Offshore wind turbine generator foundation scouring dynamic monitoring and power limit evaluation method

Abstract

The invention discloses an offshore wind turbine generator foundation scouring dynamic monitoring and power limit evaluation method, and aims to solve the problems that in the prior art, foundation scouring monitoring and protection measures lack quantitative evaluation, and power limit control is not real-time. Comprising the steps of data acquisition and real-time monitoring, scouring influence evaluation, fan power dynamic limit control, quantitative evaluation of fan protection measures and system integration and intelligent decision support. By monitoring the basic scouring condition, the fan state and environmental data in real time and combining numerical simulation and neural network optimization, the influence of scouring on the stability of the fan is dynamically evaluated, power output is automatically adjusted, safe and stable operation of the fan is ensured, quantitative evaluation and optimization of protective measures are achieved, the management precision and response speed of the wind power plant are improved, and the wind power plant management cost is reduced. The safety and the economical efficiency of the offshore wind turbine generator are improved; intelligent early warning and fault prediction can be integrated, prevention measures are taken before potential risks occur, non-planned downtime is shortened, and a comprehensive intelligent solution is provided for operation of the wind power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind turbines, and particularly to a method for dynamically monitoring the scour of an offshore wind turbine foundation and evaluating power limitation. Background Art

[0002] As an important part of the renewable energy field, offshore wind turbines have received extensive attention and development in recent years. These turbines are usually deployed in the marine environment to generate electricity by using wind power and provide clean energy for the power grid. However, the complexity and harshness of the marine environment pose many challenges to the stable operation of offshore wind turbines, and the problem of foundation scour is particularly prominent.

[0003] Foundation scour refers to the phenomenon that the soil or seabed material around the foundation of a wind turbine is gradually washed away due to the action of marine environmental factors such as waves, tides, and wind. Long-term foundation scour will lead to a decrease in foundation stability, and problems such as settlement, inclination, or exposure may occur. These problems will seriously affect the operation stability and wind resistance of the wind turbine, and further threaten the economy and safety of the wind farm.

[0004] At present, the monitoring and protection measures for the foundation scour problem of offshore wind turbines have not been fully quantitatively evaluated. Traditional monitoring methods are mostly based on static data analysis, and the impact is evaluated by regularly detecting the foundation scour situation. However, this method lacks a dynamic response and real-time adjustment mechanism, and cannot monitor the change of foundation scour in real time and take corresponding protection measures accordingly. Therefore, it is difficult to effectively evaluate the power dynamic limitation of the unit and the effectiveness of its protection measures when the foundation scour is relatively serious.

[0005] In the prior art, although some technical solutions for monitoring and protecting the foundation scour of offshore wind turbines have been proposed, these solutions often have problems such as insufficient monitoring accuracy, unscientific evaluation methods, and ineffective protection measures. For example, some solutions only monitor the foundation scour situation through simple physical measurement means and cannot accurately reflect the real situation of the scour; some solutions ignore the dynamic change factors of the marine environment when evaluating the impact of the scour on the stability of the wind turbine, resulting in inaccurate evaluation results; and some solutions lack pertinence and flexibility when taking protection measures and cannot adjust the protection measures in real time according to the scour situation.

[0006] For example, CN119084250A discloses a method and system for monitoring the scour depth of offshore wind turbine foundations. This method collects the vibration acceleration at different positions of the offshore wind turbine in real time, obtains the natural frequency of the wind turbine through filtering and Fourier transformation, and then calculates the critical frequency based on the natural frequency of the offshore wind turbine and the scour limit depth, and finally predicts whether the scour depth is in a safe range. However, although the method proposed by CN119084250A has achieved the monitoring of the foundation scour depth to a certain extent, its technical solution still has some defects and deficiencies, such as limited monitoring accuracy, unclear quantitative relationship, lack of dynamic response mechanism, single protective measures, and low system integration.

[0007] In view of the above problems, there is an urgent need for a method that can monitor the foundation scouring in real time, dynamically evaluate the impact of scouring on the stability of wind turbines, and take corresponding dynamic power restrictions and protective measures based on the evaluation results. This method should be able to comprehensively consider the dynamic changes in the marine environment, improve the accuracy of monitoring and evaluation, and ensure the effectiveness and pertinence of protective measures, thereby ensuring the safe and stable operation of offshore wind turbines. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a method for dynamic monitoring and power limitation assessment of offshore wind turbine foundation scour, so as to solve the problems existing in the field of offshore wind turbine foundation scour monitoring and assessment, such as low monitoring accuracy, lack of dynamic response mechanism, insufficient quantitative assessment of protective measures and low system integration.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for dynamic monitoring and power limitation assessment of offshore wind turbine foundation scour, comprising the following steps: Step 1: Real-time monitoring of offshore wind turbine foundation scouring, wind turbine status and environmental parameters; Step 2: Combine numerical simulation and neural network technology to dynamically evaluate the impact of scouring on wind turbine stability; Step 3: According to the evaluation results, realize the dynamic limit control of wind turbine power; Step 4: Conduct quantitative evaluation of protection measures and optimize protection strategies.

[0010] In a preferred solution, the real-time monitoring in Step 1 includes collecting data on foundation scouring depth, scouring rate and scouring direction through sensors.

[0011] In a preferred solution, the environmental parameters in Step 1 include water flow velocity, water flow direction, wave height and seabed soil type.

[0012] In a preferred embodiment, in Step 2, the numerical simulation is based on the principles of hydrodynamics to simulate the basic scouring process and predict the development trend of scouring.

[0013] In a preferred embodiment, in Step 2, neural network technology is used to establish a scouring impact assessment model. By inputting monitoring data and environmental parameters, the degree of impact of scouring on the stability of the wind turbine is output.

[0014] In a preferred embodiment, in Step 3, the power dynamic limit control method includes setting a power limit threshold. When the degree of scouring impact exceeds the threshold, the output power of the wind turbine is automatically reduced.

[0015] In a preferred embodiment, the power limit threshold is determined based on the design parameters of the wind turbine, historical data of foundation scouring, and safety standards.

[0016] In a preferred embodiment, in Step 4, the quantitative assessment of protective measures includes evaluating the effectiveness of existing protective measures and putting forward improvement suggestions based on the evaluation results.

[0017] In a preferred embodiment, the quantitative assessment comprehensively considers the cost, implementation difficulty, and expected protective effect of the protective measures.

[0018] In a preferred embodiment, the assessment method further includes system integration and intelligent decision support. Specifically, it integrates monitoring data, evaluation results, and control instructions. It also includes system warning. When the degree of scouring impact or the state of the wind turbine is abnormal, a warning signal is automatically sent.

[0019] The dynamic monitoring and power limit assessment method for the foundation scouring of an offshore wind turbine provided by the present invention has the following beneficial effects: 1. By integrating multiple monitoring and assessment links, the present invention forms a complete system, improves the overall efficiency and accuracy of monitoring and assessment, reduces the operation and maintenance costs, and provides a strong guarantee for the safe and stable operation of offshore wind turbines.

[0020] 2. The present invention adopts advanced monitoring technologies such as high-resolution sonar equipment, as well as a variety of sensors and real-time monitoring means, provides more detailed and accurate scouring morphology and dynamic change data, improves the accuracy and real-time performance of scouring monitoring, and provides a reliable basis for scouring impact assessment.

[0021] 3. According to the scouring impact assessment results, the present invention optimizes the power limit control strategy through intelligent algorithms, dynamically adjusts the power output of the unit, avoids exacerbating the foundation damage due to overloading operation, enhances the system response ability, enables the system to respond to scouring impacts in real time, and improves the safety and reliability of the wind turbine.

[0022] 4. The present invention introduces machine learning algorithms and big data analysis technologies to automatically generate optimal decision-making suggestions, improving decision-making efficiency and accuracy. It can also predict in real time the impact of foundation scour on the stability of wind turbines and dynamically adjust power limits and protection measures according to the evaluation results, enhancing the adaptability and flexibility of the system.

[0023] 5. Based on real-time monitoring data and the evaluation results of scour impact, the present invention quantitatively evaluates the effectiveness of protection measures, providing a scientific basis and strong support for the optimization of protection measures. This helps to scientifically evaluate and optimize protection measures, reduce the operation and maintenance costs of wind farms, and improve the economic benefits of wind farms.

[0024] 6. The present invention has made significant improvements in monitoring technologies, dynamic response mechanisms, quantitative evaluation of protection measures, and system integration. It effectively solves the problems existing in the field of foundation scour monitoring and evaluation of offshore wind turbines, improves the safety and reliability of wind turbines, and provides strong guarantee for the long-term stable operation of offshore wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The present invention will be further described below in conjunction with the drawings and embodiments: Figure 1 is the technical flow chart of the evaluation method of the present invention; Figure 2 is the technical flow chart of Embodiments 1 and 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] The technical solution of the present invention will be further described below in conjunction with the drawings and embodiments: Embodiment 1 As Figure 1 and Figure 2 shown, this embodiment provides a method for dynamic monitoring of foundation scour and power limit evaluation of offshore wind turbines, and the specific steps are as follows: 1. Data acquisition and real-time monitoring Scour monitoring: Install seabed sensors, acoustic depth sounders, and pressure sensors around the foundation of the offshore wind turbine. These devices can monitor the scour situation around the foundation in real time, including the scour depth, scour speed, and changes in seabed morphology. For example, the seabed sensor can collect data every 10 minutes, and the acoustic depth sounder can measure the scour depth in real time with an accuracy of centimeter level.

[0027] Wind turbine status monitoring: Install vibration sensors and tilt sensors on the tower and foundation of the wind turbine. These sensors can monitor the operating status of the wind turbine in real time, including the vibration amplitude, frequency of the tower, and tilt angle of the unit. For example, the vibration sensor can detect minute vibration changes, and the tilt sensor can accurately measure the tilt angle of the unit with an accuracy of 0.1 degree.

[0028] Environmental data collection: Environmental data such as wind speed, wind direction, and air pressure are collected in real time through devices such as anemometers, wind vanes, and barometers. These data provide important environmental basis for subsequent scouring impact assessment and power limit control.

[0029] 2. Scouring impact assessment Numerical simulation and model prediction: Based on the principles of ocean engineering mechanics, numerical simulation methods such as finite element analysis and hydrodynamic simulation are adopted, and combined with real-time monitoring data, a dynamic assessment model of the impact of foundation scouring on the stability of wind turbines is established. This model can predict the mutual influence between parameters such as the scouring depth, speed of the foundation and the tilt, vibration of the wind turbine. For example, through finite element analysis, the stress condition of the foundation under different scouring depths can be simulated, and then its impact on the stability of the wind turbine can be evaluated.

[0030] Quantitative assessment of scouring impact: Using the above model, quantitative analysis of the scouring situation is carried out to evaluate the impact of foundation scouring of wind turbine units on the operation stability of wind turbines. For example, different scouring depth thresholds can be set. When the scouring depth exceeds a certain threshold, a warning mechanism is triggered to prompt the management to take corresponding measures.

[0031] 3. Dynamic power limit control of wind turbines Dynamic power limit mechanism: According to the scouring impact assessment results, dynamic power limit rules are set. When the scouring impact is severe, the system automatically reduces the power output of the unit. For example, when the scouring depth reaches a certain preset value, the system can reduce the power output of the unit to 80% of the rated power to avoid aggravating the foundation damage due to overloading operation.

[0032] Neural network optimization control: The reinforcement learning algorithm (such as Deep Q-Network, DQN) is used to optimize the power limit control strategy. Through the intelligent learning of the neural network, the power output strategy is adjusted to adapt to different scouring situations and environmental conditions. For example, in the reinforcement learning framework, the system can automatically adjust the power output according to real-time information such as scouring depth, wind speed, and wind turbine status to ensure that the wind turbine operates in a safe and efficient state.

[0033] 4. Quantitative assessment of wind turbine protection measures Assessment and optimization of protection measures: Based on real-time monitoring data and scouring impact assessment results, the effectiveness of protection measures is quantified. For example, the settlement situation of the foundation before and after installing settlement supports can be compared, as well as the impact of power limit measures on the operation stability of wind turbines. Through simulation and experimental data, the effectiveness of protection measures is verified and the implementation plan is optimized.

[0034] Implementation feedback of protection measures: Regularly evaluate the effectiveness of protection measures, and optimize the protection measures by combining the fan operation data and the scouring monitoring data. For example, if it is found that the settlement speed of the foundation accelerates, it is possible to consider thickening the protection layer or adopting other reinforcement measures.

[0035] 5. System integration and intelligent decision support Intelligent decision-making system: Integrate multiple modules such as data acquisition, real-time monitoring, scouring impact assessment, power limit control, and quantitative assessment of protection measures into a comprehensive evaluation and adjustment system. This system can automatically generate decision-making suggestions, such as adjusting power output, taking protection measures, etc. For example, when the scouring is severe and the inclination angle of the fan exceeds the preset value, the system can automatically adjust the power output to the safe range and notify the management personnel to take further protection measures.

[0036] Multi-task learning and decision support: Utilize the multi-task learning ability of neural networks to optimize multiple aspects such as scouring assessment, power control, and protection measures simultaneously. For example, through the multi-task learning framework, the system can comprehensively consider multiple factors such as scouring depth, wind speed, and fan status to generate the optimal power limit strategy and protection measure plan.

[0037] This embodiment not only describes in detail the key steps such as data acquisition, real-time monitoring, scouring impact assessment, power limit control, and quantitative assessment of protection measures, but also demonstrates through specific examples how to apply these steps to achieve the objectives of the present invention.

[0038] Embodiment 2 In another preferred embodiment, based on the above Embodiment 1, as Figure 1 and Figure 2 shown, on the basis of Embodiment 1, this embodiment provides another method for dynamic monitoring of offshore wind turbine foundation scouring and power limit assessment, aiming to further demonstrate the diversity and practicality of the present invention.

[0039] 1. Data acquisition and real-time monitoring Enhanced scouring monitoring: On the basis of Embodiment 1, this embodiment increases the number of scouring monitoring points and deploys high-resolution sonar equipment in key scouring areas. These devices can provide more detailed scouring morphology and dynamic change data, such as the shape, size, and position change of scouring pits. For example, the sonar equipment can perform a scan every 5 minutes to generate a real-time scouring topographic map.

[0040] Upgraded fan status monitoring: In addition to vibration sensors and tilt sensors, this embodiment also adds acceleration sensors and temperature sensors to monitor the operating status and temperature changes of key fan components (such as gearboxes and generators). These data help to more comprehensively evaluate the operating stability and potential failure risks of the fan.

[0041] Environmental data fusion: In addition to basic wind speed, wind direction, and air pressure data, this embodiment also integrates marine environmental data such as wave height, wave direction, and sea current speed. These data provide a more comprehensive environmental basis for scour impact assessment and power limit control.

[0042] 2. Scour impact assessment Model optimization and verification: This embodiment uses more advanced numerical simulation methods and machine learning algorithms to optimize the scour impact assessment model. By introducing more real-time monitoring data and historical data, the model is trained and verified, improving the prediction accuracy and reliability of the model. For example, the random forest algorithm is used to model the relationship between scour depth and wind turbine stability, and cross-validation is used to evaluate the performance of the model.

[0043] Scour risk level classification: Based on the scour impact assessment results, this embodiment introduces a scour risk level classification system. According to indicators such as scour depth, speed, and wind turbine status, the scour risk is divided into three levels: low, medium, and high, and corresponding protection measures and power limit strategies are formulated for different levels.

[0044] 3. Dynamic power limit control of wind turbines Segmented power limit strategy: This embodiment adopts a segmented power limit strategy to adjust the power output of the wind turbine according to the scour risk level. For example, at a low risk level, the wind turbine can operate normally; at a medium risk level, the wind turbine power output is reduced to 90% of the rated power; at a high risk level, the wind turbine power output is reduced to 70% of the rated power or lower.

[0045] Application of fuzzy control logic: In order to more flexibly respond to complex scour conditions and environmental conditions, this embodiment introduces fuzzy control logic to optimize the power limit control strategy. By defining fuzzy sets and fuzzy rules, the system can automatically adjust the power output according to real-time scour depth, wind speed, and wind turbine status information, ensuring that the wind turbine operates in a safe and efficient state.

[0046] 4. Quantitative evaluation of wind turbine protection measures Comparison of protection measure effects: This embodiment conducts effect comparison experiments on different types of protection measures (such as settlement supports, scour protection plates, seabed reinforcement, etc.). Through real-time monitoring data and numerical simulation results, the impacts of various protection measures on foundation scour and wind turbine stability are quantified, providing a basis for optimizing the implementation plan.

[0047] Cost-benefit analysis of protection measures: In addition to effect comparison, this embodiment also analyzes the cost-benefit of protection measures. By considering factors such as the cost of protection measures, implementation difficulty, and expected effects, the cost performance of different protection measures is evaluated, providing an economic basis for decision-making.

[0048] 5. System Integration and Intelligent Decision Support Upgrade of the Comprehensive Evaluation and Regulation System: On the basis of Embodiment 1, this embodiment upgrades the comprehensive evaluation and regulation system. The system integrates more data acquisition modules, evaluation models and control strategies, providing more comprehensive decision support functions. For example, the system can automatically generate scour risk warning reports, power limit recommendation reports, and protection measure optimization reports, etc.

[0049] Application of the Intelligent Decision Support System: In order to further improve the decision-making efficiency and accuracy, this embodiment introduces an intelligent decision support system. Based on machine learning algorithms and big data analysis techniques, the system can automatically analyze real-time monitoring data and historical data to generate optimal decision recommendations. For example, when the scour risk level increases, the system can automatically adjust the power limit strategy and notify the management to take corresponding protection measures.

[0050] This embodiment not only demonstrates the diversity and practicality of the present invention in aspects such as data acquisition, real-time monitoring, scour impact assessment, power limit control, and quantitative assessment of protection measures, but also shows through specific examples how to apply these steps to achieve the objectives of the present invention.

[0051] In a preferred solution, the real-time monitoring in Step 1 includes collecting data on the basic scour depth, scour rate, and scour direction through sensors; with the above settings, combined with big data analysis techniques, the collected data is processed and pattern-recognized in real time to promptly detect scour anomalies, providing accurate support for subsequent maintenance decisions and ensuring the safe and stable operation of water conservancy facilities.

[0052] In a preferred solution, the environmental parameters in Step 1 include water flow velocity, water flow direction, wave height, and seabed soil type; with the above settings, it aims to comprehensively consider the environmental impacts of underwater operations to ensure safe and efficient operations; at the same time, the solution also stipulates the frequency and methods of real-time monitoring of these parameters, as well as emergency handling measures in case of anomalies.

[0053] In a preferred solution, the numerical simulation in Step 2 is based on the principles of fluid dynamics to simulate the basic scour process and predict the development trend of scour; with the above settings, combined with high-precision grid division and advanced turbulence models, it ensures the accuracy of the simulation results; at the same time, by real-time monitoring the scour depth and range, the simulation parameters are dynamically adjusted to further optimize the scour protection strategy.

[0054] In the preferred solution, in Step 2, neural network technology is used to establish an erosion impact assessment model. By inputting monitoring data and environmental parameters, it outputs the degree of impact of erosion on the stability of the wind turbine. With the above settings, it can accurately predict the potential threats of erosion to the stable operation of the wind turbine, provide a scientific basis for subsequent maintenance decisions, and ensure the efficient and safe operation of the wind farm.

[0055] In the preferred solution, in Step 3, the power dynamic limit control method includes setting a power limit threshold. When the degree of erosion impact exceeds the threshold, the output power of the wind turbine is automatically reduced. With the above settings, it can effectively avoid the problem of wind turbine overload caused by erosion. Further, by real-time monitoring the degree of erosion impact and dynamically adjusting the power limit threshold, it ensures that the wind turbine operates in a safe and efficient state.

[0056] In the preferred solution, the power limit threshold is determined based on the design parameters of the wind turbine, historical data of foundation erosion, and safety standards. With the above settings, it aims to ensure the structural integrity and stability of the wind turbine under different operating conditions. At the same time, through real-time monitoring and adjustment, it can effectively respond to extreme weather conditions and foundation changes, ensuring the long-term efficient operation of the wind farm.

[0057] In the preferred solution, in Step 4, the quantitative assessment of protective measures includes evaluating the effectiveness of existing protective measures and putting forward improvement suggestions based on the evaluation results. With the above settings, it also introduces a dynamic monitoring mechanism to track the implementation effect of protective measures in real time, ensuring the accuracy of the evaluation results and the timeliness of improvement suggestions, thus forming a closed-loop safety management system.

[0058] In the preferred solution, the quantitative assessment comprehensively considers the cost, implementation difficulty, and expected protective effect of protective measures. With the above settings, it ensures the comprehensiveness and accuracy of the assessment. Through detailed cost analysis, objective evaluation of implementation difficulty, and scientific prediction of expected protective effects, this solution can provide strong support for enterprises to select the most suitable protective measures.

[0059] In the preferred solution, the assessment method also includes system integration and intelligent decision support. Specifically, it integrates monitoring data, evaluation results, and control instructions. It also includes system warning. When the degree of erosion impact or the wind turbine status is abnormal, it automatically issues a warning signal. With the above settings, it can achieve comprehensive monitoring and management of the erosion impact on the wind turbine, improve the operation and maintenance efficiency and safety of the wind farm. In addition, by intelligently analyzing historical data and optimizing future maintenance strategies, it further extends the service life of the wind turbine and reduces operating costs.

[0060] In summary, the dynamic monitoring and power limit assessment method for the foundation scour of offshore wind turbines proposed by the present invention comprehensively solves the problems existing in the field of offshore wind turbine foundation scour monitoring and assessment, such as low monitoring accuracy, lack of dynamic response mechanism, insufficient quantitative assessment of protection measures, and low system integration. This solution organically combines multiple links, such as data acquisition, real-time monitoring, scour impact assessment, power limit control, and quantitative assessment of protection measures, to form a complete system. This integrated method is not common in previous research and practice. By adopting advanced monitoring technologies such as high-resolution sonar equipment, acceleration sensors, and temperature sensors, it provides more detailed and accurate data on scour morphology, dynamic changes, and the operating status of key components of the wind turbine, laying a reliable foundation for scour impact assessment and power limit control. At the same time, a scour risk level classification system is introduced, and different levels of protection measures and power limit strategies are formulated according to indicators such as scour depth, speed, and wind turbine status. The method of risk classification management is novel and unique. In addition, the solution also introduces an intelligent decision support system, which uses machine learning algorithms and big data analysis technologies to automatically generate optimal decision-making suggestions, improving the decision-making efficiency and accuracy. More importantly, the present invention proposes a method for setting dynamic power limit rules based on the results of scour impact assessment, and adopts a reinforcement learning algorithm to optimize the power limit control strategy. Through the intelligent learning of the neural network, it adapts to different scour situations and environmental conditions. This method of applying the neural network to the power control of wind turbines has significant innovation. The solution also utilizes the multi-task learning ability of the neural network to comprehensively consider multiple factors to generate optimal power limit strategies and protection measure plans, improving the overall system performance and efficiency. Finally, a method for quantifying the effectiveness of protection measures based on real-time monitoring data and scour impact assessment results is proposed, and the cost-benefit of the protection measures is analyzed, providing strong support for the safe and efficient operation of wind turbines. This method of quantitative assessment and optimization is not common in previous wind turbine management.

Claims

1. A method for dynamic monitoring of scour of an offshore wind turbine foundation and power limit assessment, characterized in that It includes the following steps: Step 1: Monitor the foundation scouring condition, the status of the wind turbine, and environmental parameters of the offshore wind turbine in real time; Step 2: Combine numerical simulation and neural network technology to dynamically evaluate the impact of scouring on the stability of the wind turbine; Step 3: According to the evaluation results, achieve dynamic limit control of the wind turbine power; Step 4: Quantitatively evaluate the protection measures and optimize the protection strategy.

2. The dynamic monitoring method for scour of the foundation of an offshore wind turbine and the power limit assessment method according to claim 1, characterized in that: The real-time monitoring in Step 1 includes collecting data on the foundation scouring depth, scouring rate, and scouring direction through sensors.

3. The dynamic monitoring method for scour of an offshore wind turbine foundation and the power limit assessment method according to claim 2, characterized in that: The environmental parameters in Step 1 include water flow velocity, water flow direction, wave height, and seabed soil type.

4. The dynamic monitoring method for scour of an offshore wind turbine foundation and the power limit assessment method according to claim 3, characterized in that: The numerical simulation in Step 2 is based on the principle of fluid dynamics to simulate the foundation scouring process and predict the development trend of scouring.

5. The method for dynamic monitoring of scour and power limit assessment of an offshore wind turbine foundation according to claim 4, wherein: The neural network technology in Step 2 is used to establish a scouring impact evaluation model, input the monitoring data and environmental parameters, and output the degree of impact of scouring on the stability of the wind turbine.

6. The dynamic monitoring method for scour of the foundation of an offshore wind turbine and the power limit assessment method according to claim 5, characterized in that: The power dynamic limit control method in Step 3 includes setting a power limit threshold. When the degree of scouring impact exceeds the threshold, the output power of the wind turbine is automatically reduced.

7. The dynamic monitoring method for scour of an offshore wind turbine foundation and the power limit assessment method according to claim 6, characterized in that: The power limit threshold is determined according to the wind turbine design parameters, historical data of foundation scouring, and safety standards.

8. The method for dynamically monitoring the scour of an offshore wind turbine foundation and evaluating power limitation according to claim 7, characterized in that: The quantitative evaluation of the protection measures in Step 4 includes evaluating the effectiveness of the existing protection measures and putting forward improvement suggestions according to the evaluation results.

9. The dynamic monitoring method for scour of an offshore wind turbine foundation and the power limit assessment method according to claim 8, characterized in that: The quantitative evaluation comprehensively considers the cost, implementation difficulty, and expected protection effect of the protection measures.

10. The dynamic monitoring method for scour of an offshore wind turbine foundation and the power limit assessment method according to claim 9, characterized in that: The evaluation method also includes system integration and intelligent decision support, specifically integrating the monitoring data, evaluation results, and control instructions; it also includes system warning. When the degree of scouring impact or the status of the wind turbine is abnormal, a warning signal is automatically sent.

Images