Floating type offshore wind power multi-body coupling real-time monitoring system and method

Abstract

The invention discloses a floating type offshore wind power multi-body coupling real-time monitoring system and method, and belongs to the technical field of offshore wind power monitoring, and the system comprises an information obtaining module which is used for interactively obtaining the layout environment information and device composition information of a floating type offshore wind power device; the physical attribute extraction module is used for extracting physical attributes of a plurality of structural bodies; the monitoring architecture obtaining module is used for obtaining a real-time monitoring architecture; the coupling model obtaining module is used for constructing and obtaining a multi-body coupling model; the real-time information transmission module is used for carrying out data acquisition on the floating type offshore wind power device; the fault response output module is used for outputting real-time fault response; and the fault prevention operation and maintenance module is used for performing fault prevention operation and maintenance on the floating type offshore wind power device. The technical problems of high operation risk and low maintenance efficiency caused by complex multi-body coupling structure, difficulty in real-time monitoring and untimely fault prevention of the floating type offshore wind power device in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind power monitoring, and in particular to a floating offshore wind power multi-body coupling real-time monitoring system and method. Background Art

[0002] Floating offshore wind power technology is an emerging technology developed to cope with the development of deep-sea wind resources, and has great development potential. As offshore wind power projects face complex environmental conditions in deep-sea areas, such as strong winds, large waves, and ocean currents, traditional fixed wind turbines are not suitable for these conditions, and floating wind power technology has emerged. Floating wind power systems deploy wind turbines in deep-sea areas through floating platforms and mooring systems to generate electricity using wind energy. Its advantage is that it can be deployed in deeper waters, farther from the shore, to capture more stable and powerful wind energy, thereby improving power generation efficiency.

[0003] At present, the monitoring methods of floating offshore wind power plants are still relatively limited, mainly relying on the traditional fixed sensor deployment method, which is difficult to cope with the multi-body coupling characteristics and dynamic changes of floating platforms. In addition, the existing monitoring systems are mainly based on single or local monitoring, lacking an overall dynamic monitoring method for coupled structures, resulting in lags in fault warning and operation and maintenance decisions, and unable to respond to complex marine environmental changes in a timely manner. Therefore, there is an urgent need for a new technical solution that can monitor the multi-body coupling status of floating wind power plants in real time, and conduct fault warnings and risk analysis in a timely manner, so as to improve their safety and operation and maintenance efficiency. Summary of the invention

[0004] The present application provides a real-time monitoring system and method for multi-body coupling of floating offshore wind power plants, aiming to solve the technical problems in the prior art of complex multi-body coupling structure of floating offshore wind power plants, difficulty in real-time monitoring, and untimely fault prevention, resulting in high operating risks and low maintenance efficiency.

[0005] In view of the above problems, the present application provides a floating offshore wind power multi-body coupling real-time monitoring system and method.

[0006] In a first aspect disclosed in the present application, a floating offshore wind power multi-body coupling real-time monitoring system is provided, the system comprising an information acquisition module, used to interactively obtain the layout environment information and device configuration information of the floating offshore wind power device; a physical property extraction module, used to extract and obtain multiple structural physical properties of multiple coupling structures in the floating offshore wind power device from the device configuration information; a monitoring architecture acquisition module, used to perform sensor scheduling configuration of the multiple coupling structures according to the multiple structural physical properties, and obtain a real-time monitoring architecture; a coupling model acquisition module, used to construct and obtain a multi-body coupling model based on the layout environment information and the device configuration information; a real-time information transmission module, used for the real-time monitoring architecture to collect data from the floating offshore wind power device and transmit the real-time wind power information obtained to the multi-body coupling model through a distributed sensor network; a fault response output module, used to perform associated operation risk analysis based on the multi-body coupling model after the multi-body coupling model is dynamically updated according to the real-time wind power information, and output a real-time fault response; a fault prevention and operation and maintenance module, used to perform fault prevention and operation and maintenance on the floating offshore wind power device according to the real-time fault response.

[0007] Another aspect disclosed in the present application provides a real-time monitoring method for multi-body coupling of floating offshore wind power, the method comprising interactively obtaining layout environment information and device configuration information of a floating offshore wind power device; extracting multiple structural physical properties of multiple coupling structures in the floating offshore wind power device from the device configuration information; performing sensor scheduling configuration of the multiple coupling structures according to the multiple structural physical properties to obtain a real-time monitoring architecture; constructing a multi-body coupling model based on the layout environment information and the device configuration information; the real-time monitoring architecture performs data collection on the floating offshore wind power device to obtain real-time wind power information, which is transmitted to the multi-body coupling model through a distributed sensor network; after the multi-body coupling model performs dynamic operation update according to the real-time wind power information, an associated operation risk analysis is performed based on the multi-body coupling model to output a real-time fault response; and fault prevention operation and maintenance of the floating offshore wind power device is performed according to the real-time fault response.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By adopting a technical solution of interactively acquiring the layout environment information and device composition information of floating offshore wind power devices, extracting the physical properties of multiple coupled structures, scheduling and configuring sensors, real-time data acquisition and dynamic updating of multi-body coupling models, the problems of complex multi-body coupling monitoring, poor data real-time performance and difficulty in timely prevention of faults in traditional offshore wind power devices are solved. The technical effect of real-time monitoring of the operating status of floating offshore wind power devices, improving the speed and accuracy of fault response, and optimizing the fault prevention and operation and maintenance processes is achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A structural schematic diagram of a floating offshore wind power multi-body coupling real-time monitoring system is provided for an embodiment of the present application.

[0012] Figure 2 A flow chart of a method for real-time monitoring of floating offshore wind power multi-body coupling is provided for the embodiment of the present application;

[0013] Explanation of the reference numerals: information acquisition module 11 , physical property extraction module 12 , monitoring architecture acquisition module 13 , coupling model acquisition module 14 , real-time information transmission module 15 , fault response output module 16 , fault prevention and operation and maintenance module 17 . DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The embodiment of the present application provides a real-time monitoring system and method for multi-body coupling of floating offshore wind power. The layout environment information and device composition information of the floating offshore wind power device are interactively obtained, and then the physical properties of multiple coupling structures are extracted. On this basis, a real-time monitoring architecture is constructed through sensor scheduling configuration, and dynamically updated in combination with a multi-body coupling model. The method realizes fault response and prevention of wind power devices through real-time data collection and associated risk analysis, aiming to improve the operating safety and maintenance efficiency of wind power equipment.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0017] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a floating offshore wind power multi-body coupling real-time monitoring system, the system comprising:

[0018] The information acquisition module 11 is used to interactively acquire the deployment environment information and device configuration information of the floating offshore wind power device.

[0019] Specifically, a floating offshore wind turbine refers to an offshore wind turbine installed on a floating platform, which can adapt to the deep sea environment and maintain stability through the anchoring system of the floating structure. The deployment environment information refers to the external environmental conditions of the floating wind turbine. The device composition information refers to the structure and design parameters of the floating wind turbine itself.

[0020] First, environmental information and equipment parameters are comprehensively collected through a variety of data sources and tools. When obtaining deployment environment information, meteorological monitoring systems (such as wind speed sensors, meteorological satellite data) are used to monitor meteorological data such as wind speed and wind direction in real time. At the same time, ocean monitoring systems (such as wave sensors, current meters) are combined to obtain ocean environmental data such as wave height, current direction and intensity. In addition, seabed topography data can be collected through sonar systems or seabed geological exploration tools (such as multi-beam echo sounders) to ensure the accuracy of seabed characteristics. Device composition information can be obtained through interaction with wind power equipment manufacturers and engineering design teams. The device composition information includes the specific parameters of the floating platform and wind turbines.

[0021] This module ensures comprehensive and real-time collection of dynamic information about the environment in which the wind turbine is located and its own structural parameters through the coordination of multi-source sensor systems, data acquisition tools and engineering design software, thus laying a solid foundation for subsequent monitoring and risk analysis.

[0022] The physical property extraction module 12 is used to extract and obtain multiple structural physical properties of multiple coupled structures in the floating offshore wind power device from the device configuration information.

[0023] Specifically, the coupled structure refers to the individual parts or modules in a wind turbine, which are connected to each other in a certain way to form a whole. Physical properties refer to the physical characteristics of each coupled structure, usually including size, shape, mass, material properties, mechanical properties, etc.

[0024] By analyzing the design drawings or computer modeling data, the location, connection method and basic geometric characteristics of each coupling structure are determined. After these coupling structures are determined, their physical properties are further extracted.

[0025] Specifically, the design files automatically generated by the 3D modeling software are used to obtain the geometric information of each structure, including its size, shape, cross-sectional characteristics, etc. Material properties are obtained by consulting the design documents or data sheets provided by the manufacturer to determine the material properties of each coupled structure, including density, elastic modulus, Poisson's ratio and other material properties. Mechanical analysis is performed on each coupled structure to calculate its mechanical properties such as strength, stiffness, and stress-strain distribution.

[0026] This module extracts the physical properties of the coupled structure from the device composition information, which not only enables a comprehensive understanding of the specific structure and performance of the floating offshore wind power device, but also provides accurate basic data for subsequent sensor deployment, dynamic analysis and fault prediction.

[0027] The monitoring architecture acquisition module 13 is used to perform sensor scheduling configuration of the multiple coupled structures according to the physical properties of the multiple structures to obtain a real-time monitoring architecture.

[0028] Specifically, sensor scheduling configuration refers to the reasonable arrangement of the location, type and quantity of sensors according to the physical characteristics and monitoring requirements of each coupled structure to achieve real-time monitoring of key parts of the device. Real-time monitoring architecture refers to the monitoring system architecture formed after sensor scheduling, which is used to continuously collect the operating status, external environment data and other key information of floating offshore wind power devices to achieve real-time monitoring and analysis.

[0029] First, the physical properties of each coupled structure are analyzed to identify the weak points or key parts of the structure. By simulating the forces and deformations of each coupled structure under various environmental stresses, stress concentration areas, parts with high vibration frequencies, and key nodes prone to fatigue can be identified. For each coupled structure, key physical properties such as the vibration characteristics of the wind turbine tower, the bending stress of the blades, the inclination and stability of the floating platform, etc., determine the key parts for sensor placement.

[0030] Then, select the appropriate sensor type according to the monitoring requirements of different parts. Specifically, the accelerometer is suitable for monitoring the vibration of the wind turbine tower, the strain gauge is used to monitor the stress and fatigue of the blades, the inclination sensor is suitable for monitoring the tilt angle of the platform, and the pressure sensor can be used to measure the underwater pressure on the platform. On this basis, determine the accuracy, weather resistance, response time and other performance of the sensor to ensure that it can adapt to the complex conditions in the marine environment.

[0031] Finally, formulate the sensor deployment strategy. According to the deployment strategy analysis tool, optimize the simulation and deploy the sensors at the most critical monitoring nodes.

[0032] This module rationally configures sensors according to the physical properties of the structure, which can effectively improve the accuracy and efficiency of monitoring. After the sensors are arranged at key parts of the structure, the real-time monitoring architecture formed can continuously monitor the operating status of the floating wind turbine, detect potential faults and risks in time, reduce the downtime of the device, and extend its service life.

[0033] The coupling model acquisition module 14 is used to construct and acquire a multi-body coupling model based on the deployment environment information and the device configuration information.

[0034] Specifically, the multi-body coupling model refers to a mathematical model formed by modeling the interaction between multiple coupled structures of a wind turbine (such as a floating platform, tower, blades, and anchoring system) and the environment in which they are located. This model takes into account the interaction forces between multiple structures and the influence of the external environment on them, and can dynamically reflect the behavior of wind turbines under different environmental conditions.

[0035] First, analyze the coupled structures of the floating offshore wind turbine. By analyzing the device composition information, multiple coupled structures in the wind turbine can be disassembled, such as the floating platform, wind turbine tower, blades, anchoring system, etc. Each structure has its own physical properties (such as size, mass, stiffness, material properties, etc.), which will be used as input parameters in the model. Then, based on these physical parameters, build the models of each individual structure.

[0036] Next, based on the deployment environment information, the environmental conditions of the wind turbine are obtained, such as wind speed, wave characteristics, current direction, seabed characteristics, etc. These environmental information are used as external input conditions to affect the stress state of each coupled structure.

[0037] After having the physical models and environmental data of each coupled structure, a multi-body coupling model is constructed. First, the connection relationship between each structure is modeled to establish the dynamic equations between the structures. Then, based on the forces of the marine environment, the mechanical equations acting on each structure are constructed. Finally, the entire multi-body coupling model is solved and simulated. During the simulation process, the operating behavior of the wind turbine under different environmental conditions can be simulated.

[0038] This module can realize dynamic simulation and accurate analysis of floating offshore wind power devices by constructing a multi-body coupling model based on the deployment environment and device composition information.

[0039] The real-time information transmission module 15 is used for the real-time monitoring framework to collect data from the floating offshore wind power device and transmit the real-time wind power information obtained to the multi-body coupling model through a distributed sensor network.

[0040] Specifically, real-time wind power information refers to data collected from various sensors of wind power devices that reflect the real-time operating status of the device, including wind speed, blade speed, platform inclination, etc. Distributed sensor network refers to a network composed of sensors installed at various key parts of wind power devices, which can realize distributed data collection, processing and transmission.

[0041] First, the key components of wind turbines will be equipped with various types of sensors for data collection. For example, accelerometers can monitor the vibration of wind turbine towers and floating platforms, strain gauges are used to monitor the stress of wind turbine blades, inclination sensors monitor the inclination of the platform, and anemometers and wind vanes are used to monitor wind speed and direction. All these sensors are distributed in different locations of the device, forming a distributed sensor network covering the entire wind turbine. The sensors will continuously collect data during the operation of the device, and these data are real-time wind power information, such as wind speed changes, tower vibration frequency, platform inclination angle, etc.

[0042] Secondly, after data collection is completed, the data collected by the sensor is transmitted to the central monitoring system through wireless transmission technology. Each sensor has an independent communication path in the distributed network, ensuring real-time data transmission, and data sharing and collaboration between devices can be achieved through the Internet of Things (IoT) technology.

[0043] Through real-time monitoring architecture and distributed sensor networks, this module can monitor the operating status of floating offshore wind power devices in real time and accurately, and dynamically interact with multi-body coupling models through data transmission to achieve real-time analysis and prediction.

[0044] The fault response output module 16 is used to perform an associated operation risk analysis based on the multi-body coupling model and output a real-time fault response after the multi-body coupling model is dynamically updated according to the real-time wind power information.

[0045] Specifically, the associated operation risk analysis refers to analyzing the risks faced by wind power devices under current conditions based on a multi-body coupling model, and evaluating the impact of these risks on the operation of the device. Real-time fault response means that when an abnormal situation or potential fault is detected through risk analysis, the system will take immediate response measures, including alarms, adjusting wind turbine operating parameters, or suggesting operation and maintenance interventions.

[0046] First, when the wind turbine sensor collects real-time data, this data is transmitted to the model for dynamic updating of the multi-body coupling model. The updating process usually takes the operating data collected by the sensor as input and adjusts the parameters in the model.

[0047] Next, based on the updated multi-body coupling model, the associated operation risk analysis is performed. The current operation risk of the device is evaluated by analyzing the status of each coupled structure in the model. Once the risk analysis shows that the wind power device is facing a fault or abnormality, the system will output a real-time fault response based on the risk level.

[0048] This module can significantly improve the safety and reliability of floating offshore wind power devices through real-time risk analysis and fault response based on multi-body coupling models.

[0049] The fault prevention operation and maintenance module 17 is used to perform fault prevention operation and maintenance on the floating offshore wind power device according to the real-time fault response.

[0050] Specifically, fault prevention operation and maintenance refers to predicting and analyzing the potential failure risks of the equipment, performing maintenance and management in advance, and avoiding equipment damage or downtime.

[0051] First, real-time fault response has identified that some components of the floating offshore wind turbine are on the verge of failure through early risk analysis and diagnosis. For example, the system detects an increase in the tilt angle of the floating platform, abnormal vibration frequency of the tower, or excessive stress in the anchoring system. Based on these fault signals, the system will issue an alarm through the built-in early warning mechanism and initiate the fault prevention operation and maintenance program.

[0052] Through real-time fault response and fault prevention operation and maintenance, floating offshore wind power devices can achieve more efficient and safe long-term operation, avoid major losses caused by sudden failures, and reduce operation and maintenance costs.

[0053] In some embodiments, the sensor scheduling configuration of the multiple coupled structures is performed according to the physical properties of the multiple structures to obtain a real-time monitoring architecture, and the execution steps include:

[0054] A plurality of monomer mathematical models are constructed according to the physical properties of the plurality of structures; a sensor deployment analysis is performed based on the plurality of monomer mathematical models to obtain a plurality of sensor deployment strategies; and a sensor scheduling configuration of the plurality of coupled structures is performed with the plurality of sensor deployment strategies as constraints to obtain a real-time monitoring architecture.

[0055] Specifically, a monomer mathematical model refers to a mathematical model established for a coupled structure, which describes its physical behavior and dynamic response characteristics under specific conditions, and is usually expressed through equations such as mechanics and structural mechanics.

[0056] First, a mathematical model of each coupled structure is constructed based on its physical properties. The purpose of each monomer mathematical model is to accurately describe the dynamic behavior of the structure in the marine environment.

[0057] Next, the sensors are analyzed and arranged based on the monomer mathematical model. The sensors should be arranged at important parts of the structure, such as key stress points, parts with frequent vibrations, or locations of vulnerable parts, to ensure that the data can reflect the operating status of the equipment. The number, location, and type of sensors are determined by analyzing the stress concentration areas and dynamic response characteristics of each structure. After the sensor layout strategy is determined, the scheduling of the sensors is configured. According to the dynamic response frequency of different structures, the acquisition frequency and transmission priority of the sensors are reasonably arranged. For example, some structures require high-frequency sampling, while other structures only require low-frequency monitoring. By reasonably configuring the scheduling, the energy consumption of the system can be reduced and the monitoring efficiency can be improved.

[0058] By constructing multiple monomer mathematical models, formulating sensor deployment strategies, and performing reasonable sensor scheduling and configuration, floating offshore wind power devices can achieve an efficient and accurate real-time monitoring architecture, improving the safety and reliability of equipment operation.

[0059] In some embodiments, performing sensor deployment analysis based on the multiple monomer mathematical models to obtain multiple sensor deployment strategies includes:

[0060] A first structural physical property of a wind turbine in the floating offshore wind power device is extracted from the device composition information, wherein the wind turbine is any one of the multiple coupled structures; multiple component structural information and multiple component behavior information of multiple key components are extracted based on the first structural physical property; after constructing multiple component structural models based on the multiple component structural information, the multiple component structural models are assembled to obtain a unit structural model; key monitoring nodes of the multiple key components are located based on the unit structural model and the multiple component behavior information to obtain a first sensor deployment strategy; and by analogy, sensor deployment analysis is performed based on the multiple monomer mathematical models to obtain the multiple sensor deployment strategies.

[0061] Specifically, component structure information refers to the detailed information such as the geometric structure and material composition of key components, which is used to build physical models and describe the behavior of components under different working conditions. Component behavior information refers to the mechanical performance of components during operation, such as stress, vibration, temperature change, etc., reflecting their actual working status.

[0062] First, the physical properties of the wind turbine are extracted from the device configuration information. The wind turbine is a key part of the coupled structure, and its physical properties include the length and width of the blades, material strength, tower height, and the wave resistance of the base float. These data can provide an accurate physical basis for subsequent model construction.

[0063] Next, the structural and behavioral information of key components of the wind turbine are further extracted based on the physical properties of the wind turbine. After obtaining the structural information of the components, the structural models of each key component are constructed using this information. These models describe the stress and deformation of each component under different working conditions through structural mechanics or finite element analysis. Then, the models of each component are assembled into a complete wind turbine structural model to ensure that the interaction between different components can reflect the real coupling relationship.

[0064] By analyzing the unit structure model, the key monitoring nodes in the wind turbine are identified. These nodes are usually areas of stress concentration or frequent vibration, and are high-risk points for equipment failure. Based on these monitoring nodes, a sensor deployment strategy is formulated to ensure that the sensors can accurately collect data reflecting the health status of the equipment.

[0065] Similarly, analyze multiple coupled structures and obtain sensor deployment strategies: After analyzing the wind turbine, perform the same model construction and sensor deployment analysis on other coupled structures in turn. Each coupled structure has its own specific monitoring requirements, and the corresponding model analysis is used to determine the respective sensor deployment strategies.

[0066] Through sensor deployment analysis based on multiple monomer mathematical models, an efficient and accurate real-time monitoring architecture can be established for floating offshore wind turbines, thereby improving the operational safety and maintenance efficiency of wind turbines.

[0067] In some embodiments, the key monitoring nodes of the multiple key components are located according to the unit structure model and the behavior information of the multiple components to obtain a first sensor deployment strategy, and the execution steps include:

[0068] The behavior information of the multiple components is fitted to the unit structure model to obtain a unit operation behavior model; based on the unit operation behavior model, the operation status data of the multiple key components are extracted to obtain multiple groups of operation status indicators; based on the multiple groups of operation status indicators, the key monitoring nodes of the multiple key components are located in the unit operation behavior model to obtain multiple groups of sensor deployment nodes; the wind turbine unit, multiple key components and multiple groups of sensor deployment nodes are stored in an associated manner to constitute the first sensor deployment strategy.

[0069] Specifically, the unit operation behavior model refers to a dynamic model generated by integrating the behavior information of components into the unit structure model, which can simulate the real-time operation status of the entire wind turbine under different environmental conditions. Operation status indicators refer to performance indicators obtained by monitoring the behavior data of key components, such as vibration frequency, temperature, stress amplitude, etc., which can reflect the health status and potential failure risks of components. Key monitoring nodes refer to parts that need to be monitored. These nodes are usually located in areas with concentrated stress or easy to damage, and are high-incidence points of failure. Sensor layout nodes are sensor installation locations selected based on monitoring needs. The selection of these nodes directly affects the accuracy and effectiveness of monitoring data.

[0070] First, the behavior information extracted from each key component (such as blade vibration characteristics, tower stress distribution, gearbox temperature change) is fitted into the structural model of the entire wind turbine. The actual behavior information of each component is coupled with the mathematical model of the overall structure of the unit to form a unit operation behavior model.

[0071] After obtaining the unit operation behavior model, sensors are used to extract real-time operation status data from different components, including blade vibration frequency, gearbox temperature, tower stress amplitude, etc. Through these real-time data, operation status indicators such as vibration amplitude, temperature change rate, stress distribution, etc. are further calculated and extracted.

[0072] Based on the multiple sets of operating status indicators extracted above, the stress concentration areas or vulnerable areas of key components are analyzed to determine the placement of sensors. These areas are usually where the equipment is subjected to greater stress or vibrates more violently during operation. Through further analysis of the unit operation behavior model, these key monitoring nodes are located to ensure that sensors can be installed in the most effective locations to monitor the operating status of the equipment.

[0073] After determining the key monitoring nodes, formulate a sensor deployment strategy based on the locations of these nodes. This includes the type of sensor (such as vibration sensor, temperature sensor, stress sensor, etc.), installation quantity, installation method, etc. Ensure that these sensors can collect key operating data in real time.

[0074] After the sensors are deployed, the key components of the wind turbine, their corresponding operating status indicators and monitoring nodes are stored in association. This ensures the traceability of monitoring data and the maintainability of the system. By storing this associated information, data support can be provided for subsequent fault diagnosis and equipment operation and maintenance.

[0075] In some embodiments, the multi-body coupling model is constructed based on the deployment environment information and the device configuration information, and the execution steps include:

[0076] Disassemble the device composition information to obtain multiple structural relationships and multiple structural boundary conditions of the multiple coupled structures; extract multiple external action information of the multiple coupled structures from the layout environment information; after obtaining multiple structural characteristics of the multiple coupled structures through online search, configure dynamic equations based on the multiple structural characteristics to obtain multiple interaction equations; after integrating the multiple monomer mathematical models into the pre-call framework, couple the multiple monomer mathematical models according to the multiple structural relationships and multiple interaction equations to obtain an initial coupling model; merge the layout environment information as a boundary condition with the initial coupling model to obtain the multi-body coupling model.

[0077] Specifically, boundary conditions refer to the constraints that define the structure in the operating environment, such as fixed ends and free ends, which determine the response of the structure under external conditions such as force, displacement, and pressure. External action information refers to the impact of the environment on the coupled structure, including natural forces such as wind, waves, and currents. Structural characteristics The physical and mechanical properties of the coupled structure, such as material stiffness, mass distribution, and damping, determine the dynamic response behavior of the structure.

[0078] First, the composition information of the floating offshore wind turbine is disassembled to extract the relationship between each coupled structure. For example, the wind turbine is connected to the sea surface through a floating platform, and the mooring system fixes the platform to the seabed.

[0079] Next, the external force information such as wind, waves and ocean currents is extracted from the actual environment where the floating wind turbine is located. This external force information determines the force conditions of each coupled structure in the environment.

[0080] Then, the physical properties of each coupled structure are obtained by searching the database or the Internet. Based on these structural properties, the dynamic equations of each coupled structure are constructed, which describe their motion laws under external action.

[0081] The previously constructed monomer mathematical model is integrated into a pre-call framework and coupled based on the mutual relationship and dynamic equations of the structures. Coupling means that the structures not only move independently but also affect each other.

[0082] Finally, the extracted deployment environment information (such as wind, wave, and current conditions) is used as boundary conditions to merge with the initial coupling model, further optimizing the accuracy of the model and generating a multi-body coupling model that can reflect the floating offshore wind power device in the actual environment. This model can be used to simulate the dynamic response of the device in a complex environment.

[0083] The multi-body coupling model can accurately simulate the motion state of wind turbines under complex external forces such as wind, waves, and currents, improving the design and operation safety of wind turbines and improving the wind and wave resistance and operation stability of the entire device.

[0084] In some embodiments, after the multi-body coupling model is dynamically updated according to the real-time wind power information, an associated operation risk analysis is performed based on the multi-body coupling model to output a real-time fault response, and the execution steps include:

[0085] Interactively obtain multiple groups of sample operation data sets under multiple sample fault modes; construct and generate a fault state identification model based on the multiple groups of sample operation data sets; when the duration for the dynamic operation update of the multi-body coupling model according to the real-time wind power information reaches a preset duration constraint, extract associated operation data of the multi-body coupling model to obtain an associated operation data set; synchronize the associated operation data set to the fault state identification model for fault mode matching to obtain a real-time fault mode and a real-time fault level; after setting the warning signal level according to the real-time fault level, output the real-time fault mode as a real-time fault response.

[0086] Specifically, the associated operation data set refers to the real-time data extracted from the multi-body coupling model, including the operation information of wind turbines, floating platforms and mooring systems in their current state. Fault pattern matching refers to comparing the real-time monitored operation data with historical fault patterns to identify whether the current equipment is in a fault state and the severity of the fault mode. Real-time fault response refers to the warning or prompt output when the system detects a fault, which usually includes the type of fault, severity and recommended treatment measures.

[0087] First, the operation data of floating offshore wind turbines under various fault modes are collected through historical data or experimental data. Through the analysis of historical operation data, the sensor stress data of the tilt of the floating platform under strong wind and wave conditions, the inclination angle of the floating platform, and the stress conditions of the mooring system are obtained as sample fault data.

[0088] By using machine learning algorithms (such as support vector machines, neural networks, etc.) or statistical analysis methods, a fault state recognition model is trained based on a sample data set. Its goal is to establish a classification model that can distinguish between fault states and healthy states by learning the operating data under normal operation and different fault modes.

[0089] When the multi-body coupling model is dynamically updated according to real-time wind power information and reaches a preset time (for example, every 10 minutes or longer), the system will extract the associated operating data from the multi-body coupling model, including the stress of the wind turbine, the inclination of the floating platform, and the tension of the mooring system. These associated operating data reflect the current operating status of the system.

[0090] The extracted associated operation data set will be transmitted to the fault state recognition model in real time. The model will match this data with the previously trained fault mode to determine whether the current system is in a fault state and determine the specific fault mode and fault level. For example, if the platform's inclination angle exceeds the safe range, it means that the platform has a tilt fault.

[0091] By constructing a fault status identification model, the fault status of floating offshore wind power devices can be detected in real time, and fault responses can be output in a timely manner to improve the safety and stability of the system.

[0092] In some embodiments, the fault state recognition model is constructed based on the plurality of sample operation data sets, and the execution steps include:

[0093] From the multiple groups of sample operation data sets, call and obtain a first group of sample operation data sets corresponding to a first sample fault mode, wherein the first sample fault mode is any one of the multiple sample fault modes; split the first group of sample operation data sets based on the fault level to obtain K sample operation data sets; call the first sample operation data set corresponding to the first fault level from the K sample operation data sets; split the first group of sample operation data sets based on the multiple coupling structures to obtain multiple sample operation data combinations; perform fault feature aggregation on the multiple sample operation data combinations to obtain a first group of fault deviation windows of the first sample fault mode at the first fault level; and so on, obtain K groups of fault deviation windows of the first sample fault mode at K fault levels; associate and store the first sample fault mode, K fault levels and K groups of fault deviation windows to obtain a first fault mode identification branch; and so on, construct multiple fault mode identification branches of the multiple sample fault modes; connect the multiple fault mode identification branches in parallel to obtain the fault state identification model.

[0094] Specifically, the fault level refers to the classification of fault modes according to the severity of the fault, which is usually divided into minor, moderate and severe levels, and each level corresponds to different maintenance needs and risks. The fault deviation window refers to the difference between the normal operating state and the fault state. By analyzing the abnormalities of the equipment operation data, the window area that deviates from the normal operating range is determined to determine the occurrence of a fault. The fault mode recognition branch refers to the recognition model branch constructed for a specific fault mode and different fault levels, which is used to identify and classify specific fault modes and levels.

[0095] First, select the data set related to the specific fault mode from the stored multiple sets of sample operation data sets. Split the first set of sample operation data sets according to the fault level, for example, split the fault into K sample operation data sets according to the three levels of minor, medium and severe. Each data set corresponds to a fault condition of different severity, so that different stages of the fault can be identified more accurately.

[0096] From the K sample operation data sets after splitting, select the sample operation data set corresponding to the first fault level (e.g., minor fault) for subsequent analysis and modeling. The above sample operation data are further split according to different coupling structures of the equipment to obtain multiple sample operation data combinations.

[0097] The split operation data are combined to analyze and extract the key fault features of each component in the current fault mode. These features include the deviation of the stress, displacement, temperature, etc. of each component from the normal operating value, forming a fault deviation window.

[0098] According to different fault levels, the fault deviation window of each level is calculated separately. For example, a minor fault has only a slight stress anomaly, while a serious fault shows obvious stress and vibration anomalies. These deviation windows can be used for subsequent real-time fault detection.

[0099] For each sample fault mode and its fault level, multiple fault mode recognition branches are constructed. Each branch is used to identify fault characteristics of different levels under a specific fault mode. After multiple fault mode recognition branches are connected in parallel, a complete fault state recognition model is formed.

[0100] By building multiple fault mode recognition branches, the system can accurately identify the type and severity of faults based on different fault levels and the specific operating conditions of the equipment structure, thereby improving the accuracy of fault detection.

[0101] In summary, the floating offshore wind power multi-body coupling real-time monitoring system provided by the embodiment of the present application has the following technical effects:

[0102] 1. By building a multi-body coupling real-time monitoring model for floating offshore wind turbines, the operating status of each component of the device can be fully monitored. Based on the coupling analysis of real-time data, the ability to fully control the operation of the device in a complex dynamic environment is improved, and the stability and safety of the wind turbine are significantly improved.

[0103] 2. Through the reasonable deployment and scheduling of sensors, an effective real-time monitoring architecture is formed. The sensor deployment strategy based on the monomer mathematical model ensures accurate monitoring of key nodes, helps improve the accuracy of data collection and the overall efficiency of the monitoring system, and reduces blind spots.

[0104] 3. By combining the composition information of the device and the layout environment information, a multi-body coupling model is constructed to realize the coupling analysis between complex structures. The model can dynamically respond to environmental changes and improve the adaptability and operational stability of offshore wind power devices in different environments.

[0105] Embodiment 2 is based on the same inventive concept as the floating offshore wind power multi-body coupling real-time monitoring system in the above embodiment. Figure 2 As shown, the embodiment of the present application provides a real-time monitoring method for multi-body coupling of floating offshore wind power, the method comprising:

[0106] Step S100: interactively obtaining deployment environment information and device configuration information of a floating offshore wind power device;

[0107] Step S200: extracting and obtaining a plurality of structural physical properties of a plurality of coupled structural bodies in the floating offshore wind power device from the device configuration information;

[0108] Step S300: performing sensor scheduling configuration of the plurality of coupled structures according to the physical properties of the plurality of structures to obtain a real-time monitoring architecture;

[0109] Step S400: constructing a multi-body coupling model based on the deployment environment information and device configuration information;

[0110] Step S500: the real-time monitoring framework collects data from the floating offshore wind power device and transmits the real-time wind power information obtained to the multi-body coupling model through a distributed sensor network;

[0111] Step S600: after the multi-body coupling model is dynamically updated according to the real-time wind power information, an associated operation risk analysis is performed based on the multi-body coupling model, and a real-time fault response is output;

[0112] Step S700: performing fault prevention operation and maintenance on the floating offshore wind power device according to the real-time fault response.

[0113] Furthermore, the sensors of the multiple coupled structures are scheduled and configured according to the physical properties of the multiple structures to obtain a real-time monitoring architecture, including: constructing multiple monomer mathematical models according to the physical properties of the multiple structures; performing sensor deployment analysis based on the multiple monomer mathematical models to obtain multiple sensor deployment strategies; and scheduling and configuring the sensors of the multiple coupled structures with the multiple sensor deployment strategies as constraints to obtain a real-time monitoring architecture.

[0114] Furthermore, a sensor deployment analysis is performed based on the multiple monomer mathematical models to obtain multiple sensor deployment strategies, including: extracting a first structural body physical property of the wind turbine group in the floating offshore wind power device from the device composition information, wherein the wind turbine group is any one of the multiple coupled structures; extracting multiple component structure information and multiple component behavior information of multiple key components based on the first structural body physical property; after constructing multiple component structure models based on the multiple component structure information, assembling the multiple component structure models to obtain a unit structure model; locating key monitoring nodes of the multiple key components based on the unit structure model and the multiple component behavior information to obtain a first sensor deployment strategy; and so on, performing a sensor deployment analysis based on the multiple monomer mathematical models to obtain the multiple sensor deployment strategies.

[0115] Furthermore, the key monitoring nodes of the multiple key components are located according to the unit structure model and the multiple component behavior information to obtain a first sensor deployment strategy, including: fitting the multiple component behavior information to the unit structure model to obtain a unit operation behavior model; extracting the operation status data of the multiple key components based on the unit operation behavior model to obtain multiple groups of operation status indicators; locating the key monitoring nodes of the multiple key components in the unit operation behavior model based on the multiple groups of operation status indicators to obtain multiple groups of sensor deployment nodes; and associatively storing the wind turbine unit, multiple key components and multiple groups of sensor deployment nodes to constitute the first sensor deployment strategy.

[0116] Furthermore, a multi-body coupling model is constructed based on the layout environment information and the device composition information, including: disassembling the device composition information to obtain multiple structural relationships and multiple structural boundary conditions of the multiple coupled structures; extracting multiple external action information of the multiple coupled structures from the layout environment information; after obtaining multiple structural characteristics of the multiple coupled structures through an Internet search, configuring dynamic equations based on the multiple structural characteristics to obtain multiple interaction equations; after integrating the multiple monomer mathematical models into a pre-call framework, coupling the multiple monomer mathematical models according to the multiple structural relationships and multiple interaction equations to obtain an initial coupling model; and fusing the layout environment information as a boundary condition with the initial coupling model to obtain the multi-body coupling model.

[0117] Furthermore, after the multi-body coupling model is dynamically updated according to the real-time wind power information, an associated operation risk analysis is performed based on the multi-body coupling model, and a real-time fault response is output, including: interactively obtaining multiple groups of sample operation data sets under multiple sample fault modes; constructing a fault state identification model based on the multiple groups of sample operation data sets; when the duration of the multi-body coupling model's dynamic operation update according to the real-time wind power information reaches a preset duration constraint, extracting associated operation data from the multi-body coupling model to obtain an associated operation data set; synchronizing the associated operation data set to the fault state identification model for fault mode matching to obtain a real-time fault mode and a real-time fault level; after setting the warning signal level according to the real-time fault level, outputting the real-time fault mode as a real-time fault response.

[0118] Furthermore, a fault state identification model is constructed based on the multiple groups of sample operation data sets, including: calling and obtaining a first group of sample operation data sets corresponding to a first sample fault mode from the multiple groups of sample operation data sets, wherein the first sample fault mode is any one of the multiple sample fault modes; splitting the first group of sample operation data sets based on the fault level to obtain K sample operation data sets; calling a first sample operation data set corresponding to the first fault level from the K sample operation data sets; splitting the first group of sample operation data sets based on the multiple coupling structures to obtain multiple sample operation data combinations; performing fault feature aggregation on the multiple sample operation data combinations to obtain a first group of fault deviation windows of the first sample fault mode at the first fault level; and so on, obtaining K groups of fault deviation windows of the first sample fault mode at K fault levels; associating and storing the first sample fault mode, K fault levels and K groups of fault deviation windows to obtain a first fault mode identification branch; and so on, constructing multiple fault mode identification branches of the multiple sample fault modes; and connecting the multiple fault mode identification branches in parallel to obtain the fault state identification model.

[0119] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0120] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications 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. Floating offshore wind power multi-body coupling real-time monitoring system, characterized in that: The system comprises: An information acquisition module, used for interactively acquiring the layout environment information and device composition information of the floating offshore wind power device; A physical property extraction module, used for extracting and obtaining a plurality of structural physical properties of a plurality of coupled structures in the floating offshore wind power device from the device configuration information; A monitoring architecture acquisition module, used to perform sensor scheduling configuration of the multiple coupled structures according to the physical properties of the multiple structures to obtain a real-time monitoring architecture; A coupling model acquisition module, used for constructing and acquiring a multi-body coupling model based on the deployment environment information and the device configuration information; A real-time information transmission module, used for the real-time monitoring framework to collect data from the floating offshore wind power device and transmit the real-time wind power information obtained to the multi-body coupling model through a distributed sensor network; A fault response output module is used to perform an associated operation risk analysis based on the multi-body coupling model and output a real-time fault response after the multi-body coupling model is dynamically updated according to the real-time wind power information; A fault prevention operation and maintenance module is used to perform fault prevention operation and maintenance on the floating offshore wind power device according to the real-time fault response.

2. The floating offshore wind power multi-body coupling real-time monitoring system according to claim 1, characterized in that: The sensor scheduling configuration of the multiple coupled structures is performed according to the physical properties of the multiple structures to obtain a real-time monitoring architecture, including: Constructing a plurality of monomer mathematical models according to the plurality of structural physical properties; Performing sensor deployment analysis based on the multiple monomer mathematical models to obtain multiple sensor deployment strategies; Taking the multiple sensor deployment strategies as constraints, the sensor scheduling configuration of the multiple coupled structures is performed to obtain a real-time monitoring architecture.

3. The floating offshore wind power multi-body coupling real-time monitoring system according to claim 2, characterized in that: Performing sensor deployment analysis based on the multiple monomer mathematical models to obtain multiple sensor deployment strategies includes: Extracting and obtaining a first structural physical property of a wind turbine group in the floating offshore wind turbine from the device configuration information, wherein the wind turbine group is any one of the multiple coupled structures; Extracting and obtaining a plurality of component structure information and a plurality of component behavior information of a plurality of key components according to the physical properties of the first structure; After constructing a plurality of component structure models according to the plurality of component structure information, assembling the plurality of component structure models to obtain a unit structure model; Positioning key monitoring nodes of the multiple key components according to the unit structure model and the behavior information of the multiple components, and obtaining a first sensor deployment strategy; By analogy, the sensor deployment analysis is performed based on the multiple monomer mathematical models to obtain the multiple sensor deployment strategies.

4. The floating offshore wind power multi-body coupling real-time monitoring system according to claim 3, characterized in that: Positioning key monitoring nodes of the multiple key components according to the unit structure model and the multiple component behavior information to obtain a first sensor deployment strategy includes: Fitting the behavior information of the plurality of components to the unit structure model to obtain a unit operation behavior model; Extracting the operating status data of the plurality of key components based on the unit operating behavior model to obtain a plurality of groups of operating status indicators; Based on the multiple groups of operating status indicators, key monitoring nodes of the multiple key components are located in the unit operating behavior model to obtain multiple groups of sensor deployment nodes; The wind turbine generator set, multiple key components and multiple groups of sensor deployment nodes are stored in association to form the first sensor deployment strategy.

5. The floating offshore wind power multi-body coupling real-time monitoring system according to claim 2, characterized in that: A multi-body coupling model is constructed based on the layout environment information and the device composition information, including: Disassembling the device configuration information to obtain a plurality of structural relationships and a plurality of structural boundary conditions of the plurality of coupled structures; Extracting and obtaining a plurality of external action information of the plurality of coupling structures from the layout environment information; After obtaining a plurality of structural characteristics of the plurality of coupled structures through online search, configuring dynamic equations based on the plurality of structural characteristics to obtain a plurality of interaction equations; After integrating the plurality of monomer mathematical models into the pre-call framework, coupling the plurality of monomer mathematical models according to the plurality of structural body relationships and the plurality of interaction equations to obtain an initial coupling model; The deployment environment information is used as a boundary condition and integrated with the initial coupling model to obtain the multi-body coupling model.

6. The floating offshore wind power multi-body coupling real-time monitoring system according to claim 5, characterized in that: After the multi-body coupling model is dynamically updated according to the real-time wind power information, an associated operation risk analysis is performed based on the multi-body coupling model to output a real-time fault response, including: Interactively obtain multiple sets of sample operation data sets under multiple sample failure modes; Constructing and generating a fault state recognition model based on the plurality of sample operation data sets; When the duration of the dynamic update of the operation of the multi-body coupling model according to the real-time wind power information reaches a preset duration constraint, extracting associated operation data of the multi-body coupling model to obtain an associated operation data set; Synchronizing the associated operation data set to the fault state identification model for fault mode matching to obtain a real-time fault mode and a real-time fault level; After setting the warning signal level according to the real-time fault level, the real-time fault mode is output as a real-time fault response.

7. The floating offshore wind power multi-body coupling real-time monitoring system according to claim 6, characterized in that: Constructing and generating a fault state recognition model based on the plurality of sample operation data sets includes: Calling and obtaining a first set of sample operation data sets corresponding to a first sample failure mode from the multiple sets of sample operation data sets, wherein the first sample failure mode is any one of the multiple sample failure modes; Splitting the first group of sample operation data sets based on the fault level to obtain K sample operation data sets; Calling a first sample operation data set corresponding to a first fault level from the K sample operation data sets; Splitting the first set of sample operation data sets based on the multiple coupling structures to obtain multiple sample operation data combinations; Performing fault feature aggregation on the plurality of sample operation data combinations to obtain a first group of fault deviation windows of the first sample fault mode at a first fault level; By analogy, K groups of fault deviation windows of the first sample fault mode at K fault levels are obtained; The first sample fault mode, K fault levels and K groups of fault deviation windows are stored in association to obtain a first fault mode identification branch; By analogy, multiple failure mode identification branches of the multiple sample failure modes are constructed; The multiple fault mode identification branches are connected in parallel to obtain the fault state identification model.

8. A real-time monitoring method for multi-body coupling of floating offshore wind power, characterized in that: The method is applied to the floating offshore wind power multi-body coupling real-time monitoring system according to any one of claims 1 to 7, and the method comprises: Interactively obtain the layout environment information and device composition information of the floating offshore wind power device; Extracting and obtaining a plurality of structural physical properties of a plurality of coupled structures in the floating offshore wind power device from the device configuration information; Performing sensor scheduling configuration of the plurality of coupled structures according to the physical properties of the plurality of structures to obtain a real-time monitoring architecture; Constructing and obtaining a multi-body coupling model based on the deployment environment information and the device composition information; The real-time monitoring framework collects data from the floating offshore wind power device and transmits the real-time wind power information to the multi-body coupling model through a distributed sensor network; After the multi-body coupling model is dynamically updated according to the real-time wind power information, an associated operation risk analysis is performed based on the multi-body coupling model to output a real-time fault response; and fault prevention operation and maintenance of the floating offshore wind power device is performed according to the real-time fault response.

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