Safety management method and system for offshore wind turbine generator

Abstract

The invention provides a safety management system and method for an offshore wind turbine generator, and relates to the technical field of offshore wind power. Comprising a meteorological sensing subsystem, a wave analysis subsystem, a dynamic modeling subsystem and a safety regulation and control subsystem, wherein the meteorological sensing subsystem comprises a wind speed monitoring module, an air pressure monitoring module, a temperature and humidity monitoring module and a data fusion module; the wave analysis subsystem comprises a wave height monitoring module, a wave period monitoring module, a wave direction monitoring module and a wave coupling calculation module; the dynamic modeling subsystem comprises a kinematics modeling module, a dynamics modeling module, a wind wheel load calculation module and a platform stability evaluation module; the safety regulation and control subsystem comprises a wind speed sudden change response module, a wave coupling optimization module, a platform posture adjustment module and an emergency shutdown module. According to the scheme, the operation safety of the offshore wind turbine generator is improved.

Description

Technical Field

[0001] The present application relates to the field of offshore wind power technology, and in particular to a safety management method and system for offshore wind turbines. Background Art

[0002] With the acceleration of the global energy structure transformation, offshore wind power, as a clean and renewable energy form, is receiving increasing attention. However, the operating environment of offshore wind farms is complex and changeable, and their power prediction and safety management face many challenges. The frontal system formed by the convergence of cold and warm air masses in the marine environment moves rapidly and can cause drastic changes in wind speed in a short period of time. Due to insufficient temporal and spatial resolution, traditional prediction models are unable to effectively identify such mutations, resulting in a significant decrease in prediction accuracy, which in turn affects the operating efficiency and safety of wind farms. In addition, changes in wave conditions have a significant impact on wind farm characteristics, especially changes in sea surface roughness caused by waves, which directly affect the vertical distribution characteristics of wind speed. When the wave height increases, the wind speed at different heights will show obvious differences, but existing prediction models often ignore the coupling effect between waves and wind fields, further weakening the accuracy of predictions.

[0003] The swaying motion of floating offshore wind turbines, driven by waves, dynamically influences the effective wind speed of the rotors, causing periodic fluctuations in the motor output power. Traditional aerodynamic calculation methods cannot accurately describe this dynamic coupling process, making it difficult to ensure the stability and safety of the platform. Therefore, developing an offshore wind power prediction model that can rapidly respond to sudden changes in offshore wind speed and fully account for wave coupling effects has become a key issue in improving the efficiency and safety of offshore wind power operations. Summary of the Invention

[0004] The present invention discloses a safety management system for offshore wind turbines, aiming to solve the above-mentioned problems.

[0005] The present invention adopts the following scheme:

[0006] A safety management system for offshore wind turbines includes a meteorological sensing subsystem, a wave analysis subsystem, a dynamic modeling subsystem, and a safety control subsystem, wherein:

[0007] The meteorological sensing subsystem includes a wind speed monitoring module, an air pressure monitoring module, a temperature and humidity monitoring module, and a data fusion module, which are responsible for collecting and processing meteorological data of offshore wind farms;

[0008] The wave analysis subsystem includes a wave height monitoring module, a wave period monitoring module, a wave direction monitoring module and a wave coupling calculation module for analyzing the influence of wave conditions on the effective wind speed of the wind rotor;

[0009] The dynamic modeling subsystem includes a kinematic modeling module, a dynamic modeling module, a wind rotor load calculation module, and a platform stability assessment module, which are used to establish a dynamic response model of the floating platform in the marine environment;

[0010] The safety control subsystem includes a wind speed mutation response module, a wave coupling optimization module, a platform attitude adjustment module and an emergency shutdown module, which are used to generate a safe operation plan based on real-time data and dynamic models.

[0011] Furthermore, the wind speed monitoring module collects wind speed data at different heights through a multi-point distributed sensor network and predicts the wind speed change trend in combination with a time series analysis algorithm;

[0012] The air pressure monitoring module uses a high-sensitivity barometer to monitor air pressure fluctuations in real time;

[0013] The temperature and humidity monitoring module uses an integrated sensor unit to obtain air temperature and humidity information, which is used to provide basic parameters for modeling the vertical distribution of wind speed;

[0014] The data fusion module uses the Kalman filter algorithm to fuse multi-source data and output high-precision comprehensive meteorological status information.

[0015] Furthermore, the wave height monitoring module uses a radar wave meter to measure the wave height in real time, and the wave period monitoring module captures the wave period characteristics through an inertial measurement unit;

[0016] The wave direction monitoring module determines the wave propagation direction based on the acoustic Doppler current profiler;

[0017] The wave coupling calculation module uses the improved Morrison equation to calculate the force exerted by waves on the floating platform. The formula is as follows: Where F is the wave force, ρ is the seawater density, and C d and C m They represent the drag coefficient and the additional mass coefficient respectively, A represents the force area, V represents the volume of liquid displaced by the object, and v represents the wave velocity. and Indicates the time rate of change of wave velocity.

[0018] Furthermore, the kinematic modeling module describes the displacement, velocity, and acceleration changes of the floating platform based on the rigid body six-degree-of-freedom motion equation. The dynamic modeling module establishes the dynamic model of the platform through the Lagrange equation. The wind rotor load calculation module calculates the aerodynamic load of the wind rotor under different working conditions using the blade element momentum theory. The formula is as follows: Where T represents the wind wheel thrust, ρ α Indicates the air density, A r Indicates the swept area of the wind wheel, C Trepresents the thrust coefficient, ω represents the angular velocity of the wind rotor, and R represents the radius of the wind rotor. The platform stability assessment module evaluates the stability of the platform by calculating the ratio of the overturning moment to the restoring moment.

[0019] Furthermore, the wind speed sudden change response module quickly adjusts the pitch angle of the wind turbine to cope with the wind speed sudden change based on the adaptive sliding mode control algorithm;

[0020] The wave coupling optimization module optimizes the wave response characteristics of the floating platform through the particle swarm optimization algorithm to reduce the power fluctuation caused by waves;

[0021] The platform attitude adjustment module uses a fuzzy logic controller to adjust the platform attitude in real time to ensure that the wind wheel is always at the optimal windward angle;

[0022] The emergency shutdown module triggers the emergency shutdown procedure when it detects extreme working conditions to protect the safety of the unit equipment.

[0023] Furthermore, the dynamic modeling subsystem establishes a dynamic response model of the floating platform based on meteorological and wave data. The kinematic modeling module uses the rigid body six-degree-of-freedom motion equation to describe the displacement, velocity, and acceleration changes of the platform. Its mathematical expression is: Where M represents the mass matrix of the platform, and x represent acceleration, velocity, and displacement vectors respectively, represents the damping matrix, K represents the stiffness matrix, F ext Represents external forces, including wave forces and wind forces.

[0024] Furthermore, the dynamic modeling module establishes the dynamic model of the platform through the Lagrange equation, and its basic form is: Where L represents the Lagrangian function, which is defined as the difference between the kinetic energy and potential energy of the system; q represents the generalized coordinate, represents generalized velocity; Q represents generalized force.

[0025] The present invention also provides a safety management method for an offshore wind turbine generator set, which uses the safety management system for an offshore wind turbine generator set, including the following steps:

[0026] S1. The meteorological sensing subsystem collects and processes meteorological data from offshore wind farms and outputs comprehensive meteorological status information;

[0027] S2. Based on meteorological information, the wave analysis subsystem analyzes the impact of wave conditions on the effective wind speed of the wind rotor and outputs the wave coupling force.

[0028] S3. The dynamic modeling subsystem establishes a dynamic response model of the floating platform based on meteorological and wave data, and outputs the platform's kinematic and dynamic characteristics.

[0029] S4. The safety control subsystem generates a safe operation plan based on the dynamic response model and activates or shuts down relevant working modules after selecting an appropriate control strategy.

[0030] S5. The operating status information of the safety control subsystem is fed back to the meteorological perception subsystem and the wave analysis subsystem to optimize the subsequent data collection and analysis process.

[0031] Beneficial effects:

[0032] This solution achieves real-time monitoring and dynamic modeling of multi-dimensional meteorological parameters and wave conditions through the synergy of the meteorological sensing subsystem, wave analysis subsystem, dynamic modeling subsystem and safety control subsystem, and generates a safety control solution based on the optimization algorithm to effectively respond to sudden changes in wind speed and wave coupling effects, thereby improving the operational safety and power generation efficiency of offshore wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic structural diagram of a safety management system for an offshore wind turbine according to an embodiment of the present invention; DETAILED DESCRIPTION

[0034] Example 1

[0035] Combine Figure 1 This embodiment provides a safety management method and system for offshore wind turbines provided by the present invention. The core of the method and system is to achieve real-time monitoring and dynamic response optimization of floating platforms in complex marine environments through the coordinated work of a meteorological sensing subsystem, a wave analysis subsystem, a dynamic modeling subsystem and a safety control subsystem.

[0036] Specifically, the safety management system for offshore wind turbines includes a meteorological perception subsystem, a wave analysis subsystem, a dynamic modeling subsystem and a safety control subsystem. The meteorological perception subsystem includes a wind speed monitoring module, an air pressure monitoring module, a temperature and humidity monitoring module and a data fusion module, which is responsible for collecting and processing meteorological data of offshore wind farms. The wave analysis subsystem includes a wave height monitoring module, a wave period monitoring module, a wave direction monitoring module and a wave coupling calculation module to analyze the impact of wave conditions on the effective wind speed of the wind rotor. The dynamic modeling subsystem includes a kinematic modeling module, a dynamic modeling module, a wind rotor load calculation module and a platform stability assessment module to establish a dynamic response model of the floating platform in the marine environment. The safety control subsystem includes a wind speed mutation response module, a wave coupling optimization module, a platform attitude adjustment module and an emergency shutdown module to generate a safe operation plan based on real-time data and dynamic models.

[0037] The wind speed monitoring module acquires wind speed data at different spatial locations through a distributed sensor network. This can be implemented using a Doppler lidar array and used to construct a three-dimensional wind speed field. The air pressure monitoring module monitors atmospheric pressure changes in real time. This can be implemented using a micro-electromechanical system air pressure sensor to capture the movement characteristics of the frontal system. The temperature and humidity monitoring module measures air temperature and humidity. This can be implemented using a capacitive humidity sensor combined with a platinum resistance temperature sensor to provide boundary conditions for modeling the vertical distribution of wind speed. The data fusion module integrates heterogeneous data from multiple sources. This can be implemented using a Kalman filter algorithm to eliminate sensor measurement errors and generate comprehensive meteorological parameters.

[0038] The wave height monitoring module refers to a device that measures the height of waves. Specifically, it can be implemented using an X-band radar and is used to obtain the spatial distribution characteristics of waves. The wave period monitoring module refers to a device that analyzes the time series characteristics of waves. Specifically, it can be implemented using an accelerometer array and is used to extract the wave spectral characteristics. The wave direction monitoring module refers to a device that determines the direction of wave propagation. Specifically, it can be implemented using an acoustic Doppler current profiler and is used to analyze the relative direction of waves and platform motion. The wave coupling calculation module refers to a processing unit that calculates the interaction between waves and structures. Specifically, it can be implemented using the improved Morrison equation and is used to quantify the force exerted by waves on floating platforms.

[0039] The kinematic modeling module refers to the calculation unit that describes the spatial motion of the platform. It can be implemented by the rigid body six-degree-of-freedom equation and is used to characterize the displacement and posture changes of the platform. The dynamic modeling module refers to the calculation unit that analyzes the relationship between the force and motion of the platform. It can be implemented by the Lagrange equation and is used to establish a correlation model between the platform motion and load. The wind wheel load calculation module refers to the algorithm module that estimates the aerodynamic load. It can be implemented by the blade element momentum theory and is used to predict the force state of the wind wheel under different working conditions. The platform stability assessment module refers to the analysis unit that determines the safety of the platform. It can be implemented by calculating the ratio of the restoring moment to the overturning moment and is used to evaluate the platform's anti-overturning ability.

[0040] The wind speed sudden change response module is a control unit that responds to rapid changes in wind speed. Specifically, it can be implemented using an adaptive sliding mode controller to adjust the pitch angle to maintain power stability. The wave coupling optimization module is an algorithm unit that improves the platform's wave response. Specifically, it can be implemented using a particle swarm optimization algorithm to reduce power fluctuations caused by waves.

[0041] The platform attitude adjustment module is the execution unit that controls the platform's spatial attitude. It can be implemented using a hydraulic servo system coupled with a fuzzy logic controller to maintain the optimal wind rotor angle. The emergency shutdown module is the control unit that triggers the protection mechanism. It can be implemented using multi-level threshold judgment logic to activate safety protection procedures under extreme operating conditions.

[0042] During operation, the meteorological perception subsystem synchronously collects multi-dimensional meteorological parameters such as wind speed, air pressure, temperature, and humidity through a multi-point distributed sensor network. The data fusion module performs spatiotemporal registration and error correction on heterogeneous data to generate high-precision three-dimensional meteorological field data. The wave analysis subsystem utilizes a radar wave meter, an accelerometer array, and an acoustic current meter to obtain wave height, wave period, and wave direction parameters, respectively. The wave coupling calculation module calculates the dynamic forces exerted by waves on the platform structure based on improved fluid dynamics equations. The dynamic modeling subsystem describes the platform's six-degree-of-freedom motion using kinematic equations and analyzes the interaction between environmental loads and structural responses using dynamic models. The platform stability assessment module calculates the equilibrium state between the structural restoring moment and the overturning moment in real time. Based on the dynamic model's predictions, the safety control subsystem generates platform attitude adjustment commands and power control strategies through an optimization algorithm. When environmental parameters exceeding safety thresholds are detected, the emergency shutdown module activates a multi-level protection mechanism. Each subsystem forms a closed-loop control system through real-time data interaction. Continuous updates of meteorological and wave data drive iterative optimization of the dynamic model. The execution results of safety control commands are fed back to the perception and analysis module, forming an adaptive safety management system.

[0043] Compared with existing technologies, this solution achieves spatial reconstruction of the meteorological field through multi-sensor fusion technology; quantifies the impact of waves on effective wind speed through improved fluid mechanics equations; realizes dynamic response prediction through joint kinematic and dynamic modeling; generates adaptive control schemes through optimization algorithms, and continuously optimizes control parameters in combination with real-time feedback mechanisms, thereby achieving synchronous perception and precise analysis of multi-dimensional environmental parameters of offshore wind farms, establishing a dynamic correlation model between environmental loads and structural responses, and developing a multi-level safety control strategy with adaptive capabilities; effectively solves the shortcomings of traditional methods in meteorological data integrity, wave coupling effect analysis, dynamic response modeling, etc., and improves the operational safety and control reliability of floating platforms in complex marine environments.

[0044] In one embodiment, the wind speed monitoring module, through a vertically arranged sensor network, is capable of synchronously collecting wind speed data at different heights above the sea surface and establishing a vertical wind shear model by comparing wind speed differences at each layer. After collecting wind speed data at different heights, the wind speed monitoring module uses a time series analysis algorithm within the control system to predict wind speed trends. The time series analysis algorithm refers to a statistical prediction method based on historical wind speed data, specifically an autoregressive integral moving average model, which is used to identify periodic patterns in wind speed changes. For example, the time series analysis algorithm performs trend decomposition on continuously collected wind speed data to extract a prediction curve that includes seasonal and random components.

[0045] A high-sensitivity barometer refers to an air pressure sensor with micropascal measurement accuracy, such as a pressure sensor based on MEMS technology. It records air pressure fluctuations in real time, thereby assisting in determining the movement speed of a frontal system and the potential extreme weather impacts it may bring. Specifically, monitored wind speed data can be combined with the meteorological front movement speed calculation formula to assist in determining the movement speed of a frontal system. For example, the meteorological front movement speed y = n*sin(θ), where n represents wind speed and θ is the angle between wind direction and the direction of the front. The Kalman filter algorithm refers to a data fusion method based on a state-space model. Specifically, it can be implemented using an extended Kalman filter to eliminate sensor noise and improve data confidence. The Kalman filter algorithm constructs wind speed, air pressure, temperature and humidity data into a state vector, and dynamically fuses multi-source data through recursive calculation to generate a time-continuous meteorological state estimate.

[0046] This solution achieves three-dimensional spatial data acquisition through a distributed sensor network; uses a high-sensitivity barometer to achieve second-level data updates; ensures the spatiotemporal consistency of parameter measurements through an integrated sensor unit; and achieves dynamic noise suppression and state optimal estimation through a Kalman filter algorithm. It can accurately identify wind speed differences at different altitudes, predict the timing of sudden wind speed changes, effectively determine the movement trajectory and speed of the frontal system, establish a wind speed vertical distribution model that takes atmospheric stability into account, and eliminate sensor errors through dynamic data fusion, providing reliable meteorological input parameters for subsequent wave coupling analysis and platform stability assessment.

[0047] In this embodiment, the wave height monitoring module uses a radar wave meter to measure wave height in real time. The wave period monitoring module uses an inertial measurement unit to capture wave period characteristics. The wave direction monitoring module determines the wave propagation direction based on an acoustic Doppler current profiler. The wave coupling calculation module uses the improved Morrison equation to calculate the force exerted by waves on the floating platform. The formula is as follows:

[0048] Where F is the wave force, ρ is the seawater density, and C d and Cm They represent the drag coefficient and the additional mass coefficient respectively, A represents the force area, V represents the volume of liquid displaced by the object, and v represents the wave velocity. and Indicates the time rate of change of wave velocity.

[0049] Among them, a radar wave meter refers to a device that measures sea level by emitting microwaves and receiving reflected signals. Specifically, it can be implemented using a frequency-modulated continuous wave radar. By analyzing the echo time delay and frequency changes to invert wave height data, it can overcome the defect that optical measurements are affected by weather. An inertial measurement unit refers to a sensor system composed of accelerometers and gyroscopes. Specifically, it can be implemented using a micro-electromechanical system sensor. Through high-frequency sampling, it obtains platform motion acceleration and angular velocity data for extracting wave period characteristics. An acoustic Doppler current profiler refers to a device that uses the Doppler effect of acoustic waves to measure the distribution of water flow velocity. Specifically, it can be implemented using a multi-band acoustic transducer array. The direction of wave propagation is determined by analyzing the frequency shift differences of sound waves in different flow directions. The improved Morrison equation can determine the coefficient values through a combination of experimental calibration and numerical simulation, making the model adaptable to floating platforms of different geometric shapes. During operation, the radar wave meter continuously obtains sea surface height change data through microwave reflection signals, and combines it with the platform acceleration data collected by the inertial measurement unit to separate the wave period characteristics; the acoustic Doppler current profiler analyzes the velocity distribution of water bodies at different depths through multi-band sound wave transmission and reception, and then determines the three-dimensional spatial characteristics of the wave propagation direction; the improved Morrison equation dynamically adjusts the drag coefficient and the additional mass coefficient to dynamically link the force area parameter with the projected area of the platform's wave-receiving surface, and at the same time combines the volume parameter of the liquid displaced by the object to reflect the buoyancy change, thereby establishing a quantitative relationship model between the wave velocity and its time rate of change and the platform force. This solution achieves the simultaneous acquisition of all parameters including wave height, period and propagation direction through combined measurements using a radar wave meter, an inertial measurement unit and an acoustic Doppler current profiler. The improved Morrison equation introduces a dynamic correction coefficient, enabling the hydrodynamic calculation model to adapt to different platform structures, solving the problem of insufficient adaptability of traditional models due to fixed coefficients. High-precision real-time measurement of wave state parameters is achieved, solving the problem of incomplete parameter acquisition in traditional monitoring technologies. The dynamically corrected hydrodynamic calculation model improves the accuracy of wave force calculations, provides reliable input data for floating platform stability analysis, and effectively reduces the error in the calculation of the effective wind speed of the wind rotor caused by the wave coupling effect.

[0050] In this embodiment, the kinematic modeling module describes the displacement, velocity and acceleration changes of the floating platform based on the rigid body six-degree-of-freedom motion equation, and the dynamic modeling module establishes the dynamic model of the platform through the Lagrange equation; the wind wheel load calculation module uses the blade element momentum theory to calculate the aerodynamic load of the wind wheel under different working conditions, and the platform stability evaluation module evaluates its stability by calculating the ratio of the platform's overturning moment to the restoring moment.

[0051] The kinematic modeling module uses the rigid body six-degree-of-freedom motion equation to describe the displacement, velocity, and acceleration changes of the platform. Its mathematical expression is:

[0052] Where M represents the mass matrix of the platform, and x represent acceleration, velocity, and displacement vectors respectively, represents the damping matrix, K represents the stiffness matrix, F ext Represents external forces, including wave forces and wind forces; used to accurately describe the spatial motion trajectory of a floating platform under the action of waves.

[0053] The dynamic modeling module establishes the platform's dynamic model through the Lagrange equation; its basic form is:

[0054] Where L represents the Lagrangian function, which is defined as the difference between the kinetic energy and potential energy of the system; q represents the generalized coordinate, represents the generalized velocity; Q represents the generalized force; this equation is used to analyze the dynamic response characteristics of the platform under complex environmental loads.

[0055] The wind rotor load calculation module uses blade element momentum theory to calculate the aerodynamic load of the wind rotor under different working conditions. The formula is as follows: Where T represents the wind wheel thrust, ρ α Indicates the air density, A r Indicates the swept area of the wind wheel, C T The equation (ω) represents the thrust coefficient, ω represents the rotor angular velocity, and R represents the rotor radius. This equation is used to analyze the platform's dynamic response characteristics under combined environmental loads. The aerodynamic load calculation method, which divides the rotor blades into multiple blade elements, is used to reflect the differences in load distribution at different radial positions during rotor rotation.

[0056] The ratio of overturning moment to restoring moment refers to the dynamic balance indicator between the external moment acting on the platform and its own restoring moment. The stability of the platform is assessed by calculating the ratio of overturning moment to restoring moment. When the ratio is less than a certain threshold (for example, 0.8), the platform is considered to be in a stable state. Otherwise, adjustment measures need to be taken. This can be used to quantify the platform's anti-capsulation ability in complex sea conditions.

[0057] This embodiment uses the kinematic modeling module to establish a six-degree-of-freedom motion equation group including sway, surge, heave, roll, pitch, and bow, and solves the instantaneous displacement and velocity change of the platform under the action of waves in real time, providing basic motion parameters for dynamic analysis. The dynamic modeling module constructs a dynamic model including wave force, wind force and inertial force based on the Lagrange equation, and obtains the dynamic response data of the platform acceleration and attitude angle by solving the differential equation under generalized coordinates. The wind rotor load calculation module divides the wind rotor blades into several blade element units, combines the relative wind speed and attack angle parameters at each blade element, and uses the momentum conservation principle to iteratively calculate the lift and drag distribution of each blade element, and finally obtains the overall wind rotor thrust through integral calculation. The platform stability assessment module monitors the relative positions of the platform's center of gravity and center of buoyancy in real time, and establishes dynamic stability criteria by calculating the instantaneous ratio of the overturning moment generated by wave force to the restoring moment generated by buoyancy. It also implements coupled modeling of the platform's kinematics and dynamics, and uses blade element momentum theory to calculate loads segment by segment, improving the spatiotemporal resolution of aerodynamic loads. Real-time monitoring of the dynamic moment ratio enhances the timeliness of the stability assessment.

[0058] Through the above technical solution, the present application can accurately capture the six-dimensional motion characteristics of the floating platform under the action of waves, accurately calculate the aerodynamic load distribution of different blade positions during the rotation of the wind rotor, and evaluate the platform's anti-overturning ability under dynamic sea conditions in real time, thereby effectively solving the load calculation deviation and stability misjudgment problems caused by inaccurate dynamic response modeling in traditional methods.

[0059] In this embodiment, the wind speed mutation response module quickly adjusts the pitch angle of the wind turbine to cope with the sudden change in wind speed based on the adaptive sliding mode control algorithm; the wave coupling optimization module optimizes the wave response characteristics of the floating platform through the particle swarm optimization algorithm to reduce the power fluctuation caused by waves; the platform attitude adjustment module uses a fuzzy logic controller to adjust the platform attitude in real time to ensure that the wind rotor is always at the optimal windward angle; the emergency shutdown module triggers the emergency shutdown procedure when extreme working conditions are detected to protect the safety of the unit equipment.

[0060] The wind speed sudden change response module quickly adjusts the pitch angle of the wind turbine to cope with the sudden change in wind speed based on the adaptive sliding mode control algorithm. The core idea of the adaptive sliding mode control algorithm is to make the system state converge to the expected value within a finite time by designing the sliding surface and switching function. The specific expression is:

[0061] u=u eq +u sw , where u represents the control input, u eq Represents the equivalent control term, which is used to compensate for the internal dynamic characteristics of the system; u sw Represents a switching control item used to suppress external interference.

[0062] The particle swarm optimization algorithm refers to a global optimization method based on swarm intelligence. For example, it can be implemented using an existing iterative mechanism that dynamically adjusts the inertia weight and learning factor. Its basic steps include initializing the position and speed of the particle swarm, calculating the fitness function value, updating the individual optimal position and the global optimal position, and adjusting the particle speed and position to reduce power fluctuations caused by waves. The fuzzy logic controller can be implemented by combining the existing membership function with the defuzzification algorithm. Its steps include defining input variables, output variables and their membership functions, building a fuzzy rule base, and converting the fuzzy output into precise control instructions through defuzzification methods. The emergency shutdown module triggers the emergency shutdown procedure when an extreme operating condition is detected to protect the safety of the unit equipment. The basis for judging extreme operating conditions includes but is not limited to wind speed exceeding the set threshold, excessive wave force, or platform overturning moment exceeding the safety range.

[0063] When a step change occurs in the wind speed monitoring data, the adaptive sliding mode control algorithm generates control instructions by calculating the pitch angle deviation in real time, driving the variable pitch system to adjust the blade angle of attack to keep the wind rotor speed within a safe range. The wave coupling optimization module establishes a mapping relationship between the platform motion parameters and power fluctuations, and uses the particle swarm algorithm to iteratively calculate the optimal counterweight distribution scheme to reduce the power fluctuations caused by waves. The platform attitude adjustment module outputs ballast water distribution instructions by solving the fuzzy relationship between the yaw angle deviation and the roll angular velocity, so that the wind rotor axis maintains a preset angle with the incoming wind direction. When the platform inclination exceeds the safety threshold, the emergency shutdown module cuts off the gearbox transmission chain and activates the mechanical brake device to avoid equipment damage caused by structural overload.

[0064] This solution achieves dynamic response and multi-objective collaborative optimization under complex working conditions through the combination of adaptive control and intelligent optimization algorithm; through this embodiment, the present application can complete pitch angle adjustment within 10 seconds to suppress the torque impact caused by sudden change in wind speed, reduce the power fluctuation amplitude caused by wave coupling effect to within 5% of the rated power, control the wind rotor windward angle deviation within the range of ±3 degrees, and trigger shutdown protection when the platform inclination angle exceeds 8 degrees, effectively avoiding damage to the equipment structure.

[0065] Example 2

[0066] This embodiment provides a safety management method for an offshore wind turbine, comprising the following steps:

[0067] S1. The meteorological sensing subsystem collects and processes meteorological data from offshore wind farms and outputs comprehensive meteorological status information;

[0068] S2. Based on meteorological information, the wave analysis subsystem analyzes the impact of wave conditions on the effective wind speed of the wind rotor and outputs the wave coupling force.

[0069] S3. The dynamic modeling subsystem establishes a dynamic response model of the floating platform based on meteorological and wave data, and outputs the platform's kinematic and dynamic characteristics.

[0070] S4. The safety control subsystem generates a safe operation plan based on the dynamic response model and activates or shuts down relevant working modules after selecting an appropriate control strategy.

[0071] S5. The operating status information of the safety control subsystem is fed back to the meteorological perception subsystem and the wave analysis subsystem to optimize the subsequent data collection and analysis process.

[0072] This embodiment achieves coupled analysis of environmental parameters and platform responses through multi-source data fusion and dynamic joint modeling. A hierarchical control strategy and real-time feedback optimization form a closed-loop control system that adapts to changes in the ocean environment. This system accurately identifies sudden changes in wind speed caused by frontal systems and analyzes and corrects the vertical wind speed distribution model through wave coupling, reducing power prediction errors. The dynamic response model calculates in real time the impact of the floating platform's rocking motion on the effective wind speed of the wind rotor, providing precise mechanical parameters for safe control. The closed-loop feedback mechanism enables the system to dynamically optimize the data acquisition and analysis process based on the actual control results, improving its adaptability to complex ocean environments.

[0073] It should be understood that the above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention.

[0074] The above description of the drawings used in the implementation manner only shows certain embodiments of the present invention and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without making any creative efforts.

Claims

1. A safety management system for offshore wind turbines, characterized in that: It includes meteorological perception subsystem, wave analysis subsystem, dynamic modeling subsystem and safety control subsystem, among which: The meteorological sensing subsystem includes a wind speed monitoring module, an air pressure monitoring module, a temperature and humidity monitoring module, and a data fusion module, which are responsible for collecting and processing meteorological data of offshore wind farms; The wave analysis subsystem includes a wave height monitoring module, a wave period monitoring module, a wave direction monitoring module and a wave coupling calculation module for analyzing the influence of wave conditions on the effective wind speed of the wind rotor; The dynamic modeling subsystem includes a kinematic modeling module, a dynamic modeling module, a wind rotor load calculation module, and a platform stability assessment module, which are used to establish a dynamic response model of the floating platform in the marine environment; The safety control subsystem includes a wind speed mutation response module, a wave coupling optimization module, a platform attitude adjustment module and an emergency shutdown module, which are used to generate a safe operation plan based on real-time data and dynamic models.

2. The safety management system for offshore wind turbines according to claim 1, characterized in that: The wind speed monitoring module collects wind speed data at different heights through a multi-point distributed sensor network and predicts wind speed change trends in combination with a time series analysis algorithm; The air pressure monitoring module uses a high-sensitivity barometer to monitor air pressure fluctuations in real time; The temperature and humidity monitoring module uses an integrated sensor unit to obtain air temperature and humidity information, which is used to provide basic parameters for modeling the vertical distribution of wind speed; The data fusion module uses the Kalman filter algorithm to fuse multi-source data and output high-precision comprehensive meteorological status information.

3. The safety management system for offshore wind turbines according to claim 1, characterized in that: The wave height monitoring module uses a radar wave meter to measure the wave height in real time, and the wave period monitoring module uses an inertial measurement unit to capture the wave period characteristics; The wave direction monitoring module determines the wave propagation direction based on the acoustic Doppler current profiler; The wave coupling calculation module uses the improved Morrison equation to calculate the force exerted by waves on the floating platform. The formula is as follows: Where F is the wave force, ρ is the seawater density, and C d and C m They represent the drag coefficient and the additional mass coefficient respectively, A represents the force area, V represents the volume of liquid displaced by the object, and v represents the wave velocity. and Indicates the time rate of change of wave velocity.

4. The safety management system for offshore wind turbines according to claim 1, characterized in that: The kinematic modeling module describes the displacement, velocity, and acceleration changes of the floating platform based on the rigid body six-degree-of-freedom motion equation. The dynamic modeling module establishes the platform's dynamic model using the Lagrange equation. The wind rotor load calculation module uses the blade element momentum theory to calculate the aerodynamic load of the wind rotor under different working conditions. The formula is as follows: Where T represents the wind wheel thrust, ρ α Indicates the air density, A r Indicates the swept area of the wind wheel, C T represents the thrust coefficient, ω represents the angular velocity of the wind wheel, and R represents the radius of the wind wheel; The platform stability assessment module evaluates the stability of the platform by calculating the ratio of the overturning moment to the restoring moment.

5. The safety management system for offshore wind turbines according to claim 1, characterized in that: The wind speed mutation response module quickly adjusts the pitch angle of the wind turbine to cope with the wind speed mutation based on the adaptive sliding mode control algorithm; The wave coupling optimization module optimizes the wave response characteristics of the floating platform through the particle swarm optimization algorithm to reduce the power fluctuation caused by waves; The platform attitude adjustment module uses a fuzzy logic controller to adjust the platform attitude in real time to ensure that the wind wheel is always at the optimal windward angle; The emergency shutdown module triggers the emergency shutdown procedure when it detects extreme working conditions to protect the safety of the unit equipment.

6. The safety management system for offshore wind turbines according to claim 1, characterized in that: The dynamic modeling subsystem establishes a dynamic response model of the floating platform based on meteorological and wave data. The kinematic modeling module uses the rigid body six-degree-of-freedom motion equation to describe the displacement, velocity, and acceleration changes of the platform. Its mathematical expression is: Where M represents the mass matrix of the platform, and x represent acceleration, velocity, and displacement vectors respectively, represents the damping matrix, K represents the stiffness matrix, F ext Represents external forces, including wave forces and wind forces.

7. The safety management system for offshore wind turbines according to claim 1, characterized in that: The dynamic modeling module establishes the platform's dynamic model through the Lagrange equation, and its basic form is: Where L represents the Lagrangian function, which is defined as the difference between the kinetic energy and potential energy of the system; q represents the generalized coordinate, represents generalized velocity; Q represents generalized force.

8. A safety management method for an offshore wind turbine, characterized in that: The safety management system for an offshore wind turbine according to any one of claims 1 to 7 comprises the following steps: S1. The meteorological sensing subsystem collects and processes meteorological data from offshore wind farms and outputs comprehensive meteorological status information; S2. Based on meteorological information, the wave analysis subsystem analyzes the impact of wave conditions on the effective wind speed of the wind rotor and outputs the wave coupling force. S3. The dynamic modeling subsystem establishes a dynamic response model of the floating platform based on meteorological and wave data, and outputs the platform's kinematic and dynamic characteristics. S4. The safety control subsystem generates a safe operation plan based on the dynamic response model and activates or shuts down relevant working modules after selecting an appropriate control strategy. S5. The operating status information of the safety control subsystem is fed back to the meteorological perception subsystem and the wave analysis subsystem to optimize the subsequent data collection and analysis process.

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