A Method and System for Synchronously Monitoring the Dynamic Response and Static Deformation of a Wind Turbine Tower

Abstract

The present invention provides a method and system for synchronously monitoring the dynamic response and static deformation of a wind turbine tower column, including arranging acceleration sensors, strain sensors and inclination sensors on the wind turbine tower column to respectively collect the dynamic response data and static deformation data of the tower column, and simultaneously collecting environmental data, including wind speed, wind direction and temperature; filtering and denoising the collected dynamic response data to extract effective vibration signals, and calibrating and compensating the collected static deformation data to eliminate the influence of sensor errors and environmental factors. Through the coordinated cooperation of algorithm combinations, the present invention realizes the high-precision synchronous monitoring of the dynamic response and static deformation of the wind turbine tower column, not only improves the comprehensiveness and accuracy of the monitoring, but also can detect structural anomalies earlier, provides a scientific basis for maintenance decisions, and significantly improves the reliability and practicality of the health monitoring of the wind turbine tower column.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine tower monitoring, and particularly to a method and system for synchronously monitoring the dynamic response and static deformation of a wind turbine tower. Background Art

[0002] At present, the health monitoring of wind turbine towers usually adopts a method of separating dynamic response monitoring and static deformation monitoring. Dynamic response monitoring mainly captures the vibration characteristics of the tower through acceleration sensors, while static deformation monitoring measures the deformation state of the tower through strain sensors and tilt sensors. However, this separated monitoring method is difficult to comprehensively reflect the complex mechanical behavior of the tower during actual operation. Especially under the combined action of dynamic wind loads and static gravity loads, there is a significant coupling effect between the dynamic response and static deformation of the tower. The existing technology lacks an effective synchronous monitoring method, resulting in one-sided monitoring results and making it difficult to accurately evaluate the structural health status of the tower. At the same time, in the sensor layout of wind turbine towers, the existing technology usually relies on experience or simple mechanical analysis and lacks a scientific layout method. This blind layout may cause sensors to fail to capture key modes or strain distributions, affecting the accuracy and comprehensiveness of monitoring data.

[0003] Therefore, in view of the above problems, a method and system for synchronously monitoring the dynamic response and static deformation of a wind turbine tower are now developed. Summary of the Invention

[0004] In order to overcome the shortcomings that the existing technology usually adopts a method of separating dynamic response monitoring and static deformation monitoring, lacks an effective synchronous monitoring method, resulting in one-sided monitoring results and making it difficult to accurately evaluate the structural health status of the tower. At the same time, in the sensor layout of wind turbine towers, the existing technology usually relies on experience or simple mechanical analysis and lacks a scientific layout method. This blind layout may cause sensors to fail to capture key modes or strain distributions, affecting the accuracy and comprehensiveness of monitoring data, the present invention provides a method and system for synchronously monitoring the dynamic response and static deformation of a wind turbine tower.

[0005] The technical solution of the present invention is: A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower, including:

[0006] Step 1: Arrange acceleration sensors, strain sensors and tilt sensors on the wind turbine tower to respectively collect the dynamic response data and static deformation data of the tower, and at the same time collect environmental data, including wind speed, wind direction and temperature;

[0007] Step 2: Filter and denoise the collected dynamic response data to extract effective vibration signals, and calibrate and compensate the collected static deformation data to eliminate the influence of sensor errors and environmental factors;

[0008] Step 3: Extract the frequency, amplitude, and damping ratio features from the preprocessed dynamic response data, and extract the strain and inclination features from the preprocessed static deformation data;

[0009] Step 4: Based on the extracted dynamic response features, use the time series analysis algorithm to predict the dynamic response of the tower column. For the extracted static deformation features, use the regression analysis algorithm to predict the static deformation of the tower column. Using the dynamic response prediction results and static deformation prediction results, calculate the interaction relationship between the two through the coupling analysis algorithm. Input the dynamic response features and static deformation features into the multi-task learning model. Through sharing the underlying network and independent upper-layer networks, synchronously optimize the prediction accuracy of the dynamic response and static deformation. Design a joint loss function, combine the dynamic response prediction error and static deformation prediction error, and optimize the model parameters through the gradient descent method to achieve the collaborative monitoring of the dynamic response and static deformation;

[0010] Step 5: According to the interaction analysis results of the dynamic response and static deformation, evaluate the structural health status of the tower column, and issue a warning message when the dynamic response or static deformation exceeds the preset threshold.

[0011] As a preference of the present invention, the dynamic response prediction algorithm adopts a long short-term memory network model. The formula for calculating the dynamic response prediction function is as follows:

[0012] ;

[0013] Where, is the dynamic response prediction function, and the i-th dynamic response feature and environmental parameters need to be input to obtain the dynamic response prediction value. is the weight coefficient of the k-th vibration mode, which is used to quantify the contribution of this mode to the overall dynamic response. represents the non-linear transfer function in the tower column vibration, which is used to describe the non-linear characteristics in the vibration. is the sensitivity coefficient of the k-th neuron to the l-th dynamic response feature, which reflects the coupling relationship between the mode and the feature. is the reference vibration level of the k-th mode, which is used to adjust the initial state of the mode. is the error term of the dynamic response prediction, which represents the random vibration or noise not captured by the model.

[0014] As a preference of the present invention, the static deformation prediction algorithm adopts a support vector regression model. The formula for the static deformation prediction algorithm is as follows:

[0015] ;

[0016] Among them, is a static deformation prediction function, which inputs the j-th static deformation feature during calculation and environmental parameters , and obtains the predicted value of static deformation. is the weight coefficient of the p-th strain mode, indicating the contribution of different strain modes to static deformation, represents the reference center data of the p-th strain mode, indicating the typical position of strain distribution, represents the diffusion parameter of the p-th strain mode, indicating the range of strain distribution, represents the error term of static deformation prediction, indicating the random deformation and noise not captured by the model.

[0017] As a preferred embodiment of the present invention, the dynamic-static coupling analysis algorithm is based on a finite element model to calculate the interaction relationship between dynamic response and static deformation. The formula is as follows: ;

[0018] Among them, is the predicted value of dynamic response, is the predicted value of static deformation, and are the time change rates of dynamic response and static deformation, indicating the instantaneous changes of vibration and deformation, is the Laplace operator, which is used to describe the coupling relationship between dynamic response and static deformation in space, is the coupling strength coefficient, which is used to adjust the interaction strength between dynamic response and static deformation.

[0019] As a preferred embodiment of the present invention, the joint loss function is the weighted sum of dynamic response prediction error and static deformation prediction error. The weight coefficient is dynamically adjusted according to the structural characteristics of the tower column. The formula used is as follows: ;

[0020] Among them, and are the true values of dynamic response and static deformation respectively, representing the actual vibration and deformation data of the tower column, is the regularization coefficient, which is used to control the model complexity and prevent overfitting, is the regularization term, which is used to constrain the model parameters and improve the generalization ability.

[0021] As a preferred embodiment of the present invention, when arranging sensors, the method is as follows:

[0022] Step s1: Establish a finite element model of the tower column, conduct modal analysis, extract the first few modal vibration modes of the tower column, determine the key positions with larger vibration amplitudes according to the modal vibration modes, and select the optimal sensor layout points among the key positions to maximize the observability of the modal vibration modes;

[0023] Step s2: Conduct a static analysis of the tower column, calculate the strain energy distribution under different load conditions, determine the high strain energy regions according to the strain energy distribution, and select representative positions in the high strain energy regions to arrange strain sensors;

[0024] Step s3: Conduct a force analysis of the tower column, calculate the inclination angle changes under different load conditions, determine the positions with significant inclination angle changes according to the inclination angle change sensitivity, and select the optimal layout points for the inclination sensors;

[0025] Step s4: Analyze the influence of environmental data on the dynamic response and static deformation of the tower column, dynamically adjust the sensor layout positions or add temporary monitoring points according to the influence of environmental factors, predict the influence of environmental factors on the sensor layout, and optimize the layout scheme.

[0026] A synchronous monitoring system for the dynamic response and static deformation of a wind turbine tower column, comprising:

[0027] The hardware part, which includes a sensor module, a data acquisition and transmission module, a central processing unit, and a power supply module. The data acquisition and transmission module is used to receive and process sensor data, supports multi-channel synchronous acquisition, and conveys information using a wireless transmission method. The central processing unit is used to run dynamic response prediction, static deformation prediction, coupling analysis, and multi-task learning algorithms;

[0028] The software part, which includes a data preprocessing module, a dynamic response prediction module, a static deformation prediction module, a coupling analysis module, a joint optimization module, and a user interface and visualization module.

[0029] As a preference of the present invention, the sensor module includes:

[0030] Acceleration sensors, which are arranged at the top, middle, and bottom of the tower column and are used to collect the dynamic response data of the tower column;

[0031] Strain sensors, which are arranged in the key stress regions of the tower column and are used to collect the static deformation data of the tower column;

[0032] Inclination sensors, which are arranged at the top and middle of the tower column and are used to collect the inclination angle data of the tower column;

[0033] An environmental sensor, the environmental sensor includes a wind speed sensor, a wind direction sensor and a temperature sensor, and is used to collect environmental data.

[0034] By adopting the above technical solutions, the present invention has the following advantages:

[0035] 1. Through the collaborative cooperation of algorithm combinations, the present invention realizes the high-precision synchronous monitoring of the dynamic response and static deformation of the wind turbine tower column. The combination of the dynamic response prediction algorithm and the static deformation prediction algorithm can comprehensively reflect the structural health state of the tower column, further quantify the interaction relationship between the dynamic response and the static deformation, reveal the complex mechanical behavior of the tower column during actual operation, synchronously optimize the prediction accuracy of the dynamic response and the static deformation, avoid the deviation caused by single-task optimization. The algorithm collaboration not only improves the comprehensiveness and accuracy of monitoring, but also can detect structural anomalies earlier, provides a scientific basis for maintenance decisions, and significantly improves the reliability and practicality of the health monitoring of the wind turbine tower column.

[0036] 2. Through finite element analysis and environmental factor influence analysis, the present invention provides a clear sensor layout method for operators, significantly improving the scientificity and adaptability of sensor installation. This scientific sensor layout method not only improves the accuracy and comprehensiveness of monitoring data, but also can adapt to complex environmental changes, providing strong technical support for the health monitoring of the wind turbine tower column. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the steps of the method for synchronous monitoring of the dynamic response and static deformation of the wind turbine tower column of the present invention.

[0038] Figure 2 It is a flowchart of the steps of the sensor layout of the present invention.

[0039] Figure 3 It is a schematic structural diagram of the system for synchronous monitoring of the dynamic response and static deformation of the wind turbine tower column of the present invention.

[0040] Figure 4 It is a schematic structural diagram of the sensor module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] Referring to an embodiment herein means that the specific features, structures or characteristics described in connection with the embodiment can be included in at least one embodiment of the present invention. The phrase appears in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0042] A synchronous monitoring method for the dynamic response and static deformation of a wind turbine tower column, as Figure 1 shown, includes:

[0043] Step 1: Arrange acceleration sensors, strain sensors and inclination sensors on the wind turbine tower column to collect the dynamic response data and static deformation data of the tower column respectively, and at the same time collect environmental data, including wind speed, wind direction and temperature;

[0044] Step 2: Filter and denoise the collected dynamic response data to extract effective vibration signals, and calibrate and compensate the collected static deformation data to eliminate the influence of sensor errors and environmental factors;

[0045] Step 3: Extract frequency, amplitude and damping ratio characteristics from the preprocessed dynamic response data, and extract strain and inclination characteristics from the preprocessed static deformation data;

[0046] Step 4: Based on the extracted dynamic response characteristics, use the time series analysis algorithm to predict the dynamic response of the tower column, and for the extracted static deformation characteristics, use the regression analysis algorithm to predict the static deformation of the tower column. Use the dynamic response prediction result and the static deformation prediction result, and calculate the interaction relationship between the two through the coupling analysis algorithm. Input the dynamic response characteristics and static deformation characteristics into the multi-task learning model, and synchronously optimize the prediction accuracy of the dynamic response and static deformation through sharing the underlying network and independent upper-layer networks. Design a joint loss function, combine the dynamic response prediction error and the static deformation prediction error, and optimize the model parameters through the gradient descent method to achieve the collaborative monitoring of the dynamic response and static deformation;

[0047] Step 5: Evaluate the structural health status of the tower column according to the interaction analysis results of the dynamic response and static deformation, and issue a warning message when the dynamic response or static deformation exceeds the preset threshold.

[0048] The dynamic response prediction algorithm uses the long short-term memory network model. The formula for calculating the dynamic response prediction function is as follows:

[0049] ;

[0050] Among them, is the dynamic response prediction function, which needs to input the i-th dynamic response characteristic and environmental parameters , and obtain the dynamic response prediction value. is the weight coefficient of the k-th vibration mode, which is used to quantify the contribution of this mode to the overall dynamic response. represents the non-linear transfer function in the tower column vibration, which is used to describe the non-linear characteristics in the vibration. is the sensitivity coefficient of the k-th neuron to the l-th dynamic response feature, reflecting the coupling relationship between the mode and the feature. is the reference vibration level of the k-th mode, used to adjust the initial state of the mode. is the error term of the dynamic response prediction, representing the random vibration or noise not captured by the model.

[0051] The static deformation prediction algorithm uses a support vector regression model. The formula of the static deformation prediction algorithm to be used is as follows:

[0052] ;

[0053] where is the static deformation prediction function, which inputs the j-th static deformation feature and the environmental parameter , and obtains the predicted value of the static deformation. is the weight coefficient of the p-th strain mode. represents the contribution of different strain modes to the static deformation. represents the reference center data of the p-th strain mode, indicating the typical position of the strain distribution. represents the diffusion parameter of the p-th strain mode, indicating the range of the strain distribution. represents the error term of the static deformation prediction, representing the random deformation and noise not captured by the model.

[0054] The dynamic-static coupling analysis algorithm is based on the finite element model to calculate the interaction relationship between the dynamic response and the static deformation. The formula is as follows:

[0055] ;

[0056] where is the predicted value of the dynamic response. is the predicted value of the static deformation. and are the time change rates of the dynamic response and the static deformation, representing the instantaneous changes of vibration and deformation. is the Laplace operator, used to describe the coupling relationship between the dynamic response and the static deformation in space. is the coupling strength coefficient, used to adjust the interaction strength between the dynamic response and the static deformation.

[0057] The combined loss function is the weighted sum of the dynamic response prediction error and the static deformation prediction error. The weight coefficient is dynamically adjusted according to the structural characteristics of the tower column. The formula used is as follows:

[0058] ;

[0059] where and are the true values of dynamic response and static deformation, representing the actual vibration and deformation data of the tower column respectively, is the regularization coefficient, which is used to control the model complexity and prevent overfitting, is the regularization term, which is used to constrain the model parameters and improve the generalization ability.

[0060] It should be noted that the working processes of the four algorithm formulas are as follows:

[0061] Based on the dynamic response characteristics (such as frequency, amplitude, damping ratio) collected by the acceleration sensor and environmental data (such as wind speed, wind direction, temperature), use the time series analysis model to predict the dynamic response of the tower column. Among them, the contributions of different vibration modes to the dynamic response are quantified by the modal weight coefficient and sensitivity coefficient, and the nonlinear characteristics in the tower column vibration are described by the nonlinear transfer function. Then, based on the static deformation characteristics (such as strain, inclination) collected by the strain sensor and inclination sensor and environmental data, use the radial basis function model to predict the static deformation of the tower column. Among them, the influence of different strain distributions on the static deformation is described by the strain modal weight coefficient and reference center, and the range of strain distribution is adjusted by the diffusion parameter. Based on the predicted results of the output dynamic response and the predicted results of the output static deformation, calculate the coupling relationship between the dynamic response and the static deformation. Among them, the instantaneous changes of vibration and deformation are described by the time change rate, and the spatial and temporal interactions between dynamic and static are quantified by the Laplace operator and coupling strength coefficient. Finally, input the extracted dynamic response characteristics and static deformation characteristics into the multi-task learning model, and synchronously optimize the prediction accuracy of the dynamic response and the static deformation through sharing the underlying network and independent upper-layer networks. Among them, the model parameters are constrained by the regularization coefficient and regularization term to prevent overfitting, and the dynamic response prediction error and static deformation prediction error are balanced by the joint loss function to achieve the collaborative monitoring of the dynamic response and the static deformation.

[0062] As Figure 2 shown, when arranging sensors, the method is as follows:

[0063] Step s1: Establish a finite element model of the tower column, conduct modal analysis, extract the first few modal vibration modes of the tower column, and determine the key positions with larger vibration amplitudes according to the modal vibration modes. Select the optimal sensor layout points among the key positions to ensure the maximization of the observability of the modal vibration modes;

[0064] Step s2: Conduct a static analysis of the tower column, calculate the strain energy distribution under different load conditions, determine the high strain energy regions according to the strain energy distribution, and arrange strain sensors at representative positions in the high strain energy regions;

[0065] Step s3: Conduct a force analysis on the tower column, calculate the inclination angle changes under different load conditions, determine the positions with significant inclination angle changes according to the inclination angle change sensitivity, and select the optimal layout points for the inclination sensors;

[0066] Step s4: Analyze the influence of environmental data on the dynamic response and static deformation of the tower column, dynamically adjust the sensor layout positions or add temporary monitoring points according to the influence of environmental factors, predict the influence of environmental factors on the sensor layout, and optimize the layout scheme.

[0067] A synchronous monitoring system for the dynamic response and static deformation of a wind turbine tower column, as Figure 3 shown, includes:

[0068] The hardware part, which includes a sensor module, a data acquisition and transmission module, a central processing unit, and a power supply module. The data acquisition and transmission module is used to receive and process sensor data, supports multi-channel synchronous acquisition, and conveys information using a wireless transmission method. The central processing unit is used to run algorithms for dynamic response prediction, static deformation prediction, coupling analysis, and multi-task learning;

[0069] The software part, which includes a data preprocessing module, a dynamic response prediction module, a static deformation prediction module, a coupling analysis module, a joint optimization module, and a user interface and visualization module.

[0070] As Figure 4 shown, the sensor module includes:

[0071] Acceleration sensors, which are arranged at the top, middle, and bottom of the tower column and are used to collect the dynamic response data of the tower column;

[0072] Strain sensors, which are arranged in the key stress areas of the tower column and are used to collect the static deformation data of the tower column;

[0073] Inclination sensors, which are arranged at the top and middle of the tower column and are used to collect the inclination angle data of the tower column;

[0074] Environmental sensors, which include wind speed sensors, wind direction sensors, and temperature sensors and are used to collect environmental data.

[0075] In summary, through the collaborative cooperation of algorithm combinations, high-precision synchronous monitoring of the dynamic response and static deformation of the wind turbine tower is achieved. The combination of the dynamic response prediction algorithm and the static deformation prediction algorithm can comprehensively reflect the structural health status of the tower, further quantify the interaction relationship between the dynamic response and the static deformation, reveal the complex mechanical behavior of the tower during actual operation, synchronously optimize the prediction accuracy of the dynamic response and the static deformation, avoid the deviation caused by single-task optimization. The algorithm collaboration not only improves the comprehensiveness and accuracy of monitoring, but also can detect structural anomalies earlier, providing a scientific basis for maintenance decisions, significantly enhancing the reliability and practicality of the wind turbine tower health monitoring. At the same time, through finite element analysis and analysis of environmental factor impacts, a clear sensor layout method is provided for operators, significantly enhancing the scientificity and adaptability of sensor installation. This scientific sensor layout method not only improves the accuracy and comprehensiveness of monitoring data, but also can adapt to complex environmental changes, providing strong technical support for the health monitoring of the wind turbine tower.

[0076] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the idea of the present invention. Therefore, the protection scope of the present invention is not limited by the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower, characterized in that: Includes: Step 1: Arrange acceleration sensors, strain sensors, and inclination sensors on the wind turbine tower to collect dynamic response data and static deformation data of the tower, as well as environmental data including wind speed, wind direction, and temperature. Step 2: Filter and denoise the collected dynamic response data to extract effective vibration signals, and calibrate and compensate the collected static deformation data to eliminate the influence of sensor errors and environmental factors; Step 3: Extract frequency, amplitude, and damping ratio features from the preprocessed dynamic response data, and extract strain and inclination features from the preprocessed static deformation data; Step 4: Based on the extracted dynamic response features, a time series analysis algorithm is used to predict the dynamic response of the tower column. Based on the extracted static deformation features, a regression analysis algorithm is used to predict the static deformation of the tower column. The dynamic response prediction results and the static deformation prediction results are used to calculate the interaction relationship between the two through a coupling analysis algorithm. The dynamic response features and the static deformation features are input into the multi-task learning model. By sharing the bottom network and independent upper-layer networks, the prediction accuracy of the dynamic response and static deformation is simultaneously optimized. A joint loss function is designed. The dynamic response prediction error and the static deformation prediction error are combined, and the model parameters are optimized through the gradient descent method to achieve coordinated monitoring of dynamic response and static deformation. Step 5: Based on the interactive analysis results of dynamic response and static deformation, the structural health of the tower column is evaluated, and an early warning message is issued when the dynamic response or static deformation exceeds the preset threshold.

2. A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower according to claim 1, characterized in that: The dynamic response prediction algorithm adopts the long short-term memory network model. The formula required to calculate the dynamic response prediction function is as follows: ;in, For the dynamic response prediction function, the i-th dynamic response feature needs to be input and environmental parameters , get the dynamic response prediction value, is the weight coefficient of the kth vibration mode, which is used to quantify the contribution of this mode to the overall dynamic response. It represents the nonlinear transfer function in the tower column vibration and is used to describe the nonlinear characteristics in the vibration. is the sensitivity coefficient of the kth neuron to the lth dynamic response feature, reflecting the coupling relationship between the mode and the feature. is the reference vibration level of the kth mode, which is used to adjust the initial state of the mode. The error term in the dynamic response prediction represents random vibration or noise not captured by the model.

3. A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower according to claim 1, characterized in that: The static deformation prediction algorithm adopts the support vector regression model. The static deformation prediction algorithm formula required is as follows: ;in, is the static deformation prediction function, and the jth static deformation feature is input during calculation and environmental parameters , the predicted value of static deformation is obtained, which is the weight coefficient of the pth strain mode, represents the contribution of different strain modes to static deformation, represents the reference center data of the pth strain mode, indicating the typical position of the strain distribution, represents the diffusion parameter of the pth strain mode, indicating the range of strain distribution, represents the error term in the static deformation prediction, which accounts for random deformation and noise not captured by the model.

4. A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower according to claim 1, characterized in that: The dynamic-static coupling analysis algorithm is based on the finite element model and calculates the interaction between dynamic response and static deformation. The formula is as follows: ;in, is the dynamic response prediction value, is the static deformation prediction value, and is the time rate of change of dynamic response and static deformation, indicating the instantaneous change of vibration and deformation, is the Laplace operator, which is used to describe the coupling relationship between dynamic response and static deformation in space. is the coupling strength coefficient, which is used to adjust the interaction strength between dynamic response and static deformation.

5. A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower according to claim 1, characterized in that: The joint loss function is the weighted sum of the dynamic response prediction error and the static deformation prediction error. The weight coefficient is dynamically adjusted according to the structural characteristics of the tower column. The formula used is as follows: ;in, and are the true values of dynamic response and static deformation, respectively, representing the actual vibration and deformation data of the tower column, is the regularization coefficient, which is used to control the complexity of the model and prevent overfitting. is a regularization term used to constrain model parameters and improve generalization ability.

6. A method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower according to claim 1, characterized in that: When arranging the sensors, the method is as follows: Step s1: Build a finite element model of the tower column and perform modal analysis to extract the first few modal vibration shapes of the tower column. Based on the modal vibration shapes, identify key locations with large vibration amplitudes and select the optimal sensor layout points in these key locations to ensure maximum observability of the modal vibration shapes. Step s2: Perform a static analysis on the tower column to calculate the strain energy distribution under different load conditions. Based on the strain energy distribution, determine the high strain energy area and select representative locations in the high strain energy area to arrange strain sensors. Step s3: Perform a force analysis on the tower column to calculate the inclination change under different load conditions. Based on the sensitivity of the inclination change, determine the location where the inclination change is significant and select the optimal layout point of the inclination sensor. Step s4: Analyze the impact of environmental data on the dynamic response and static deformation of the tower column. According to the influence of environmental factors, dynamically adjust the sensor layout or add temporary monitoring points, predict the impact of environmental factors on sensor layout, and optimize the layout plan.

7. A system for synchronously monitoring the dynamic response and static deformation of a wind turbine tower, based on a method for synchronously monitoring the dynamic response and static deformation of a wind turbine tower according to any one of claims 1 to 6, characterized in that: Includes: The hardware part includes a sensor module, a data acquisition and transmission module, a central processing unit, and a power supply module. The data acquisition and transmission module is used to receive and process sensor data, supports multi-channel synchronous acquisition, and transmits information using wireless transmission. The central processing unit is used to run dynamic response prediction, static deformation prediction, coupling analysis, and multi-task learning algorithms; The software part includes a data preprocessing module, a dynamic response prediction module, a static deformation prediction module, a coupling analysis module, a joint optimization module and a user interface and visualization module.

8. A wind turbine tower dynamic response and static deformation synchronous monitoring system according to claim 7, characterized in that: The sensor module includes: Acceleration sensors are arranged at the top, middle and bottom of the tower column to collect dynamic response data of the tower column; A strain sensor is arranged in a key stress-bearing area of the tower column and is used to collect static deformation data of the tower column; Inclination sensors are arranged at the top and middle of the tower column and are used to collect inclination angle data of the tower column; Environmental sensors include wind speed sensors, wind direction sensors, and temperature sensors, and are used to collect environmental data.

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