, a multi-body coupling simulation model. By establishing the multi-body coupling simulation model, it is possible to automatically simulate the interactions between components and obtain the force conditions and motion response data in a dynamic environment, providing an important theoretical basis and data support for the performance evaluation and maintenance decision-making of offshore wind power equipment.

[0022] Furthermore, a multi-body coupling simulation model is established based on the structural design and the material properties, including: performing finite element modeling according to the structural design and the material properties to generate a finite element simulation model of the device; and performing coupled analysis of the wind turbine, the floating platform, and the mooring system through the finite element simulation model of the device to generate the multi-body coupling simulation model.

[0023] Furthermore, performing finite element modeling according to the structural design and the material properties to generate a finite element simulation model of the device includes: establishing a geometric model of the device according to the structural design and the material properties; and performing mesh division on the geometric model of the device to generate the finite element simulation model of the device.

[0024] Specifically, first, according to the specific structural design and material property data of the offshore wind power equipment, a geometric model of the offshore wind power equipment is constructed using CAD software or professional 3D modeling tools. The geometric model includes all the key components and detailed content of the wind turbine, floating platform, and mooring system, accurately reflecting the shape, size, and material information of the equipment to ensure consistency with the actual structure. Then, according to the structural information, material properties, and analysis requirements of the offshore wind power equipment, a meshing operation is performed on the geometric model. Meshing refers to dividing a complex geometric shape into multiple small elements so that the mechanical behavior of each element can be calculated independently in the simulation, improving the calculation accuracy and efficiency. Through meshing, a finite element simulation model of the offshore wind power equipment is generated. The finite element simulation model is a mathematical model constructed using the finite element analysis method, which performs numerical calculations by dividing the structure of the offshore wind power equipment into many small and simple elements (called finite elements). In the finite element simulation model, according to the actual working conditions of the offshore wind power equipment in the marine environment, corresponding boundary conditions and loads are set. The boundary conditions are used to restrict the degrees of freedom of the floating platform to ensure that the offshore wind power equipment can move in the expected manner during the simulation. The loads are used to simulate the acting forces of environmental factors such as wind, waves, and currents on the equipment in the ocean. These loads are dynamically changing to ensure the authenticity and reliability of the simulation results. Finally, the finite element simulation model of the equipment is used to perform a coupled analysis of the wind turbine, floating platform, and mooring system, simulating the interaction and mutual influence between each part, considering the combined forces and motion responses of each component in the dynamic environment, and generating a multi-body coupled simulation model. The multi-body coupled simulation model not only includes the detailed structural and material property information of each component of the offshore wind power equipment but also simulates the interaction between each component. For example, the rotation of the wind turbine will affect the stability of the floating platform, and the movement of the floating platform will change the load distribution of the wind turbine. At the same time, as a key component connecting the floating platform to the seabed, the stiffness and damping characteristics of the mooring system play an important role in restricting the motion response of the floating platform. Through multi-body coupled simulation analysis, the overall performance of the offshore wind power equipment in the dynamic environment can be comprehensively evaluated, improving the accuracy and reliability of the performance evaluation of the offshore wind power equipment.

[0025] In one embodiment, as Figure 2As shown, through the finite element simulation model of the device, a coupled analysis of the wind turbine, floating platform, and mooring system is carried out to generate the multi-body coupled simulation model, including: Based on the finite element simulation model of the device, for the wind turbine, a simulation of the dynamic response of the wind turbine under gusts and sustained high wind speeds is carried out to generate the first-layer simulation model; Based on the finite element simulation model of the device, a simulation of the response of the floating platform under different wave heights and wave periods is carried out to generate the second-layer simulation model; Based on the finite element simulation model of the device, a simulation of the tension of the anchor chain and rope in the mooring system under different tidal currents and ocean currents is carried out to generate the third-layer simulation model; Integrate the first-layer simulation model, the second-layer simulation model, and the third-layer simulation model, and establish a fatigue load spectrum output layer to generate the multi-body coupled simulation model.

[0026] Specifically, according to the finite element simulation model of the device, a dynamic response simulation of the wind turbine is carried out. Under gusts and sustained high wind speeds, the wind turbine will be subjected to rapidly changing or continuously high-intensity wind loads, which will affect the stability and power generation efficiency of the entire wind turbine. Through the finite element simulation module, the deformation, vibration, and rotational speed changes of the wind turbine under these extreme wind conditions are simulated to generate the first-layer simulation model, and the first-layer simulation model is used to accurately simulate the dynamic characteristics of the wind turbine. The floating platform is an important structure for supporting the wind turbine, and its stability is directly related to the safe operation of the entire offshore wind power equipment. According to the finite element simulation model of the device, a simulation analysis of the response of the floating platform under different wave heights and wave periods is carried out to obtain the second-layer simulation model, and the second-layer simulation model is used to simulate the stability and dynamic behavior of the floating platform under various ocean wave conditions. The mooring system is composed of components such as anchor chains and ropes, which are responsible for fixing the floating platform on the sea surface to prevent it from drifting due to the action of wind and waves. Under different tidal currents and ocean currents, the mooring system will be subjected to the dual action of water flow and the movement of the floating platform, resulting in complex tension changes. Similarly, according to the finite element simulation model of the device, a simulation of the tension of the anchor chain and rope in the mooring system under different tidal currents and ocean currents is carried out, including the magnitude, direction, and variation law of the tension over time, to generate the third-layer simulation model, and the third-layer simulation model is used to simulate the safety and reliability of the mooring system in a changing environment. Finally, the generated first-layer simulation model, second-layer simulation model, and third-layer simulation model are integrated, and a fatigue load spectrum output layer is established to form a complete multi-body coupled simulation model. The fatigue load spectrum output layer combines the dynamic response information under different working conditions, including the force conditions of the wind turbine, floating platform, and mooring system under various environmental influences. The multi-body coupled simulation model comprehensively considers the interaction between the wind turbine, floating platform, and mooring system, and can provide comprehensive dynamic response and fatigue analysis data, thereby improving the accuracy and reliability of the remaining life assessment of floating offshore wind power equipment.

[0027] Further, the establishment of the fatigue load spectrum output layer includes: establishing multiple groups of fatigue load spectrum simulation samples, where any group of fatigue load spectrum simulation samples includes a first-layer simulation result sample, a second-layer simulation result sample, a third-layer simulation result sample, and an identification information sample marked with the fatigue load spectrum; training the fatigue load spectrum output layer with the first-layer simulation result sample, the second-layer simulation result sample, the third-layer simulation result sample, and the identification information sample.

[0028] Specifically, first, multiple groups of fatigue load spectrum simulation samples are established. The multiple groups of fatigue load spectrum simulation samples are realized by integrating the simulation result samples of the first-layer simulation model, the second-layer simulation model, and the third-layer simulation model. Among them, the first-layer simulation result samples are derived from the dynamic responses of wind turbines under gusts and continuous high wind speeds, the second-layer simulation result samples are based on the performance of floating platforms under different wave heights and wave periods, and the third-layer simulation result samples involve the tensile force analysis of mooring systems under tidal current and ocean current conditions. Each group of fatigue load spectrum simulation samples not only contains the three-layer simulation result samples but also contains an identification information sample marked with the fatigue load spectrum. The identification information sample is used to indicate the characteristics of the fatigue load spectrum generated under specific simulation conditions. Then, the first-layer simulation result samples, the second-layer simulation result samples, the third-layer simulation result samples, and the identification information samples are used to train the fatigue load spectrum output layer. Taking the first-layer simulation result samples, the second-layer simulation result samples, and the third-layer simulation result samples as input features and the corresponding identification information samples as target outputs, a suitable machine learning algorithm, such as a neural network, is selected to train the fatigue load spectrum output layer, enabling the fatigue load spectrum output layer to learn the mapping relationship between the input features and the output fatigue load spectrum until the performance of the fatigue load spectrum output layer reaches the preset standard. Through this training process, the fatigue load spectrum output layer can learn how to extract features from the load changes under different working conditions, thereby accurately generating the fatigue load spectrum, providing a basis for subsequent fatigue analysis, and further comprehensively and accurately evaluating and judging the fatigue performance and possible failure risks of offshore wind power equipment during long-term operation.

[0029] Input the environmental load information into the multi-body coupling simulation model for stress amplitude and load variation to generate a multi-body fatigue load spectrum.

[0030] Specifically, the environmental load information collected based on the sensor network is used as input data and input into the multi-body coupling simulation model. The multi-body coupling simulation model simulates the stress amplitude and load changes of the wind turbine, floating platform, and mooring system of the offshore wind power equipment under dynamic environments according to the input data. The stress amplitude refers to the maximum stress borne by a structure or material under a specific load, and the load change reflects the dynamic fluctuation of the load over time. Furthermore, through the simulation of the multi-body coupling simulation model, the force-bearing states and dynamic responses of each component under different working conditions can be obtained, and a multi-body fatigue load spectrum is generated. The multi-body fatigue load spectrum refers to a statistical characteristic chart or data set of the fatigue loads borne by the wind power equipment under environmental load information conditions, including stress amplitude and load change. According to the multi-body coupling simulation model, by comprehensively considering various environmental influence factors, detailed, accurate, and reliable equipment fatigue load data are obtained, and thus the accurate prediction and evaluation of the structural fatigue life of the floating offshore wind power equipment are realized.

[0031] Based on the multi-body fatigue load spectrum, calculate the fatigue damage accumulation data, and predict the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data.

[0032] Specifically, after obtaining the multi-body fatigue load spectrum, according to the multi-body fatigue load spectrum, using the fatigue cumulative damage theory, calculate the fatigue damage accumulation data of the entire offshore wind power equipment. Furthermore, based on the calculated fatigue damage accumulation data, predict the remaining life of the floating offshore wind power equipment. Based on the predicted remaining life of the floating offshore wind power equipment, the time during which the floating offshore wind power equipment can operate safely under the current environment can be obtained, providing a basis for the maintenance of offshore wind power equipment and improving the safety and reliability of the operation of offshore wind power equipment.

[0033] Further, based on the multi-body fatigue load spectrum, calculate the fatigue damage accumulation data, and predict the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data, including: using the rainflow counting method to process the multi-body fatigue load spectrum, extracting the amplitude and frequency of each load cycle, where each load cycle represents different environmental conditions; using the linear cumulative damage theory to accumulate the fatigue damage of all cycles according to the amplitude and frequency of each load cycle, and obtaining the remaining life.

[0034] Specifically, the rainflow counting method is a fatigue life prediction method used to analyze materials under complex stress histories, capable of identifying and extracting load cycles in a load spectrum, that is, the stress change process experienced by a device during operation. By using the rainflow counting method, the amplitude and frequency of each load cycle are extracted from the multi-body fatigue load spectrum. The amplitude refers to the maximum stress amplitude, and the frequency refers to the number of cycles. The amplitude and frequency of each load cycle represent the stress states of the offshore wind power equipment under different environmental conditions, such as wind speed, wave height, tidal current, etc. These different environmental conditions reflect the dynamic stresses borne by the wind power equipment in the actual marine environment. The linear cumulative damage theory is a theoretical model used to predict the fatigue failure of materials under repeated loads. This theory assumes that a certain amount of fatigue damage is generated in each load cycle of the material, and these damages can be accumulated until the total damage reaches a certain critical value, at which point the material or structure will fail. By using the linear cumulative damage theory, the amplitude and frequency of each cycle are calculated to obtain the contribution of each cycle to the overall fatigue damage, and then the fatigue damages of all cycles are accumulated to predict the remaining life of the floating offshore wind power equipment. By adopting the rainflow counting method and the linear cumulative damage theory, the safe operating time of the floating offshore wind power equipment under the current environment can be predicted, ensuring the effective management of the offshore wind power equipment before fatigue failure and improving the safety and reliability of the operation of the offshore wind power equipment.

[0035] Using the linear cumulative damage theory, based on the amplitude and frequency of each of the load cycles, accumulating the fatigue damage of all cycles to obtain the remaining life, includes: determining the material S-N curve based on the material properties; calculating the fatigue damage corresponding to the amplitude and frequency of each of the load cycles according to the material S-N curve; performing damage accumulation calculation based on the fatigue damage to generate a fatigue damage accumulation value; predicting the remaining life of the floating offshore wind power equipment under different environmental conditions according to the fatigue damage accumulation value.

[0036] Specifically, through the material database, the S-N curve of material properties is obtained. The S-N curve refers to the stress-life curve, which represents the number of cycles or life that the material can withstand under different stress amplitudes. By matching each load cycle in the multi-body fatigue load spectrum with the S-N curve of material properties, the fatigue damage degree corresponding to the amplitude and frequency of each load cycle is calculated, that is, the fatigue damage of each load cycle under specific stress conditions is determined. Then, through the linear cumulative damage theory, the fatigue damages of each load cycle are accumulated to generate an overall cumulative fatigue damage value, and the cumulative fatigue damage value reflects the total fatigue damage suffered by the offshore wind power equipment under different stresses and cycle numbers. Finally, by comparing the cumulative fatigue damage value with the fatigue failure point of the equipment, the remaining life of the floating offshore wind power equipment under different environmental conditions is predicted. By obtaining the cumulative fatigue damage value and predicting the remaining life of the floating offshore wind power equipment, it is ensured that the equipment can be managed and maintained in a timely manner before possible fatigue failure, thereby improving its operational safety and reliability.

[0037] For example, a typical floating offshore wind power equipment is selected. The capacity of its wind turbine is 12 MW, and the floating foundation is a three-column floating platform. The equipment is located in deep sea waters with a water depth exceeding 60 meters, and the platform is fixed through a multi-point mooring system. The tower height is 120 meters, the rotor diameter is 180 meters, the equipment structure is made of high-strength low-alloy steel, and the mooring system adopts a combination of multi-strand anchor chains and polyethylene ropes. Its design life is 25 years. The offshore meteorological data is collected in real time through multi-point sensors installed around the equipment. The sensor data is recorded every 10 minutes and uploaded to the central monitoring system. Among them, the average wind speed is 10 - 12 m / s, the maximum wind speed can reach 25 m / s, the wave height is between 4 - 6 meters, and the tidal current speed is 1 - 2 m / s.

[0038] Based on the finite element method, a multi-body coupling simulation model is established, including the coupling analysis of the wind turbine, floating platform, mooring system, ocean current and wind load. Among them, based on the finite element simulation model of the equipment, the force on the wind turbine under different wind speeds and the stress distribution of the tower are simulated, and the dynamic response of the rotor under gusts and continuous high wind speeds is analyzed; through the finite element simulation model of the equipment, the response of the floating platform under different wave heights and wave periods is analyzed, and the influence of the heave, roll and sway of the floating platform on the equipment structure is evaluated; for the multi-point mooring system, the tensions of the anchor chain and rope under different tidal current and ocean current conditions are analyzed, and the fatigue stress of the anchoring system and the fracture risk in extreme cases are evaluated.

[0039] Based on the actual sea condition data and structural analysis results, a multi-body fatigue load spectrum of the equipment is generated. This load spectrum includes the stress amplitude distribution of the equipment under different sea condition conditions and the frequencies at which they occur. Among them, according to sea condition conditions such as wind speed, wave height, and tidal current velocity, the operating environment of the equipment is divided into several typical working conditions, including stable conditions, medium conditions, and extreme conditions. For each working condition, the stress amplitude and the number of cycles in the stress cycle of the equipment are analyzed. Through coupled simulation, the forces and stress distributions of the wind turbine, floating platform, and mooring system under each working condition are obtained.

[0040] Based on the fatigue load spectrum, the rainflow counting method is used to statistically analyze the stress cycles under each working condition to identify the stress amplitude of each load cycle. Then, the linear cumulative damage theory is used to calculate the cumulative fatigue damage of each part, and finally the fatigue life of the equipment is obtained. Among them, according to the material characteristics of the equipment used, a suitable S-N curve is selected for fatigue life assessment to predict the remaining life of the equipment under different numbers of stress cycles; by integrating the stress cycles and stress amplitudes under different working conditions, the overall cumulative fatigue damage value of the equipment is calculated.

[0041] According to the calculation results of the cumulative fatigue damage, the remaining service life of the equipment under different environmental conditions is predicted. The results show that the fatigue damage of the equipment under extreme working conditions is significantly higher than that under stable working conditions. Under extreme wind and wave conditions, the remaining life of the equipment is reduced by 30%. According to this prediction result, it can provide a scientific basis for the maintenance and replacement plan of the equipment, and improve the safety and reliability of the operation of offshore wind power equipment.

[0042] Through the technical solutions of the above embodiments, the life prediction method based on the structural fatigue analysis of floating offshore wind power equipment provided by this application has the following technical effects:

[0043] 1. High accuracy: Combining the actual working conditions and dynamic responses of the equipment improves the accuracy of life prediction and the safety of equipment operation.

[0044] 2. Strong real-time performance: Real-time data is collected through a sensor network to achieve dynamic monitoring of fatigue damage.

[0045] 3. Wide adaptability: Applicable to different types of floating wind power equipment.

[0046] The above are only the preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this application shall be included within the protection scope of this application.

[0047] This specification and the accompanying drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and deformations to the present application without departing from the scope of the present application. Thus, provided that these modifications and deformations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and deformations therein.

Claims

1. A life prediction method based on the structural fatigue analysis of floating offshore wind power equipment, characterized in that, Including: Obtaining the equipment structure data of the floating offshore wind power equipment, wherein the equipment structure data includes structural design and material properties; Connecting a sensor network to collect environmental load information; Establishing a multi-body coupling simulation model based on the structural design and the material properties, wherein the multi-body coupling simulation model is used to simulate the force conditions and motion responses of the floating offshore wind power equipment in a dynamic environment; Inputting the environmental load information into the multi-body coupling simulation model for stress amplitude and load variation to generate a multi-body fatigue load spectrum; Calculating the fatigue damage accumulation data based on the multi-body fatigue load spectrum and predicting the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data; Among them, establishing a multi-body coupling simulation model based on the structural design and the material properties includes: Performing finite element modeling according to the structural design and the material properties to generate an equipment finite element simulation model; Performing coupling analysis of the wind turbine, floating platform and mooring system through the equipment finite element simulation model to generate the multi-body coupling simulation model; Among them, performing coupling analysis of the wind turbine, floating platform and mooring system through the equipment finite element simulation model to generate the multi-body coupling simulation model includes: Based on the equipment finite element simulation model, for the wind turbine, simulating the dynamic response of the wind turbine under gusts and continuous high wind speeds to generate a first-layer simulation model; Based on the equipment finite element simulation model, simulating the response of the floating platform under different wave heights and wave periods to generate a second-layer simulation model; Based on the equipment finite element simulation model, simulating the tension of the anchor chain and rope in the mooring system under different tidal currents and sea current conditions to generate a third-layer simulation model; Integrating the first-layer simulation model, the second-layer simulation model and the third-layer simulation model, and establishing a fatigue load spectrum output layer to generate the multi-body coupling simulation model; Among them, establishing the fatigue load spectrum output layer includes: Establishing multiple groups of fatigue load spectrum simulation samples, wherein any group of fatigue load spectrum simulation samples includes a first-layer simulation result sample, a second-layer simulation result sample, a third-layer simulation result sample and an identification information sample marked with a fatigue load spectrum; Training the fatigue load spectrum output layer with the first-layer simulation result sample, the second-layer simulation result sample, the third-layer simulation result sample, and the identification information sample.

2. The life prediction method based on the structural fatigue analysis of floating offshore wind power equipment according to claim 1, wherein, The sensor network includes multi-point sensors installed in a preset area of the floating offshore wind power equipment, and the multi-point sensors are used to monitor the offshore meteorological data to generate the environmental load information; Among them, the sensor network performs data collection according to a preset prediction period.

3. The life prediction method based on the structural fatigue analysis of floating offshore wind power equipment according to claim 1, wherein, Performing finite element modeling according to the structural design and the material properties to generate an equipment finite element simulation model includes: Establishing an equipment geometric model according to the structural design and the material properties; Performing mesh division on the equipment geometric model to generate the equipment finite element simulation model.

4. The life prediction method based on the structural fatigue analysis of floating offshore wind power equipment according to claim 1, wherein, Based on the multi-body fatigue load spectrum, calculate the fatigue damage accumulation data, and predict the remaining life of the floating offshore wind power equipment with the fatigue damage accumulation data, including: Process the multi-body fatigue load spectrum using the rainflow counting method to extract the amplitude and frequency of each load cycle, where each load cycle represents different environmental conditions; Utilize the linear cumulative damage theory to accumulate the fatigue damage of all cycles based on the amplitude and frequency of each load cycle to obtain the remaining life.

5. The life prediction method based on the structural fatigue analysis of floating offshore wind power equipment according to claim 4, wherein, Utilize the linear cumulative damage theory to accumulate the fatigue damage of all cycles based on the amplitude and frequency of each load cycle to obtain the remaining life, including: Determine the material S-N curve based on the material properties; Calculate the fatigue damage corresponding to the amplitude and frequency of each load cycle according to the material S-N curve; Perform damage accumulation calculation based on the fatigue damage to generate a fatigue damage accumulation value; Predict the remaining life of the floating offshore wind power equipment under different environmental conditions according to the fatigue damage accumulation value.

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