A method for monitoring the state of the yaw system of an offshore wind turbine

Abstract

The present invention relates to the technical field of wind turbines, and in particular to a method for monitoring the state of a yaw system of an offshore wind turbine generator set, comprising the following steps: S1, data acquisition: real-time acquisition of operating data of a yaw system and a basic structure; S2, data preprocessing and feature extraction: preprocessing the collected operating data, and extracting features from the preprocessed operating data; S3, dynamic yaw control based on tidal impact analysis: adjusting the control strategy of the yaw system; S4, dynamic adjustment based on underwater sediment monitoring: automatically adjusting the operating mode of the yaw system; S5, state identification and early warning: identifying the working state of the yaw system, and if an abnormal state of the yaw system is detected, an early warning signal will be triggered, and state information and fault cause analysis will be provided. The present invention ensures that the wind turbine set always maintains the best windward state, and improves the overall power generation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbines, and in particular to a method for monitoring the state of a yaw system of an offshore wind turbine generator set. Background Art

[0002] With the continuous increase in global energy demand and the improvement of environmental protection requirements, offshore wind power generation has gradually become an important part of renewable energy. Due to the complex environment in which offshore wind turbines are located, they are affected by a variety of marine environmental factors, such as tides, waves, wind and the movement of underwater sediments. These external factors not only have a significant impact on the operating stability of wind turbines, but also cause system wear, foundation settlement and equipment failure. Therefore, how to effectively monitor and control the yaw system of wind turbines to improve their stability and life has become a key issue that needs to be urgently solved in the development of offshore wind farms.

[0003] At present, many existing yaw system monitoring methods often rely only on single or limited sensor data, such as wind direction, wind speed, etc. for simple yaw control. However, these methods are often powerless when dealing with complex marine environments and are unable to fully capture the system's response to complex environmental changes such as tidal impact and underwater sediment movement. Existing technologies lack multi-dimensional dynamic monitoring and comprehensive analysis of the external environment and the internal state of the system. They are unable to adjust the yaw strategy in time when the system is impacted by tidal currents or uneven foundation settlement, which may lead to increased mechanical wear of the system, damage to the foundation structure, and untimely fault warnings. In addition, the existing monitoring systems also have lags in state identification and fault warnings, making it difficult to achieve timely preventive maintenance under complex working conditions.

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for monitoring the status of the yaw system of an offshore wind turbine generator set, which can improve the response speed and prediction accuracy of the yaw system in a complex marine environment, extend the equipment life cycle, reduce maintenance costs, and improve overall operating efficiency. Summary of the invention

[0005] The invention provides a method for monitoring the state of a yaw system of an offshore wind turbine generator set.

[0006] A method for monitoring the state of a yaw system of an offshore wind turbine generator set comprises the following steps:

[0007] S1, data collection: a variety of sensors are arranged in the yaw system and its foundation structure of the offshore wind turbine, including vibration sensors, angle sensors, acceleration sensors, temperature sensors and underwater acoustic wave sensors, to collect real-time operating data of the yaw system and foundation structure, including yaw angle, vibration data, temperature changes, magnitude and direction of tidal impact force and movement status of underwater sediments;

[0008] S2, data preprocessing and feature extraction: preprocess the collected operation data, including filtering, denoising and synchronization, and perform feature extraction on the preprocessed operation data to extract operation parameters related to tidal impact force, underwater sediment movement and mechanical state of the yaw system, including yaw angle change rate, vibration spectrum characteristics, tidal impact force spectrum and sediment movement rate;

[0009] S3, dynamic yaw control based on tidal current impact analysis: according to the real-time monitored tidal current direction and intensity, the control strategy of the yaw system is adjusted, including:

[0010] S31, real-time tidal current data collection and analysis: real-time collection of tidal current direction, speed and intensity, and analysis of the impact of the current tidal current on the yaw system and its changing trend;

[0011] S32, dynamic adjustment of yaw control strategy: based on the analysis results of the tidal current, dynamically adjust the control strategy of the yaw system, including real-time optimization of the yaw angle, adjustment of the speed of the yaw drive system and the braking force;

[0012] S4, dynamic adjustment based on underwater sediment monitoring: when it is detected that the movement of sediments leads to uneven settlement of the wind turbine foundation structure, the operating mode of the yaw system will be automatically adjusted;

[0013] S5, status identification and early warning: Combined with the adjusted control strategy and operation mode of the yaw system, the working status of the yaw system is identified. If an abnormal state of the yaw system is detected, an early warning signal will be triggered, and status information and fault cause analysis will be provided.

[0014] Optionally, the data preprocessing and feature extraction in S2 includes:

[0015] S21, data preprocessing: filtering, denoising and synchronizing the collected operation data, specifically including:

[0016] Filtering: Use Butterworth filter to perform low-pass filtering on the collected operation data;

[0017] Denoising: Wavelet denoising method is used to decompose the signal into different scales through wavelet transform, and soft threshold denoising is performed in the wavelet domain;

[0018] Synchronization: Use linear interpolation to synchronize the operating data of different sensors;

[0019] S22, yaw angle change rate calculation: Based on the collected yaw angle, the yaw angle change rate is calculated to reflect the dynamic response of the yaw system, which is expressed as:

[0020] ;

[0021] in, is the yaw angle change rate, For time interval The angle change within is the time interval;

[0022] S23, vibration spectrum feature extraction: performing fast Fourier transform (FFT) on the vibration data after filtering and denoising to obtain the spectrum features of the vibration signal;

[0023] S24, tidal current impact force spectrum analysis: Based on the magnitude and direction of real-time tidal current impact force, short-time Fourier transform (STFT) is used for time-frequency analysis to obtain the tidal current impact force spectrum characteristics;

[0024] S25, sediment movement rate calculation: using the movement state of underwater sediments, the sediment movement rate is calculated through the Doppler effect.

[0025] Optionally, the real-time power flow data collection and analysis in S31 includes:

[0026] S311, real-time tidal current data collection: The direction, speed and intensity of the tidal current are collected in real time by deploying underwater acoustic sensors and current meters around offshore wind turbines. The tidal current intensity is expressed as:

[0027] ;

[0028] in, is the tidal intensity, is the density of seawater, is the tidal action area, is the tidal velocity;

[0029] S312, Tidal current impact analysis: Based on the collected tidal current direction, speed and intensity data, calculate the impact of the tidal current on the yaw system , expressed as:

[0030] ;

[0031] in, is the angle between the tidal current direction and the direction of the yaw system, is the tidal intensity;

[0032] S313, analysis of the changing trend of tidal impact force: the tidal impact force collected at different time points is analyzed by using the Bayesian dynamic linear model (DLM) to identify the changing trend of tidal impact force.

[0033] Optionally, the Bayesian dynamic linear model (DLM) in S313 includes:

[0034] S3131, fusion of state space representation and environmental factors: introduce the environmental factors of the tidal current (such as tidal current speed, direction, temperature, etc.) into the state vector of the model to reflect the impact of the complex environment on the tidal current impact force, expressed as:

[0035] ;

[0036] in, For time The impact of the trend, is the state vector of environmental factors (such as tidal velocity, direction, temperature, etc.), is the model parameter vector, is the observation error or noise;

[0037] S3132, state transfer and adaptive dynamic adjustment: Considering the time correlation of the flow impact force, the parameters are updated according to the historical flow data through the dynamic adjustment mechanism, which is expressed as:

[0038] ;

[0039] in, For time The model parameter vector, is the state transfer matrix, is the state noise;

[0040] S3133, Bayesian update and tidal current impact force prediction: The Bayesian update mechanism is introduced to dynamically correct the model prediction results and predict the impact force in combination with the current tidal current data, which is expressed as:

[0041] ;

[0042] in, For in time Under the observed data, the model parameters The posterior distribution of is the likelihood function of the current flow impulse given the current parameters, is the parameter prior distribution of the previous moment;

[0043] ;

[0044] in, To predict the impact of the trend at the next moment, is the state vector of the environmental factors at the next moment, is the expected value of the parameter vector after Bayesian update, is the expected value of the observation error.

[0045] Optionally, the dynamic adjustment of the yaw control strategy in S32 includes:

[0046] S321, real-time optimization of yaw angle: based on the real-time analysis results of the tidal current direction and intensity, dynamically adjust the yaw angle of the wind turbine generator set, expressed as:

[0047] ;

[0048] in, is the adjusted yaw angle, is the current yaw angle, is the angle adjustment calculated based on real-time power flow data, is the adjustment factor;

[0049] S322, adjusting the rotation speed of the yaw drive system: dynamically adjusting the rotation speed of the yaw drive system according to the changing trend of the tidal current impact force, expressed as:

[0050] ;

[0051] in, is the speed of the yaw drive system after adjustment, is the real-time change of the flow impact force, is the nominal impact force of the system, is the adjustment factor;

[0052] S323, adjusting the braking force: Based on the real-time data of the tidal current impact force, the braking force of the yaw system is adjusted, which is expressed as:

[0053] ;

[0054] in, The braking force of the yaw system after adjustment, is the current braking force, To calculate according to the change of tidal impact force, To control the adjustment factor.

[0055] Optionally, the dynamic adjustment based on underwater sediment monitoring in S4 includes:

[0056] S41, Real-time sediment movement detection and analysis: Real-time monitoring of sediment movement, analysis of the speed and direction of sediment movement, and assessment of its potential impact on the wind turbine foundation structure;

[0057] S42, risk level assessment: Based on sediment movement and settlement data, assess the potential risk of settlement to wind turbines and classify the risk levels;

[0058] S43, switching the operating mode of the yaw system: automatically switching the operating mode of the yaw system according to the assessed risk level, including reducing the yaw action frequency or limiting the change of the yaw angle.

[0059] Optionally, the real-time sediment movement detection and analysis in S41 includes:

[0060] S411, real-time sediment movement detection: using underwater acoustic wave sensors arranged around the wind turbine foundation to monitor the movement of sediments in real time and calculate the movement speed of sediments;

[0061] S412, Sediment Movement Direction Analysis: Determine the direction of sediment movement by triangulation based on signals from multiple underwater acoustic sensors , expressed as:

[0062] ;

[0063] in, and are the position coordinates of the two sensors;

[0064] S413, Potential impact assessment: Combined with the detected sediment movement speed and direction, the potential impact of sediment movement on the wind turbine foundation structure is assessed by calculating the settlement rate of the foundation structure caused by sediment, expressed as:

[0065] ;

[0066] in, is the sedimentation rate, is the sedimentation sensitivity coefficient, is the sediment movement speed, The main stress direction of the foundation structure.

[0067] Optionally, the risk level assessment in S42 includes:

[0068] S421, Risk Assessment Model: Based on the Speed ​​of Sediment Movement and direction , combined with the sedimentation rate of the foundation structure and sedimentation depth , evaluate the potential risk of settlement to wind turbines, expressed as:

[0069] ;

[0070] in, is the overall risk index, and is the weight coefficient, The main stress direction of the foundation structure;

[0071] S422, Risk level classification: based on the calculated risk index , the risk level is divided into low risk, medium risk and high risk, expressed as:

[0072] ;

[0073] in, For time The risk level results are Represent low, medium and high risk levels respectively. , is the preset risk level threshold.

[0074] Optionally, the switching of the operating mode of the yaw system in S43 includes:

[0075] S431, reduce yaw action frequency: When the risk level is from medium to high, the yaw system automatically reduces the yaw action frequency to reduce the additional stress on the basic structure caused by frequent adjustment of the yaw angle, expressed as:

[0076] ;

[0077] in, is the nominal yaw frequency, is the current risk index, is a high risk threshold. is the frequency adjustment factor;

[0078] S432, limit yaw angle change: At high risk levels, the yaw system automatically limits the maximum change of the yaw angle , in order to avoid excessive adjustment of the yaw angle leading to uneven stress in the base structure, expressed as:

[0079] ;

[0080] in, is the nominal yaw angle change, is the angle change limitation coefficient.

[0081] Optionally, the status identification and warning in S5 includes:

[0082] S51, state identification based on the adjusted control strategy and operation mode: combining the adjusted control strategy and operation mode of the yaw system, analyzing the working state of the yaw system in real time, expressed as:

[0083] ;

[0084] in, is the health index of the yaw system, are the operating parameters of the yaw system (such as yaw angle, drive speed, braking force, etc.), is the weight coefficient of each parameter, is the total number of parameters;

[0085] S52, abnormal state detection and warning triggering: If the calculated health index Exceeding the warning threshold , then trigger the early warning signal;

[0086] S53, status information and fault cause analysis: After the warning is triggered, the operating parameters Analyze and determine the source of the fault by comparing the deviation degree of each operating parameter, which is expressed as:

[0087] ;

[0088] in, For time Time parameters The deviation, For time Parameters of the moment The actual operating value of For parameters The nominal or expected value.

[0089] Beneficial effects of the present invention:

[0090] The present invention can comprehensively reflect the mechanical state of the system and changes in the external environment by real-time collection and analysis of multiple sensor data of the yaw system and its basic structure of the offshore wind turbine generator set. It combines multiple data preprocessing and feature extraction technologies such as filtering, denoising, synchronization, and spectrum analysis to accurately obtain key parameters (such as yaw angle change rate, vibration spectrum characteristics, tidal impact force spectrum, and sediment movement rate), providing a reliable basis for the dynamic control and adjustment of the yaw system. This precise data analysis method greatly improves the operating stability of the yaw system in a complex marine environment.

[0091] The present invention can flexibly cope with the impact caused by the tidal direction, intensity and sediment movement through dynamic adjustment based on tidal impact force and underwater sediment monitoring. The yaw system automatically adjusts the operation mode according to real-time data, such as reducing the yaw action frequency and limiting the yaw angle change, which can effectively reduce the excessive operation and mechanical wear of the system caused by external factors and extend the system service life. At the same time, the automated adjustment strategy ensures that the wind turbine always maintains the best windward state, thereby improving the overall power generation efficiency.

[0092] The present invention, by combining the adjusted yaw control strategy and operation mode, monitors key parameters in real time and performs health status assessment. When an abnormal state is detected, the system can automatically trigger a warning signal and provide fault cause and status information through parameter deviation analysis, so that operation and maintenance personnel can take measures in time to effectively prevent potential faults, reduce equipment maintenance costs and downtime risks, and improve the system's operational safety and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0094] Figure 1 A schematic diagram of a monitoring method flow chart of an embodiment of the present invention;

[0095] Figure 2 It is a schematic diagram of dynamic yaw control based on tidal current impact analysis according to an embodiment of the present invention. DETAILED DESCRIPTION

[0096] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0097] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0098] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0099] like Figure 1-Figure 2 As shown, a method for monitoring the state of a yaw system of an offshore wind turbine generator set comprises the following steps:

[0100] S1, data collection: a variety of sensors are arranged in the yaw system and its foundation structure of the offshore wind turbine, including vibration sensors, angle sensors, acceleration sensors, temperature sensors and underwater acoustic wave sensors, to collect real-time operating data of the yaw system and foundation structure, including yaw angle, vibration data, temperature changes, magnitude and direction of tidal impact force and movement status of underwater sediments;

[0101] S2, data preprocessing and feature extraction: preprocess the collected operation data, including filtering, denoising and synchronization, and perform feature extraction on the preprocessed operation data to extract operation parameters related to tidal impact force, underwater sediment movement and mechanical state of the yaw system, including yaw angle change rate, vibration spectrum characteristics, tidal impact force spectrum and sediment movement rate;

[0102] S3, dynamic yaw control based on tidal current impact analysis: according to the real-time monitored tidal current direction and intensity, the control strategy of the yaw system is adjusted to reduce the impact of the tidal current on the yaw system, avoid excessive system operation or component wear caused by the tidal current, and ensure the stability and safety of the system. Specifically, it includes:

[0103] S31, real-time tidal current data collection and analysis: real-time collection of tidal current direction, speed and intensity, and analysis of the impact of the current tidal current on the yaw system and its changing trend;

[0104] S32, Dynamic adjustment of yaw control strategy: Based on the analysis results of the tidal current, dynamically adjust the control strategy of the yaw system, including real-time optimization of the yaw angle, adjustment of the rotation speed and braking force of the yaw drive system, so as to reduce the negative impact of the tidal current on the system and ensure the stability of the yaw action and the operating efficiency of the system;

[0105] S4, dynamic adjustment based on underwater sediment monitoring: when it is detected that the movement of sediments leads to uneven settlement of the wind turbine foundation structure, the operation mode of the yaw system will be automatically adjusted to reduce the yaw action frequency or yaw speed to prevent yaw errors caused by foundation settlement, ensuring the accurate operation of the system in complex seabed environments;

[0106] S5, status identification and early warning: Combined with the adjusted control strategy and operation mode of the yaw system, the working status of the yaw system is identified. If an abnormal state of the yaw system is detected, an early warning signal will be triggered, and status information and fault cause analysis will be provided to help operation and maintenance personnel take preventive measures in time to avoid further damage to the system;

[0107] Through the above content, we can effectively cope with complex factors in the marine environment such as tidal impact and underwater sediment movement, realize accurate monitoring and active maintenance of the yaw system of offshore wind turbines, improve the stability and safety of the system in complex marine environments, prevent potential failures, extend the equipment life cycle, and provide timely fault warnings and response plans.

[0108] Data preprocessing and feature extraction in S2 include:

[0109] S21, data preprocessing: filtering, denoising and synchronizing the collected operation data to eliminate environmental noise and interference and ensure the accuracy of the data, including:

[0110] Filtering: Use Butterworth filter to perform low-pass filtering on the collected operation data to remove high-frequency noise interference, which is expressed as:

[0111] ;

[0112] in, is the transfer function of the filter, is a complex variable in the complex frequency domain, is the cutoff frequency of the filter, is the order of the filter;

[0113] Denoising: The wavelet denoising method is used to decompose the signal into different scales through wavelet transform, and soft threshold denoising is performed in the wavelet domain, which is expressed as:

[0114] ;

[0115] in, is the denoised wavelet coefficient, are the original wavelet coefficients, is the threshold value;

[0116] Synchronization: The linear interpolation method is used to synchronize the operating data of different sensors to ensure the time consistency between the sensor data, which is expressed as:

[0117] ;

[0118] in, is the interpolated data value, is the time of the interpolation point, , is the time of the known data point;

[0119] S22, yaw angle change rate calculation: Based on the collected yaw angle, the yaw angle change rate is calculated to reflect the dynamic response of the yaw system, which is expressed as:

[0120] ;

[0121] in, is the yaw angle change rate, For time interval The angle change within represents the angle change of the yaw system within a given time period. is the time interval, indicating the length of time that the yaw angle change occurs;

[0122] S23, vibration spectrum feature extraction: Perform fast Fourier transform (FFT) on the vibration data after filtering and denoising to obtain the spectrum feature of the vibration signal, which is expressed as:

[0123] ;

[0124] in, is the representation of the signal in the frequency domain, which is expressed in terms of frequency The signal strength under is the time domain signal value, indicating the time point The signal value at is the frequency, which represents the frequency component of the signal, is the number of data points, the total number of sampling points, is an imaginary unit;

[0125] S24, tidal current impact force spectrum analysis: Based on the real-time tidal current impact force magnitude and direction, short-time Fourier transform (STFT) is used for time-frequency analysis to obtain the tidal current impact force spectrum characteristics, which are expressed as:

[0126] ;

[0127] in, It is the signal representation in the time-frequency domain, describing the and frequency The signal strength under is the time domain signal value, indicating the time point The signal value at is a window function that limits the signal in time In the nearby period of time, is the time index, representing the discrete time point of the signal;

[0128] S25, sediment movement rate calculation: Using the movement state of underwater sediments, the sediment movement rate is calculated through the Doppler effect, expressed as:

[0129] ;

[0130] in, is the Doppler shift, is the speed of sound waves in water, is the transmitting frequency, is the angle between the sound wave and the direction of sediment movement;

[0131] Through the above content, the mechanical state of the yaw system and the changes in the external environment can be accurately reflected, providing an important basis for the dynamic control and adjustment of the system, and ensuring the stable operation of offshore wind turbines in complex marine environments.

[0132] Real-time power flow data collection and analysis in S31 includes:

[0133] S311, real-time tidal current data collection: The direction, speed and intensity of the tidal current are collected in real time by using underwater acoustic sensors and current meters arranged around the offshore wind turbines. The direction of the tidal current is captured by the acoustic sensors and calculated by triangulation. The tidal current intensity is expressed as:

[0134] ;

[0135] in, is the tidal intensity, is the density of seawater, is the tidal action area, is the tidal velocity;

[0136] S312, Tidal current impact analysis: Based on the collected tidal current direction, speed and intensity data, calculate the impact of the tidal current on the yaw system , expressed as:

[0137] ;

[0138] in, is the angle between the tidal current direction and the direction of the yaw system, is the tidal intensity;

[0139] S313, trend analysis of tidal impact force: The tidal impact force collected at different time points is analyzed through the Bayesian dynamic linear model (DLM) to identify the trend of tidal impact force;

[0140] Through the above, it is possible to dynamically adapt to the complex marine environment and provide more accurate and timely impact force assessment, which not only improves the system's response speed to tidal changes, but also enhances the reliability of the prediction, ensuring that the yaw system can operate stably and efficiently under various tidal conditions.

[0141] The Bayesian Dynamic Linear Model (DLM) in S313 includes:

[0142] S3131, fusion of state space representation and environmental factors: introduce the environmental factors of the tidal current (such as tidal current speed, direction, temperature, etc.) into the state vector of the model to reflect the impact of the complex environment on the tidal current impact force, expressed as:

[0143] ;

[0144] in, For time The impact of the trend, is the state vector of environmental factors (such as tidal velocity, direction, temperature, etc.), is the model parameter vector, which represents the influence of environmental factors on the tidal current impact force. is the observation error or noise;

[0145] S3132, state transfer and adaptive dynamic adjustment: Considering the time correlation of the tide impact force, the dynamic adjustment mechanism is used to update the parameters according to the historical tide data to adapt to the changes in the tide, which is expressed as:

[0146] ;

[0147] in, For time The model parameter vector, is the state transfer matrix, describing the time and The relationship between the parameters at each moment, is the state noise, which indicates the random changes of parameters;

[0148] S3133, Bayesian update and tidal current impact force prediction: The Bayesian update mechanism is introduced to dynamically correct the model prediction results and predict the impact force in combination with the current tidal current data, which is expressed as:

[0149] ;

[0150] in, For in time Under the observed data, the model parameters The posterior distribution of is the likelihood function of the current flow impulse given the current parameters, is the parameter prior distribution of the previous moment;

[0151] ;

[0152] in, To predict the impact of the trend at the next moment, is the state vector of the environmental factors at the next moment, is the expected value of the parameter vector after Bayesian update, which represents the optimal estimate of the model parameters. is the expected value of the observation error;

[0153] Through the above contents, the adaptability of the model in complex marine environments is enhanced, and the prediction results can be adjusted in real time to cope with sudden and nonlinear tidal changes, thereby ensuring the stability and operating efficiency of the yaw system.

[0154] The dynamic adjustment of the yaw control strategy in S32 includes:

[0155] S321, real-time optimization of yaw angle: based on the real-time analysis results of the tidal current direction and intensity, dynamically adjust the yaw angle of the wind turbine generator set so that the wind rotor always maintains the best windward state, expressed as:

[0156] ;

[0157] in, is the adjusted yaw angle, is the current yaw angle, is the angle adjustment calculated based on real-time power flow data, It is the adjustment coefficient used to smoothly control the angle change;

[0158] S322, adjust the rotation speed of the yaw drive system: according to the changing trend of the tidal current impact force, dynamically adjust the rotation speed of the yaw drive system to ensure that the system can quickly and stably adjust the yaw angle under the tidal current impact, expressed as:

[0159] ;

[0160] in, is the speed of the yaw drive system after adjustment, is the real-time change of the flow impact force, is the nominal impact force of the system, is the adjustment factor;

[0161] S323, adjust the braking force: Combined with the real-time data of the tidal impact force, adjust the braking force of the yaw system to ensure that the system can remain stable when dealing with strong tidal currents, expressed as:

[0162] ;

[0163] in, The braking force of the yaw system after adjustment, is the current braking force, To calculate according to the change of tidal impact force, To control the adjustment factor;

[0164] Through the above contents, wind turbines can flexibly respond to changes in tides and maintain the best windward state, which improves the response speed and stability of the system, ensures continuous and efficient operation in various marine environments, reduces mechanical wear and energy loss caused by tidal impact, extends the service life of the equipment and improves power generation efficiency.

[0165] Dynamic adjustments based on underwater sediment monitoring in S4 include:

[0166] S41, Real-time sediment movement detection and analysis: Real-time monitoring of sediment movement, analysis of the speed and direction of sediment movement, and assessment of its potential impact on the wind turbine foundation structure;

[0167] S42, risk level assessment: Based on sediment movement and settlement data, assess the potential risk of settlement to wind turbines and classify the risk levels;

[0168] S43, switching the operating mode of the yaw system: automatically switching the operating mode of the yaw system according to the assessed risk level, including reducing the yaw action frequency or limiting the change of the yaw angle, so as to reduce the risk of further subsidence and ensure the operating stability of the system during the subsidence;

[0169] Through the above content, it is possible to quickly identify and respond to the risk of uneven settlement caused by sediments, reduce the possibility of further settlement by adjusting the system's operating parameters, ensure the stability and safety of wind turbines in complex seabed environments, thereby extending the equipment's service life and improving operating efficiency.

[0170] Real-time sediment movement detection and analysis in S41 includes:

[0171] S411, real-time sediment movement detection: using underwater acoustic wave sensors arranged around the wind turbine foundation to monitor the movement of sediments in real time and calculate the movement speed of sediments;

[0172] S412, Sediment Movement Direction Analysis: Determine the direction of sediment movement by triangulation based on signals from multiple underwater acoustic sensors , expressed as:

[0173] ;

[0174] in, and are the position coordinates of the two sensors, and the direction of sediment movement is deduced by the time difference of the signals they received;

[0175] S413, Potential impact assessment: Combined with the detected sediment movement speed and direction, the potential impact of sediment movement on the wind turbine foundation structure is assessed by calculating the settlement rate of the foundation structure caused by sediment, expressed as:

[0176] ;

[0177] in, is the sedimentation rate, is the settlement sensitivity coefficient, which reflects the sensitivity of sediment movement to the settlement of the foundation structure. is the sediment movement speed, The main stress direction of the foundation structure;

[0178] Through the above content, it is possible to identify and quantify the risk of uneven settlement caused by sediments in real time, predict possible structural problems in advance, and provide key data support for dynamic adjustment and risk management of the system, thereby ensuring the safe and stable operation of wind turbines in complex seabed environments.

[0179] The risk level assessment in S42 includes:

[0180] S421, Risk Assessment Model: Based on the Speed ​​of Sediment Movement and direction , combined with the sedimentation rate of the foundation structure and sedimentation depth , evaluate the potential risk of settlement to wind turbines, expressed as:

[0181] ;

[0182] in, is the overall risk index, and is the weight coefficient, which is used to adjust the contribution of sediment movement speed and sedimentation depth to the risk index. The main stress direction of the foundation structure;

[0183] S422, Risk level classification: based on the calculated risk index , the risk level is divided into low risk, medium risk and high risk, expressed as:

[0184] ;

[0185] in, For time The risk level results are Represent low, medium and high risk levels respectively. , is the preset risk level threshold;

[0186] Preset risk level threshold , Threshold settings based on sediment movement rate and sedimentation rate include:

[0187] Low risk threshold : The corresponding system is in a relatively safe state, that is, the sediment movement rate is low, and the basic settlement rate and depth are within the controllable range. It is set by monitoring historical data and relevant operating experience and is expressed as:

[0188] ;

[0189] in, is the sediment movement rate under low risk conditions, is the basic sedimentation rate under low risk conditions, is the foundation settlement depth under low risk conditions, is the direction of sediment movement, is the main stress direction of the wind turbine foundation structure, and is the weight coefficient, reflecting the relative contribution of sediment movement and settlement depth to the risk;

[0190] High risk threshold : The corresponding system is in a more dangerous state, the sediment movement rate is faster, the basic settlement rate and depth are larger, and the impact on the system cannot be ignored. It can be set based on historical data under extreme conditions and expressed as:

[0191]

[0192] in, is the sediment movement rate under high-risk conditions, is the basic sedimentation rate under high risk conditions, is the foundation settlement depth under high risk conditions;

[0193] Through the above content, the risk level in different environments can be accurately quantified, which helps the system respond in time and adjust the operating mode of the yaw system to ensure operational safety and stability under different risk conditions and minimize potential structural damage and downtime.

[0194] The operation modes of switching the yaw system in S43 include:

[0195] S431, reduce yaw action frequency: When the risk level is from medium to high, the yaw system automatically reduces the yaw action frequency to reduce the additional stress on the basic structure caused by frequent adjustment of the yaw angle, expressed as:

[0196] ;

[0197] in, is the nominal yaw frequency, is the current risk index, is a high risk threshold. is the frequency adjustment factor;

[0198] S432, limit yaw angle change: At high risk levels, the yaw system automatically limits the maximum change of the yaw angle , in order to avoid excessive adjustment of the yaw angle leading to uneven stress in the base structure, expressed as:

[0199] ;

[0200] in, is the nominal yaw angle change, is the angle change limitation coefficient;

[0201] Through the above, when there is a high risk of sediment movement and foundation settlement, the system can reduce frequent movements and excessive angle adjustments, thereby reducing the stress load on the foundation structure and preventing potential structural damage.

[0202] Status recognition and warning in S5 include:

[0203] S51, state identification based on the adjusted control strategy and operation mode: combining the adjusted control strategy and operation mode of the yaw system, analyzing the working state of the yaw system in real time, expressed as:

[0204] ;

[0205] in, is the health index of the yaw system, are the operating parameters of the yaw system (such as yaw angle, drive speed, braking force, etc.), is the weight coefficient of each parameter, indicating the contribution of different parameters to the system state. is the total number of parameters;

[0206] S52, abnormal state detection and warning triggering: If the calculated health index Exceeding the warning threshold , then trigger the early warning signal;

[0207] The warning threshold The settings include:

[0208] The mean of health index : ;

[0209] in, It is the health index at each time point under normal working conditions. is the total number of data points;

[0210] Standard Deviation of Health Index : ;

[0211] Warning threshold Settings: ;

[0212] in, is the adjustment factor, select ,Right now Principle, which means that when the health index deviates from the normal mean The above triggers an alert;

[0213] S53, status information and fault cause analysis: After the warning is triggered, the operating parameters Analyze and determine the source of the fault by comparing the deviation degree of each operating parameter, which is expressed as:

[0214] ;

[0215] in, For time Time parameters The deviation, For time Parameters of the moment The actual operating value of For parameters The nominal or expected value of

[0216] Through the above content, it is possible to proactively detect and analyze the source of potential faults, accurately locate the problem parameters through deviation calculation, and provide detailed status information and fault causes, which can effectively prevent further damage to the system, reduce maintenance costs and downtime, and improve the stability of the yaw system and overall operating efficiency.

[0217] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0218] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for monitoring the state of a yaw system of an offshore wind turbine generator set, characterized in that: The following steps are involved: S1, data collection: a variety of sensors are arranged in the yaw system and its foundation structure of the offshore wind turbine, including vibration sensors, angle sensors, acceleration sensors, temperature sensors and underwater acoustic wave sensors, to collect real-time operating data of the yaw system and foundation structure, including yaw angle, vibration data, temperature changes, magnitude and direction of tidal impact force and movement status of underwater sediments; S2, data preprocessing and feature extraction: preprocess the collected operation data, including filtering, denoising and synchronization, and perform feature extraction on the preprocessed operation data to extract operation parameters related to tidal impact force, underwater sediment movement and mechanical state of the yaw system, including yaw angle change rate, vibration spectrum characteristics, tidal impact force spectrum and sediment movement rate; S3, dynamic yaw control based on tidal current impact analysis: according to the real-time monitored tidal current direction and intensity, the control strategy of the yaw system is adjusted, including: S31, real-time tidal current data collection and analysis: real-time collection of tidal current direction, speed and intensity, and analysis of the impact force and change trend of the current tidal current on the yaw system; S32, dynamic adjustment of yaw control strategy: based on the analysis results of the tidal current, dynamically adjust the control strategy of the yaw system, including real-time optimization of the yaw angle, adjustment of the speed of the yaw drive system and the braking force; S4, dynamic adjustment based on underwater sediment monitoring: when it is detected that the movement of sediments leads to uneven settlement of the wind turbine foundation structure, the operating mode of the yaw system will be automatically adjusted; S5, status identification and warning: Combined with the adjusted control strategy and operation mode of the yaw system, the working status of the yaw system is identified. If an abnormal state of the yaw system is detected, a warning signal will be triggered, and status information and fault cause analysis will be provided; The real-time flow data collection and analysis in S31 includes: S311, real-time tidal current data collection: The direction, speed and intensity of the tidal current are collected in real time by deploying underwater acoustic sensors and current meters around offshore wind turbines. The tidal current intensity is expressed as: ； in, is the tidal intensity, is the density of seawater, is the tidal action area, is the tidal velocity; S312, Tidal current impact analysis: Based on the collected tidal current direction, speed and intensity data, calculate the impact of the tidal current on the yaw system , expressed as: ； in, is the angle between the tidal current direction and the direction of the yaw system, is the tidal intensity; S313, trend analysis of tidal current impact force: Analyze the tidal current impact force collected at different time points through the Bayesian dynamic linear model to identify the trend of tidal current impact force; The Bayesian dynamic linear model in S313 includes: S3131, fusion of state space representation and environmental factors: the environmental factors of the tidal current are introduced into the state vector of the model to reflect the impact of the complex environment on the tidal current, which is expressed as: ； in, For time The impact of the trend, is the state vector of environmental factors, is the model parameter vector, is the observation error or noise; S3132, state transfer and adaptive dynamic adjustment: Considering the time correlation of the flow impact force, the parameters are updated according to the historical flow data through the dynamic adjustment mechanism, which is expressed as: ； in, For time The model parameter vector, is the state transfer matrix, is the state noise; S3133, Bayesian update and tidal current impact force prediction: The Bayesian update mechanism is introduced to dynamically correct the model prediction results and predict the impact force in combination with the current tidal current data, which is expressed as: ； in, For in time Under the observed data, the model parameters The posterior distribution of is the likelihood function of the current flow impulse given the current parameters, is the parameter prior distribution of the previous moment; ； in, To predict the impact of the trend at the next moment, is the state vector of the environmental factors at the next moment, is the expected value of the parameter vector after Bayesian update, is the expected value of the observation error.

2. A method for monitoring the state of a yaw system of an offshore wind turbine generator set according to claim 1, characterized in that: The data preprocessing and feature extraction in S2 include: S21, data preprocessing: filtering, denoising and synchronizing the collected operation data, specifically including: Filtering: Use Butterworth filter to perform low-pass filtering on the collected operation data; Denoising: Wavelet denoising method is used to decompose the signal into different scales through wavelet transform, and soft threshold denoising is performed in the wavelet domain; Synchronization: Use linear interpolation to synchronize the operating data of different sensors; S22, yaw angle change rate calculation: Based on the collected yaw angle, the yaw angle change rate is calculated to reflect the dynamic response of the yaw system, which is expressed as: ； in, is the yaw angle change rate, For time interval The angle change within is the time interval; S23, vibration spectrum feature extraction: performing fast Fourier transform on the vibration data after filtering and denoising to obtain the spectrum features of the vibration signal; S24, tidal current impact force spectrum analysis: Based on the real-time tidal current impact force magnitude and direction, short-time Fourier transform is used to perform time-frequency analysis to obtain the tidal current impact force spectrum characteristics; S25, sediment movement rate calculation: using the movement state of underwater sediments, the sediment movement rate is calculated through the Doppler effect.

3. A method for monitoring the state of a yaw system of an offshore wind turbine generator set according to claim 1, characterized in that: The dynamic adjustment of the yaw control strategy in S32 includes: S321, real-time optimization of yaw angle: based on the real-time analysis results of the tidal current direction and intensity, dynamically adjust the yaw angle of the wind turbine generator set, expressed as: ； in, is the adjusted yaw angle, is the current yaw angle, is the angle adjustment calculated based on real-time power flow data, is the adjustment factor; S322, adjusting the rotation speed of the yaw drive system: dynamically adjusting the rotation speed of the yaw drive system according to the changing trend of the tidal current impact force, expressed as: ； in, is the speed of the yaw drive system after adjustment, is the real-time change of the flow impact force, is the nominal impact force of the system, is the adjustment factor; S323, adjusting the braking force: Based on the real-time data of the tidal current impact force, the braking force of the yaw system is adjusted, which is expressed as: ； in, The braking force of the yaw system after adjustment, is the current braking force, To calculate according to the change of tidal impact force, To control the adjustment factor.

4. A method for monitoring the state of an offshore wind turbine yaw system according to claim 1, characterized in that: The dynamic adjustment based on underwater sediment monitoring in S4 includes: S41, Real-time sediment movement detection and analysis: Real-time monitoring of sediment movement, analysis of the speed and direction of sediment movement, and assessment of its potential impact on the wind turbine foundation structure; S42, risk level assessment: Based on sediment movement and settlement data, assess the potential risk of settlement to wind turbines and classify the risk levels; S43, switching the operating mode of the yaw system: automatically switching the operating mode of the yaw system according to the assessed risk level, including reducing the yaw action frequency or limiting the change of the yaw angle.

5. A method for monitoring the state of a yaw system of an offshore wind turbine generator set according to claim 4, characterized in that: The real-time sediment movement detection and analysis in S41 includes: S411, real-time sediment movement detection: using underwater acoustic wave sensors arranged around the wind turbine foundation to monitor the movement of sediments in real time and calculate the movement speed of sediments; S412, Sediment Movement Direction Analysis: Determine the direction of sediment movement by triangulation based on signals from multiple underwater acoustic sensors , expressed as: ； in, and are the position coordinates of the two sensors; S413, Potential impact assessment: Combined with the detected sediment movement speed and direction, the potential impact of sediment movement on the wind turbine foundation structure is assessed by calculating the settlement rate of the foundation structure caused by sediment, expressed as: ； in, is the sedimentation rate, is the sedimentation sensitivity coefficient, is the sediment movement speed, The main stress direction of the foundation structure.

6. A method for monitoring the state of an offshore wind turbine yaw system according to claim 5, characterized in that: The risk level assessment in S42 includes: S421, Risk Assessment Model: Based on the Speed ​​of Sediment Movement and direction , combined with the sedimentation rate of the foundation structure and sedimentation depth , evaluate the potential risk of settlement to wind turbines, expressed as: ； in, is the overall risk index, and is the weight coefficient, The main stress direction of the foundation structure; S422, Risk level classification: based on the calculated risk index , the risk level is divided into low risk, medium risk and high risk, expressed as: ； in, For time The risk level results are Represent low, medium and high risk levels respectively. , is the preset risk level threshold.

7. A method for monitoring the state of an offshore wind turbine yaw system according to claim 6, characterized in that: The switching operation mode of the yaw system in S43 includes: S431, reduce yaw action frequency: When the risk level is from medium to high, the yaw system automatically reduces the yaw action frequency to reduce the additional stress on the basic structure caused by frequent adjustment of the yaw angle, expressed as: ； in, is the nominal yaw frequency, is the current risk index, is a high risk threshold. is the frequency adjustment factor; S432, limit yaw angle change: At high risk levels, the yaw system automatically limits the maximum change of the yaw angle , in order to avoid excessive adjustment of the yaw angle leading to uneven stress in the base structure, expressed as: ； in, is the nominal yaw angle change, is the angle change limitation coefficient.

8. The method for monitoring the state of the yaw system of an offshore wind turbine generator set according to claim 1, characterized in that: The state identification and early warning in S5 include: S51, state identification based on the adjusted control strategy and operation mode: combining the adjusted control strategy and operation mode of the yaw system, analyzing the working state of the yaw system in real time, expressed as: ； in, is the health index of the yaw system, are the operating parameters of the yaw system, is the weight coefficient of each parameter, is the total number of parameters; S52, abnormal state detection and warning triggering: If the calculated health index Exceeding the warning threshold , then trigger the early warning signal; S53, status information and fault cause analysis: After the warning is triggered, the operating parameters Analyze and determine the source of the fault by comparing the deviation degree of each operating parameter, which is expressed as: ； in, For time Time parameters The deviation, For time Parameters of the moment The actual operating value of For parameters The nominal or expected value.

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