Life prediction method based on structural fatigue analysis of floating type offshore wind power equipment

Abstract

The invention provides a service life prediction method based on structural fatigue analysis of floating type offshore wind power equipment, and relates to the technical field of wind power generation. Equipment structure data of the floating type offshore wind power equipment is acquired, a sensor network is connected to acquire environmental load information, and a multi-body coupling simulation model is established based on the equipment structure data; environmental load information is input into the multi-body coupling simulation model for stress amplitude and load change, fatigue damage accumulation data is calculated based on the multi-body fatigue load spectrum, and the residual life of the floating type offshore wind power equipment is predicted through the fatigue damage accumulation data. The technical problem that in the prior art, unique stress characteristics and dynamic response of floating type offshore wind power equipment are ignored, and consequently the service life of the wind power equipment is predicted inaccurately is solved. The technical effects that the fatigue accumulation process of the floating type offshore wind power equipment is accurately calculated, and the operation safety and the service life prediction precision of the floating type wind power equipment are improved are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular 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, are gaining more and more attention from global wind energy developers because they can be used in deep sea areas. However, floating wind power equipment is prone to structural fatigue damage due to long-term exposure to complex marine environments and multiple environmental loads such as wind, waves, and tides, which seriously affects the equipment's operational safety and service life.

[0003] Traditional wind power equipment life prediction methods are mainly based on empirical data from onshore or fixed offshore wind power equipment, ignoring the unique stress characteristics and dynamic response of floating wind power equipment, and therefore lack an accurate description of the equipment fatigue accumulation process. In order to improve the operational 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 existing technology that the unique stress characteristics and dynamic response of floating offshore wind power equipment are ignored, resulting in inaccurate prediction of the life 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 of ignoring the unique stress characteristics and dynamic response of floating offshore wind power equipment, 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 comprising: obtaining equipment structure data of 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 stress 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 to generate a multi-body fatigue load spectrum; based on the multi-body fatigue load spectrum, calculating fatigue damage accumulation data, 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 this application have at least the following technical effects or advantages:

[0008] The method provided in the embodiment of the present application obtains the equipment structure data of the floating offshore wind power equipment, wherein the equipment structure data includes the structural design and material properties; connects the sensor network to collect environmental load information; establishes 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 stress condition and motion response of the floating offshore wind power equipment in a dynamic environment; inputs the environmental load information into the multi-body coupling simulation model to perform stress amplitude and load changes to generate a multi-body fatigue load spectrum; calculates 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. The technical effect of accurately calculating the fatigue accumulation process of the floating offshore wind power equipment and improving the operating safety and life prediction accuracy of the floating wind power equipment is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a life prediction method based on structural fatigue analysis of floating offshore wind power equipment provided in this application;

[0011] Figure 2 A schematic diagram of the process of generating a multi-body coupling simulation model in the life prediction method based on structural fatigue analysis of floating offshore wind power equipment provided in this application. DETAILED DESCRIPTION

[0012] This 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 that the unique stress characteristics and dynamic response of floating offshore wind power equipment are ignored in the prior art, resulting in inaccurate life prediction of wind power equipment. The fatigue accumulation process of floating offshore wind power equipment is accurately calculated, and the technical effect of improving the operating safety and life prediction accuracy of floating offshore wind power equipment is achieved.

[0013] Below, 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 part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

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

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

[0016] Specifically, firstly, the structural design of floating offshore wind power equipment, including the shape, size and relative position of each component of the wind power equipment, is obtained based on the overall and local structural design drawings, CAD models, analysis reports and other sources of floating offshore wind power equipment. Then, the material properties of floating offshore wind power equipment are obtained by consulting the material manual and the technical specifications provided by the supplier. Material properties refer to the mechanical properties of the materials used in the equipment, including but not limited to the mechanical properties parameters of steel, composite materials and connecting parts, such as elastic modulus, yield strength, tensile strength and fatigue limit. After obtaining the structural design and material properties of the wind power equipment, these data are integrated to form a comprehensive and accurate acquisition of the equipment structure data of floating offshore wind power equipment, providing accurate data support for the structural fatigue analysis of floating offshore wind power equipment.

[0017] Connect sensor networks to collect environmental load information.

[0018] Furthermore, 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 offshore 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 that need to be monitored, such as wind speed, wind direction, wave height and temperature, are determined. Then, according to the meteorological parameters to be monitored, multi-point sensors are installed in the preset area of ​​the wind power equipment. The sensor types include but are not limited to anemometers, wind vanes, wave buoys, current meters, etc. For example, anemometers calculate wind speed by measuring the rotation speed of the sensor blades caused by wind, while wave buoys estimate wave height and period by measuring the displacement of the floating body with the ups and downs of waves. These sensors are distributed in key positions in the preset area of ​​the equipment, such as around the floating foundation, at different heights of the tower, on the surface of the blades, etc., and these sensors have high sensitivity, high stability and resistance to harsh environments, ensuring that they can fully monitor offshore meteorological data. Multi-point sensors are interconnected through wireless networks to form a sensor network, making data collection and transmission more efficient. The sensor network collects data regularly according to the preset prediction period to ensure real-time monitoring of environmental changes without wasting resources due to too frequent data collection. After preliminary processing of the data collected by the sensors, environmental load information is generated. The preliminary processing includes data cleaning, noise and outlier removal. The environmental load information refers to various external forces and influence information applied to floating offshore wind power equipment under offshore environmental conditions, such as wind load, wave load, current load, etc. Collecting and generating environmental load information through sensors can help analyze the stress conditions and motion responses of floating offshore wind power equipment in a dynamic environment, thereby improving the fatigue life of the wind power equipment structure and the accuracy of reliability assessment results.

[0020] A multi-body coupling simulation model is established based on the structural design and the material properties, wherein the multi-body coupling simulation model is used to simulate the stress condition and motion response of the floating offshore wind power equipment in a dynamic environment.

[0021] Specifically, according to the structural design of floating offshore wind power equipment, CAD software or professional modeling tools are used to construct the geometric model of offshore wind power equipment. The construction process ensures the relative position and connection method between the components, and ensures 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, the material properties are assigned to ensure that the simulation can truly reflect the performance of the material under different loads. After completing the geometric model, finite element analysis technology is used, combined with offshore meteorological data, to create a computational model that can simulate the stress and motion response of floating offshore wind power equipment in a dynamic environment, that is, a multi-body coupling simulation model. By establishing a multi-body coupling simulation model, the interaction between the components can be automatically simulated, and the stress and motion response data in a dynamic environment can be obtained, which provides 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 equipment; 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.

[0023] Furthermore, finite element modeling is performed according to the structural design and the material properties to generate a finite element simulation model of the equipment, including: establishing a device geometric model according to the structural design and the material properties; and meshing the device geometric model to generate the device finite element simulation model.

[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 three-dimensional modeling tools. The geometric model contains all the key components and details of the wind turbine, floating platform and mooring system, and accurately reflects 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, the geometric model is meshed. Meshing refers to subdividing complex geometric shapes into multiple small units so that the mechanical behavior of each unit 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 refers to 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, simple units (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, the corresponding boundary conditions and loads are set. The boundary conditions are used to limit the degree of freedom of the floating platform to ensure that the offshore wind power equipment can move in the expected way during the simulation process. The loads are used to simulate the forces of environmental factors such as wind, waves, and currents in the ocean on the equipment. These loads are dynamically changing to ensure the authenticity and reliability of the simulation results. Finally, the equipment finite element simulation model is used to conduct a coupling analysis of the wind turbine, floating platform and mooring system, simulate the interaction and mutual influence between the various parts, consider the common force and motion response of each component in a dynamic environment, and generate a multi-body coupling simulation model. The multi-body coupling simulation model not only contains the detailed structure and material property information of each component of the offshore wind power equipment, but also simulates the interaction between the various components. 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 with the seabed, the stiffness and damping characteristics of the mooring system play an important role in constraining the motion response of the floating platform. Through multi-body coupling simulation analysis, the overall performance of offshore wind power equipment in a dynamic environment can be comprehensively evaluated, thereby improving the accuracy and reliability of offshore wind power equipment performance evaluation.

[0025] In one embodiment, Figure 2As shown, the coupling analysis of the wind turbine, the floating platform and the mooring system is performed through the equipment finite element simulation model to generate the multi-body coupling simulation model, including: based on the equipment finite element simulation model, simulating the dynamic response of the wind turbine and the wind wheel 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 cycles to generate a second-layer simulation model; based on the equipment finite element simulation model, simulating the tension of the anchor chain and the rope in the mooring system under different tidal and 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.

[0026] Specifically, according to the finite element simulation model of the equipment, the dynamic response simulation of the wind turbine is carried out. Under gusty and continuous high wind speed conditions, the wind rotor will be subjected to rapidly changing or continuous high-intensity wind loads, which will affect the stability and power generation efficiency of the entire wind turbine unit. Through the finite element simulation module, the deformation, vibration and speed change of the wind turbine unit under these extreme wind conditions are simulated to generate the first layer of simulation model, which is used to accurately simulate the dynamic characteristics of the wind turbine unit. As an important structure supporting the wind turbine unit, the stability of the floating platform is directly related to the safe operation of the entire offshore wind power equipment. According to the finite element simulation model of the equipment, the response of the floating platform under different wave heights and wave cycles is simulated and analyzed to obtain the second layer of simulation model, which is used to simulate the stability and dynamic behavior of the floating platform under various ocean wave conditions. The mooring system consists 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 wind and waves. Under different tidal and current conditions, the mooring system will be subject to the dual effects of water flow and floating platform movement, resulting in complex tension changes. Similarly, according to the finite element simulation model of the equipment, the tension of the anchor chain and rope in the mooring system under different tidal and current conditions is simulated, including the magnitude, direction and change law of the tension over time, and a third-layer simulation model is generated. 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 to establish a fatigue load spectrum output layer, thereby forming a complete multi-body coupling simulation model. The fatigue load spectrum output layer combines the dynamic response information under different working conditions, including the stress conditions of wind turbines, floating platforms and mooring systems under various environmental influences. The multi-body coupling simulation model comprehensively considers the interaction between wind turbines, floating platforms and mooring systems, 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] Furthermore, the establishment of 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 the fatigue load spectrum; and 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, firstly, multiple groups of fatigue load spectrum simulation samples are established, which 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, wherein the first layer simulation result samples are derived from the dynamic response of the wind turbine under gusts and continuous high wind speeds, the second layer simulation result samples are based on the performance of the floating platform under different wave heights and wave cycles, and the third layer simulation result samples involve the tension analysis of the mooring system under tidal and current conditions. Each group of fatigue load spectrum simulation samples contains not only three layers of simulation result samples, but also identification information samples marked with fatigue load spectrum, and the identification information sample identification information is used to indicate the characteristics of the fatigue load spectrum generated under specific simulation conditions. Then, the fatigue load spectrum output layer is trained using the first layer simulation result samples, the second layer simulation result samples, the third layer simulation result samples, and the identification information samples. The first layer simulation result samples, the second layer simulation result samples, and the third layer simulation result samples are used as input features, and the corresponding identification information samples are used as target outputs. A suitable machine learning algorithm, such as a neural network, is selected to train the fatigue load spectrum output layer so that the fatigue load spectrum output layer can 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 load changes under different working conditions, thereby accurately generating fatigue load spectra, providing a basis for subsequent fatigue analysis, and then comprehensively and accurately evaluating and judging the fatigue performance and possible failure risks of offshore wind power equipment in long-term operation.

[0029] The environmental load information is input into the multi-body coupling simulation model to perform stress amplitude and load changes 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 turbines, floating platforms and mooring systems of offshore wind power equipment in a dynamic environment based on the input data. The stress amplitude refers to the maximum stress borne by the structure or material under a specific load, and the load change reflects the dynamic fluctuation of the load over time. Then, through the simulation of the multi-body coupling simulation model, the stress state and dynamic response of each component under different working conditions can be obtained, and a multi-body fatigue load spectrum can be generated. The multi-body fatigue load spectrum refers to the statistical characteristic chart or data set of the fatigue load borne by the wind power equipment under the environmental load information conditions, including stress amplitude and load changes. According to the multi-body coupling simulation model, various environmental influencing factors are comprehensively considered to obtain detailed, accurate and reliable equipment fatigue load data, thereby realizing the accurate prediction and evaluation of the fatigue life of the floating offshore wind power equipment structure.

[0031] Based on the multi-body fatigue load spectrum, fatigue damage accumulation data is calculated, and the remaining life of the floating offshore wind power equipment is predicted using the fatigue damage accumulation data.

[0032] Specifically, after obtaining the multi-body fatigue load spectrum, the fatigue damage accumulation data of the entire offshore wind power equipment is calculated based on the multi-body fatigue load spectrum and the fatigue cumulative damage theory. Then, based on the calculated fatigue damage accumulation data, the remaining life of the floating offshore wind power equipment is predicted. 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, which provides a basis for the maintenance of the offshore wind power equipment and improves the safety and reliability of the operation of the offshore wind power equipment.

[0033] Furthermore, based on the multi-body fatigue load spectrum, fatigue damage accumulation data is calculated, and the remaining life of the floating offshore wind power equipment is predicted using the fatigue damage accumulation data, including: using a rain flow counting method to process the multi-body fatigue load spectrum to extract the amplitude and frequency of each load cycle, wherein each load cycle represents different environmental conditions; using a linear cumulative damage theory to accumulate the fatigue damage of all cycles according to the amplitude and frequency of each load cycle to obtain the remaining life.

[0034] Specifically, the rain flow counting method is a fatigue life prediction method used to analyze materials under complex stress histories. It can identify and extract load cycles in the load spectrum, that is, the stress change process experienced by the equipment during operation. The rain flow counting method is used to extract the amplitude and frequency of each load cycle 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 state of offshore wind power equipment under different environmental conditions, such as wind speed, wave height, tidal current, etc. These different environmental conditions reflect the dynamic stress that wind power equipment is subjected to 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. The theory assumes that the material will produce certain fatigue damage in each load cycle, and these damages can be accumulated until the total damage reaches a certain critical value, and the material or structure will fail. 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 damage of all cycles is accumulated to predict the remaining life of the floating offshore wind power equipment. By adopting the rain flow counting method and linear cumulative damage theory, the safe operation time of floating offshore wind power equipment under the current environment can be predicted, ensuring that offshore wind power equipment is effectively managed before fatigue failure, thereby improving the safety and reliability of offshore wind power equipment operation.

[0035] The linear cumulative damage theory is used to accumulate fatigue damage of all cycles according to the amplitude and frequency of each load cycle to obtain the remaining life, including: determining a material SN curve based on the material properties; calculating fatigue damage corresponding to the amplitude and frequency of each load cycle according to the material SN curve; performing damage accumulation calculation based on the fatigue damage to generate a fatigue damage accumulation value; and predicting the remaining life of the floating offshore wind power equipment under different environmental conditions according to the fatigue damage accumulation value.

[0036] Specifically, the SN curve of the material properties is obtained through the material database. The SN curve refers to the stress-life curve, which indicates 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 SN curve of the 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 damage of each load cycle is accumulated to generate an overall fatigue damage cumulative value, which reflects the total fatigue damage suffered by the offshore wind power equipment under different stresses and cycles. Finally, the fatigue damage cumulative value is compared with the fatigue failure point of the equipment to predict the remaining life of the floating offshore wind power equipment under different environmental conditions. By obtaining the fatigue damage cumulative 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 time before fatigue failure may occur, thereby improving the safety and reliability of its operation.

[0037] For example, a typical floating offshore wind power equipment is selected, the capacity of its wind turbine is 12MW, and the floating foundation is a three-column floating platform. The equipment is located in deep sea areas with a water depth of more than 60 meters, and the platform is fixed by a multi-point mooring system. The tower height is 120 meters, the wind rotor diameter is 180 meters, the equipment structure is made of high-strength low-alloy steel, and the mooring system is a combination of multi-strand anchor chains and polyethylene ropes. Its design life is 25 years. 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 10-12m / s, the maximum wind speed can reach 25m / s, the wave height is between 4-6 meters, and the tidal speed is 1-2m / s.

[0038] Based on the finite element method, a multi-body coupling simulation model is established, including the coupling analysis of wind turbines, floating platforms, mooring systems, ocean currents and wind loads. Among them, based on the equipment finite element simulation model, the forces of wind turbines under different wind speeds and the stress distribution of the tower are simulated, and the dynamic response of the wind wheel under gusts and continuous high wind speeds is analyzed; through the equipment finite element simulation model, the response of the floating platform under different wave heights and wave cycles is analyzed, and the impact of the heave, swing and lateral movement of the floating platform on the equipment structure is evaluated; for the multi-point mooring system, the tension of the anchor chain and rope under different tidal and ocean current conditions is analyzed, and the fatigue stress of the anchor system and the risk of fracture under extreme conditions 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. The load spectrum includes the stress amplitude distribution of the equipment under different sea conditions and its frequency of occurrence. Among them, according to sea conditions such as wind speed, wave height, and tidal speed, the operating environment of the equipment is divided into several typical working conditions, including stable working conditions, medium working conditions, and extreme working conditions. For each working condition, the stress amplitude and number of cycles of the equipment in the stress cycle are analyzed. Through coupled simulation, the force and stress distribution of the wind turbine, floating platform, and mooring system under each working condition are obtained.

[0040] Based on the fatigue load spectrum, the rain flow counting method is used to perform statistical analysis on 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 used in the equipment, the appropriate SN curve is selected for fatigue life assessment to predict the remaining life of the equipment under different stress cycle numbers; by integrating the stress cycles and stress amplitudes under different working conditions, the overall fatigue damage accumulation value of the equipment is calculated.

[0041] Based on the calculation results of 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 conditions is significantly higher than that under stable conditions. Under extreme wind and wave conditions, the remaining service life of the equipment is reduced by 30%. Based on the prediction results, a scientific basis can be provided for the maintenance and replacement plan of the equipment, and the safety and reliability of the operation of offshore wind power equipment can be improved.

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

[0043] 1. High accuracy: Combined with the actual working conditions and dynamic response of the equipment, the accuracy of life prediction and the safety of equipment operation are improved.

[0044] 2. Strong real-time performance: Real-time data collection through sensor networks enables dynamic monitoring of fatigue damage.

[0045] 3. Wide adaptability: suitable for different types of floating wind power equipment.

[0046] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0047] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A life prediction method based on structural fatigue analysis of floating offshore wind power equipment, characterized in that: include: Acquiring equipment structural data of floating offshore wind power equipment, wherein the equipment structural data includes structural design and material properties; Connect to 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 stress condition and motion response 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 to generate a multi-body fatigue load spectrum; Based on the multi-body fatigue load spectrum, fatigue damage accumulation data is calculated, and the remaining life of the floating offshore wind power equipment is predicted using the fatigue damage accumulation data.

2. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 1, characterized in that: The sensor network includes a multi-point sensor installed in a preset area of ​​the floating offshore wind power equipment, and the multi-point sensor is used to monitor offshore meteorological data and generate the environmental load information; Wherein, the sensor network collects data according to a preset prediction period.

3. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 1, characterized in that: A multi-body coupling simulation model is established based on the structural design and the material properties, including: Perform finite element modeling according to the structural design and the material properties to generate a finite element simulation model of the equipment; The multi-body coupling simulation model is generated by performing coupling analysis of the wind turbine, the floating platform and the mooring system through the equipment finite element simulation model.

4. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 3, characterized in that: Finite element modeling is performed according to the structural design and the material properties to generate a finite element simulation model of the equipment, including: Establishing a device geometric model according to the structural design and the material properties; Meshing is performed on the device geometric model to generate a finite element simulation model of the device.

5. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 3, characterized in that: The wind turbine, floating platform and mooring system coupling analysis is performed through the equipment finite element simulation model to generate the multi-body coupling simulation model, including: Based on the finite element simulation model of the equipment, simulating the dynamic response of the wind turbine and the wind rotor 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 finite element simulation model of the equipment, the tension of the anchor chain and the rope in the mooring system under different tidal and current conditions is simulated to generate a third-layer simulation model; The first layer simulation model, the second layer simulation model and the third layer simulation model are integrated, and a fatigue load spectrum output layer is established to generate the multi-body coupling simulation model.

6. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 5, characterized in that: The step of establishing the fatigue load spectrum output layer comprises: 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; The fatigue load spectrum output layer is trained with the first layer simulation result samples, the second layer simulation result samples, the third layer simulation result samples, and the identification information samples.

7. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 1, characterized in that: Based on the multi-body fatigue load spectrum, fatigue damage accumulation data is calculated, and the remaining life of the floating offshore wind power equipment is predicted by the fatigue damage accumulation data, including: Processing the multi-body fatigue load spectrum using a rain flow counting method to extract the amplitude and frequency of each load cycle, wherein each load cycle represents a different environmental condition; The linear cumulative damage theory is used to accumulate fatigue damage of all cycles according to the amplitude and frequency of each load cycle to obtain the remaining life.

8. The life prediction method based on structural fatigue analysis of floating offshore wind power equipment according to claim 7, characterized in that: The linear cumulative damage theory is used to accumulate fatigue damage of all cycles according to the amplitude and frequency of each load cycle to obtain the remaining life, including: Determining a material SN curve based on the material characteristics; Calculating fatigue damage corresponding to the amplitude and frequency of each load cycle according to the material SN curve; Perform damage accumulation calculation based on the fatigue damage to generate a fatigue damage accumulation value; The remaining life of the floating offshore wind power equipment under different environmental conditions is predicted according to the fatigue damage accumulation value.

Images