Flow control system for improving generating efficiency of fan blade

Abstract

The invention discloses a flow control system for improving the power generation efficiency of fan blades, and relates to the technical field of wind power generation. The flow control system for improving the power generation efficiency of the fan blade comprises a data acquisition and preprocessing module which is used for acquiring blade surface and surrounding environment data in real time; the dynamic flow control module is used for carrying out dynamic flow evaluation on the preprocessed blade surface and surrounding environment data and carrying out dynamic flow control according to a dynamic flow evaluation result; the wind speed and airflow prediction module is used for performing wind speed and airflow state prediction according to the real-time blade surface and surrounding environment data and pre-adjusting a vortex generator and a flap in advance according to a wind speed and airflow state prediction result; and the multi-layer feedback optimization module is used for comprehensively analyzing the local feedback result of the blade and carrying out blade power generation efficiency optimization adjustment according to the local feedback result of the blade. The problem that the aerodynamic performance of the fan blade is imbalanced due to dramatic changes of the wind speed and the airflow state is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a flow control system for improving the power generation efficiency of wind turbine blades. Background Art

[0002] At present, wind power generation is widely used worldwide as a clean and renewable energy source, and its power generation efficiency and system stability are directly related to the overall energy utilization efficiency. In the prior art, in order to improve the aerodynamic performance and power generation efficiency of wind turbine blades, it is usually necessary to rely on offline CFD simulation, wind tunnel tests and a large amount of historical operating data to establish an operating database, and adjust the blade angle and power output through preset control strategies and traditional adaptive control algorithms (such as PID, fuzzy control, etc.). At the same time, the system uses a variety of sensors to collect data on the blade surface and its surrounding environment in real time, in order to cope with changes in wind speed, wind direction and airflow state to a certain extent.

[0003] However, due to the fact that wind speed and airflow often change dramatically and rapidly in actual working conditions, the traditional method has obvious deficiencies in response speed, local aerodynamic adjustment accuracy and overall coordination. This deficiency not only makes it difficult for the blade aerodynamic characteristics to always remain in the optimal state, but also when the wind speed changes suddenly, the control system often responds lagging, resulting in local airflow separation and abnormal pressure distribution, thereby reducing the overall power generation efficiency. It may also increase mechanical load and fatigue effects, shortening the service life of the equipment.

[0004] In addition, the existing system lacks an effective feedforward and feedback coordination mechanism between modules, and is unable to integrate the local, overall and grid-connected operating states in real time for dynamic optimization control, which greatly reduces the operating stability and energy capture capabilities of the wind turbine in complex environments.

[0005] Therefore, there is an urgent need for a flow control system that improves the power generation efficiency of wind turbine blades. Summary of the invention

[0006] Technical issues solved

[0007] In view of the deficiencies in the prior art, the present invention provides a flow control system for improving the power generation efficiency of wind turbine blades, thereby solving the problem of aerodynamic performance imbalance of wind turbine blades caused by drastic changes in wind speed and airflow state.

[0008] Technical Solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: a flow control system for improving the power generation efficiency of wind turbine blades, including a data acquisition and preprocessing module: real-time acquisition of blade surface and surrounding environment data, and storage of the blade surface and surrounding environment data in a working condition database, and preprocessing of the blade surface and surrounding environment data; a dynamic flow control module: dynamic flow evaluation of the preprocessed blade surface and surrounding environment data, and dynamic flow control based on the dynamic flow evaluation results; a wind speed and airflow prediction module: predicting the wind speed and airflow state based on the real-time blade surface and surrounding environment data, and pre-adjusting the vortex generator and flaps based on the wind speed and airflow state prediction results; a multi-layer feedback optimization module: comprehensive analysis of the local feedback results of the blades, and optimization and adjustment of the blade power generation efficiency based on the local feedback results of the blades.

[0010] Furthermore, the real-time acquisition of blade surface and surrounding environment data, and the storage of the blade surface and surrounding environment data in the operating condition database specifically includes: the blade surface and surrounding environment data include: current wind speed, local pressure, current blade lift-to-drag ratio, pitch angle and generator speed, and the blade surface and surrounding environment data are stored in the operating condition database; the current wind speed is obtained through a wind speed sensor, the local pressure is obtained through a pressure sensor and the current blade lift-to-drag ratio is calculated, the pitch angle is obtained through a self-pitch actuator position sensor, and the generator speed, output power, voltage and current are obtained through an electrical measurement unit.

[0011] Furthermore, the specific process of preprocessing the blade surface and surrounding environment data is as follows: aligning the blade surface and surrounding environment data according to the same timestamp, eliminating obvious noise and invalid data, normalizing the blade surface and surrounding environment data, and calculating the historical blade surface and surrounding environment data in the working condition database through the edge algorithm to obtain the optimal lift-to-drag ratio of the current working condition, the optimal pressure distribution of the current working condition, the standard deviation of wind speed fluctuations, the wind speed change rate, the optimal blade pitch of the current working condition, the acceptable fluctuation threshold of the current working condition and the power optimization setting value.

[0012] Furthermore, the dynamic flow evaluation is performed on the pre-processed blade surface and surrounding environment data, and the dynamic flow control is performed according to the dynamic flow evaluation result. The specific process is as follows: obtaining the current blade lift-to-drag ratio, the optimal lift-to-drag ratio of the current working condition, the local pressure, the optimal pressure distribution of the current working condition, the standard deviation of wind speed fluctuations and the acceptable fluctuation threshold of the current working condition, and obtaining the dynamic flow control evaluation value by comprehensive calculation of the current blade lift-to-drag ratio, the optimal lift-to-drag ratio of the current working condition, the local pressure, the optimal pressure distribution of the current working condition, the standard deviation of wind speed fluctuations and the acceptable fluctuation threshold of the current working condition; setting several dynamic flow control thresholds to determine whether the current local start-up is in a reasonable range, and when the dynamic flow control evaluation value exceeds the set dynamic flow control threshold, sending a small angle position adjustment instruction to the local "smart flap" and "vortex generator", and adjusting the dynamic flow control threshold according to the change trend.

[0013] Furthermore, the specific method for obtaining the dynamic flow control evaluation value is: Where η DFC represents the dynamic flow control evaluation value, R m Indicates the lift-to-drag ratio of the current blade, R t represents the optimal lift-to-drag ratio of the current working condition, w1 represents the weight coefficient of the lift-to-drag ratio, P m Indicates the local pressure, P opt represents the optimal pressure distribution of the current working condition, w2 represents the weight coefficient of pressure, σ represents the standard deviation of wind speed fluctuation, U t It indicates the acceptable fluctuation threshold of the current working condition, and w3 indicates the weight coefficient of wind speed.

[0014] Furthermore, the wind speed and airflow state is predicted based on the real-time blade surface and surrounding environment data, and the vortex generators and flaps are pre-adjusted according to the wind speed and airflow state prediction results. The specific process is as follows: obtaining the current wind speed, wind speed fluctuation standard deviation and wind speed change rate, and obtaining the wind speed and airflow prediction value by comprehensive calculation of the current wind speed, wind speed fluctuation standard deviation and wind speed change rate; predicting whether the future wind speed will increase or decrease based on the wind speed and airflow prediction value to adjust the vortex generators and flap angles; vortex generator: if the wind speed is predicted to increase, extend it in advance to enhance boundary layer control; if it is predicted to decrease, retract it to reduce drag; flap angle: determine the fine-tuning angle based on the wind speed and airflow prediction value to avoid aerodynamic shock due to lag when the actual wind speed arrives.

[0015] Furthermore, the specific method for obtaining the wind speed and airflow prediction value is: In the formula, ξ represents the wind speed and airflow prediction value, U(t) represents the current wind speed, and λ1 represents the wind speed level weight coefficient. represents the wind speed change rate, λ2 represents the wind speed change weight coefficient, σ represents the wind speed fluctuation standard deviation, and λ3 represents the wind speed fluctuation weight coefficient.

[0016] Furthermore, the specific process of the comprehensive analysis of the local feedback results of the blades is: obtaining local pressure, optimal pressure distribution of the current working conditions, pitch angle, optimal blade pitch of the current working conditions, generator speed and power optimization setting values, and obtaining a real-time dynamic adjustment value through comprehensive calculation of local pressure, optimal pressure distribution of the current working conditions, pitch angle, optimal blade pitch of the current working conditions, generator speed and power optimization setting values.

[0017] Furthermore, the specific method for obtaining the real-time dynamic adjustment value is: Where DA represents the real-time dynamic adjustment value, P m Indicates the local pressure, P opt represents the optimal pressure distribution of the current working condition, α represents the local pressure weight coefficient, φ opt Indicates the optimal pitch for the current working condition, φ m represents the pitch angle, β is the pitch angle weight coefficient, ω set represents the power optimization setting value, ω m represents the generator speed, and γ represents the generator speed weight coefficient.

[0018] Furthermore, the specific process of optimizing and adjusting the blade power generation efficiency according to the local feedback results of the blades is as follows: quickly adjusting the local angle of the intelligent flap or vortex generator according to the real-time dynamic adjustment value, so that the local aerodynamic state is slightly corrected. When the local adjustment exceeds the limit, the actuator automatically returns to the preset safe minimum interference position to avoid structural abnormalities. In combination with the overall power output demand of the wind turbine, the pitch angle is fine-tuned in time to achieve a balance between power and safety boundaries. By adjusting the generator control strategy, converter parameters, etc., the speed adjustment range is expanded or contracted in advance to adapt to fluctuations in load and grid-connected voltage. When the grid frequency or voltage fluctuates abnormally, the wind turbine actively cooperates and provides frequency modulation services to ensure system safety and stability.

[0019] Beneficial Effects

[0020] The present invention has the following beneficial effects:

[0021] (1) The present invention realizes the effect of real-time and accurate monitoring of the blade surface and environmental conditions through a multi-sensor data acquisition and preprocessing module, and effectively solves the problem of control failure caused by untimely data acquisition in the prior art.

[0022] (2) The present invention realizes precise real-time control of the local aerodynamic state of the blade through an online fine-tuning strategy based on the dynamic flow control evaluation value, thereby effectively solving the problem in the prior art that local aerodynamic performance disorders are difficult to correct in a timely manner.

[0023] (3) The present invention uses a wind speed and airflow prediction module to perform feedforward pre-adjustment of future wind speed changes, thereby achieving the effect of automatically adjusting the vortex generator and flap angle before the wind speed suddenly changes, effectively solving the problem of aerodynamic shock caused by response lag in the prior art.

[0024] (4) The present invention realizes the coordinated optimization effect of local, overall and grid-connected control through a multi-level feedback optimization mechanism of real-time dynamic adjustment value, and effectively solves the problem of insufficient operation stability caused by uncoordinated overall regulation in the prior art.

[0025] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a structural diagram of a flow control system for improving the power generation efficiency of wind turbine blades according to the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 The embodiment of the present invention provides a technical solution: a flow control system for improving the power generation efficiency of wind turbine blades, including a data acquisition and preprocessing module: real-time acquisition of blade surface and surrounding environment data, and storage of the blade surface and surrounding environment data in a working condition database, and preprocessing of the blade surface and surrounding environment data; a dynamic flow control module: dynamic flow evaluation of the preprocessed blade surface and surrounding environment data, and dynamic flow control according to the dynamic flow evaluation results; a wind speed and airflow prediction module: predicting the wind speed and airflow state according to the real-time blade surface and surrounding environment data, and pre-adjusting the vortex generator and the flap according to the wind speed and airflow state prediction results; a multi-layer feedback optimization module: comprehensive analysis of the local feedback results of the blades, and optimization and adjustment of the blade power generation efficiency according to the local feedback results of the blades.

[0029] Specifically, the blade surface and surrounding environment data are collected in real time, and the blade surface and surrounding environment data are stored in the working condition database. The specific process is: the blade surface and surrounding environment data include current wind speed, local pressure, current blade lift-to-drag ratio, pitch angle and generator speed. These key parameters are recorded in real time and stored in the working condition database; the current wind speed is obtained by the wind speed sensor to ensure accurate reflection of the environmental wind conditions; the local pressure data is collected by the pressure sensor, and the current blade lift-to-drag ratio is calculated based on these data, so as to accurately describe the aerodynamic state of the blade; at the same time, the pitch angle is obtained by the self-pitch actuator position sensor to monitor the real-time adjustment of the blade angle; in addition, the electrical parameters such as generator speed, output power, voltage and current are collected in real time by the electrical measurement unit to fully present the power generation status of the system.

[0030] In this embodiment, by collecting blade surface and surrounding environment data in real time and storing them in the working condition database, the system effectively realizes comprehensive monitoring of the status of wind turbine blades and their surrounding environment. This step ensures the accurate capture and recording of key parameters such as wind speed, local pressure, blade lift-to-drag ratio, pitch angle and generator speed. Specifically, the use of high-precision wind speed sensors can accurately measure the ambient wind speed, and the pressure sensor helps to obtain the local pressure of the blade and calculate the lift-to-drag ratio, reflecting the aerodynamic efficiency of the blade. The pitch angle is captured by a special position sensor, which is crucial for dynamically adjusting the blade angle to adapt to different wind conditions. At the same time, the generator operation data (speed, output power, voltage and current) collected by the electrical measurement unit provides the basis for the system's power output and efficiency analysis. This information is systematically integrated and stored in the working condition database, so that the entire wind turbine control system can respond and adjust quickly according to real-time data, thereby optimizing the operating efficiency and stability of the wind turbine, significantly improving the adaptability and energy capture efficiency of the wind turbine, and effectively solving the control challenges that the wind turbine may encounter in a complex and changing environment.

[0031] Specifically, the specific process of preprocessing the blade surface and surrounding environment data is as follows: First, the system aligns the collected blade surface and surrounding environment data according to the same timestamp to ensure the timing consistency of the data, which is crucial for subsequent dynamic analysis and real-time response. Next, the system will remove obvious noise and invalid data in the data. This step is to ensure data quality and avoid erroneous readings from interfering with control decisions. Next, all valid data are normalized and the data scale is unified for cross-condition comparison and analysis.

[0032] These pre-processed data are further processed by edge computing algorithms, and the system analyzes historical blade surface and surrounding environment data in the operating database, including historical aerodynamic performance indicators and environmental variables. This in-depth analysis enables the system to calculate and determine multiple key parameters, such as the optimal lift-to-drag ratio under the current operating conditions, which is an important indicator for measuring the aerodynamic efficiency of the blade; the optimal pressure distribution under the current operating conditions, which is directly related to the airflow behavior on the blade surface; the standard deviation of wind speed fluctuations, which evaluates the stability of the current ambient wind speed; the wind speed change rate, which provides quantitative information on the wind speed change trend; the optimal blade pitch under the current operating conditions, which critically affects the efficiency of the blade in capturing wind energy; and the acceptable fluctuation threshold and power optimization setting value under the current operating conditions. These parameters provide a basis for the dynamic adjustment of the system to achieve the best operating state. This preprocessing process not only improves the data processing efficiency, but also provides a scientific basis for subsequent real-time control and optimization decisions, effectively improving the operating efficiency and response speed of the entire system.

[0033] In this embodiment, the step of preprocessing the blade surface and surrounding environment data is extremely critical, and its main effect is to ensure the quality and consistency of data from acquisition to control through precise data cleaning, synchronization and normalization. Specifically, by aligning all data according to the same timestamp and eliminating obvious noise and invalid data, the process significantly improves the reliability of the data and the accuracy of the analysis. Normalization processing allows data from different sensors and under different environmental conditions to be compared and analyzed fairly. In addition, by analyzing the preprocessed data using edge computing algorithms, the system can extract key performance indicators such as the optimal lift-to-drag ratio, the optimal pressure distribution, and the optimal blade pitch for the current operating conditions. These calculation results directly affect subsequent dynamic control decisions. Through this step, the system not only realizes real-time monitoring and precise control of data, but also further improves the power generation efficiency and adaptability of the wind turbine by optimizing these key parameters, effectively solving the problems of control delays and performance degradation caused by improper data processing.

[0034] Specifically, dynamic flow evaluation is performed on the pre-processed blade surface and surrounding environment data, and dynamic flow control is performed based on the dynamic flow evaluation results. The specific process is as follows: first, key aerodynamic and environmental parameters are obtained: current blade lift-to-drag ratio, optimal lift-to-drag ratio under current working conditions, local pressure, optimal pressure distribution, wind speed fluctuation standard deviation, and acceptable fluctuation threshold under current working conditions. These parameters are extracted from the pre-processed data, among which the current blade lift-to-drag ratio directly reflects the aerodynamic efficiency of the blade, and the optimal lift-to-drag ratio and optimal pressure distribution are target values ​​set based on historical data and simulation results, which are used to evaluate whether the current blade performance reaches or approaches the ideal state.

[0035] The system then calculates the dynamic flow control evaluation value by comprehensively considering these parameters. This method allows the system to comprehensively evaluate the degree of deviation between the current state of the blade and the ideal state.

[0036] According to the dynamic flow control evaluation results, the system sets several dynamic flow control thresholds to determine whether the current state of the blade is in a reasonable working range. When the dynamic flow control evaluation value exceeds the preset threshold, it indicates that the blade performance deviates from the optimal working condition and immediate measures need to be taken to adjust it. At this time, the system sends small angle or position adjustment instructions to the local "smart flaps" and "vortex generators". These instructions are to fine-tune the airflow state on the blade surface and restore or optimize aerodynamic performance. At the same time, the system adjusts the dynamic flow control threshold in a timely manner according to the changing trend to ensure the flexibility and real-time nature of the control strategy.

[0037] In this embodiment, by performing dynamic flow evaluation on the pre-processed blade surface and surrounding environment data and implementing dynamic flow control, the system effectively realizes the precise control of the aerodynamic performance of the wind turbine blade. This process covers the acquisition and integration of key parameters such as the current blade lift-to-drag ratio, optimal lift-to-drag ratio, local pressure, optimal pressure distribution, wind speed fluctuation standard deviation and acceptable fluctuation threshold, and the dynamic flow control evaluation value is calculated by these parameters. The analysis result of the evaluation value is used to guide whether the system needs to be adjusted. If it exceeds the preset dynamic flow control threshold, it automatically triggers the sending of fine-tuning instructions to the smart flaps and vortex generators. This real-time dynamic adjustment not only improves the adaptability of the fan under unstable airflow conditions, but also optimizes the lift-to-drag ratio by finely controlling the aerodynamic state of the blades, thereby achieving higher power generation efficiency and system stability. This step significantly improves the fan's ability to respond to complex airflow changes, and effectively solves the problem of reduced aerodynamic efficiency caused by delayed response of the fan in the prior art.

[0038] Specifically, the dynamic flow control evaluation value is obtained as follows: Where η DFC represents the dynamic flow control evaluation value, which reflects the aerodynamic efficiency of the blade under actual working conditions, R m Indicates the lift-to-drag ratio of the current blade, which is directly calculated from the real-time measurement data of the blade surface pressure and flow velocity, and reflects the aerodynamic efficiency of the blade under actual working conditions. t It represents the optimal lift-to-drag ratio for the current working condition, which is usually determined based on historical data, CFD simulation or wind tunnel test results as a performance benchmark. w1 represents the weight coefficient of the lift-to-drag ratio, P m Represents the local pressure, obtained by the pressure sensor installed at the key position of the blade, P optrepresents the optimal pressure distribution of the current working condition, which is usually provided by wind tunnel testing and numerical simulation and stored in the working condition database for real-time query. w2 represents the weight coefficient of pressure, σ represents the standard deviation of wind speed fluctuation, which is obtained by continuous monitoring of high-precision wind speed sensors, and U t It indicates the acceptable fluctuation threshold of the current working condition, which is usually set according to the blade design specifications. w3 indicates the weight coefficient of wind speed, which is obtained from the working condition database. The value range is usually between 0 and 1. The specific value is optimized according to the design characteristics of the blade and the historical operation data.

[0039] In this embodiment, by calculating the dynamic flow control evaluation value, the difference between the current aerodynamic state of the blade and the ideal state is effectively quantified, thereby realizing refined dynamic flow control. The evaluation value combines the real-time data of the current blade lift-to-drag ratio, the optimal lift-to-drag ratio, the local pressure, the optimal pressure distribution, and the wind speed fluctuation, and weights and integrates these parameters through the set weight factor to obtain a specific value. The high or low value indicates the urgency and direction of the blade performance adjustment, so that the control system can respond quickly according to the real-time data. For example, when the dynamic flow control evaluation value exceeds the preset dynamic flow control threshold, the system will automatically adjust the angle of the smart flap and vortex generator on the blade to optimize the aerodynamic efficiency and power generation performance of the blade. In addition, the system can also adjust the threshold in time according to the changing trend of the dynamic flow control evaluation value to ensure the flexibility and effectiveness of the control strategy. This step not only improves the response speed of the fan to environmental changes, but also enhances the system's ability to operate stably under complex wind conditions, and effectively solves the problems of efficiency loss and increased mechanical load caused by delayed response in traditional technologies.

[0040] Specifically, the wind speed and airflow state is predicted based on the real-time blade surface and surrounding environment data, and the vortex generator and flap are pre-adjusted in advance according to the wind speed and airflow state prediction results. The specific process is: first, the current wind speed is obtained through a high-precision wind speed sensor, and the wind speed fluctuation standard deviation and wind speed change rate are calculated at the same time. These parameters can reflect the stability and change trend of the wind speed. By integrating these data, the system uses a preset mathematical model to calculate the wind speed and airflow prediction value, which provides a basis for the next control decision.

[0041] Based on the wind speed and airflow forecast values, the system can predict whether the wind speed will tend to increase or decrease in the future. This information is crucial for dynamically adjusting the vortex generators and flap angles. In terms of operational strategy, if the forecast results show that the wind speed will increase, the system will instruct the vortex generators to extend in advance to enhance boundary layer control, which helps to increase the adhesion of the blade surface, thereby increasing lift and reducing the risk of stalling due to increased wind speed. On the contrary, if the wind speed is predicted to drop, the vortex generators will be retracted to reduce unnecessary resistance and energy consumption. At the same time, the angle adjustment of the flaps is also based on the wind speed and airflow forecast values. By fine-tuning the angle of the flaps, the aerodynamic characteristics of the blades can be effectively adjusted to ensure that the blades can maintain optimal aerodynamic performance when the wind speed changes, avoiding aerodynamic shocks caused by response delays.

[0042] In this embodiment, by implementing the prediction of wind speed and airflow state and pre-adjusting the vortex generators and flaps based on the prediction results, the adaptability and operating efficiency of the fan under changing wind speed conditions are significantly improved. In this process, the system uses the real-time collected wind speed, wind speed fluctuation standard deviation and wind speed change rate to comprehensively calculate the wind speed and airflow prediction value, and accurately predict the rising or falling trend of the wind speed. Based on this prediction, the angles of the vortex generators and flaps can be adjusted accordingly in advance: when the wind speed is expected to rise, the vortex generators are extended to enhance boundary layer control, increase lift and prevent stall; when the wind speed is expected to drop, the vortex generators are retracted to reduce resistance and energy consumption. Fine-tuning the flap angle further optimizes the aerodynamic performance of the blades, ensuring that the fan is fully prepared before receiving the actual wind speed change, avoiding the loss of aerodynamic efficiency due to response lag. This strategy not only improves the power generation efficiency of the fan, but also reduces the mechanical wear and load caused by environmental changes, thereby optimizing the overall performance and reliability of the fan.

[0043] Specifically, the wind speed and airflow prediction value is obtained as follows: In the formula, ξ represents the wind speed and airflow prediction value, U(t) represents the current wind speed, which ensures the real-time and accuracy of the data, and λ1 represents the wind speed level weight coefficient. It represents the wind speed change rate. This parameter helps predict the trend change of wind speed. λ2 represents the wind speed change weight coefficient. σ represents the wind speed fluctuation standard deviation, which reflects the instability of wind speed. λ3 represents the wind speed fluctuation weight coefficients λ1, λ2 and λ3, which are obtained from the working condition database. The value range is usually between 0 and 1.

[0044] In this embodiment, through the accurate wind speed and airflow prediction method, the future wind speed changes can be effectively predicted, thereby providing key information for the wind turbine control system to optimize the adjustment of vortex generators and flaps. The prediction model combines the current wind speed, wind speed change rate and wind speed fluctuation standard deviation, and ensures that each influencing factor is considered according to its actual importance to the wind speed change through the set weight factors λ1, λ2 and λ3. This forward-looking control not only improves the response speed of the wind turbine to wind speed changes, but also significantly enhances the operating efficiency and stability of the entire system by adjusting the wind turbine components in advance to adapt to the predicted wind conditions. This step effectively solves the problem of traditional wind turbines not responding in time when facing rapidly changing environmental conditions, ensuring that the wind turbine can maintain the optimal operating state under various climatic conditions, maximizing power generation efficiency and reducing potential damage caused by sudden wind speed changes.

[0045] Specifically, the specific process of comprehensive analysis of blade local feedback results is to collect key blade and generator related parameters, such as local pressure, optimal pressure distribution of current working conditions, pitch angle, optimal blade pitch of current working conditions, generator speed and power optimization setting value. These parameters are obtained from different monitoring systems and sensors to ensure the real-time and accuracy of the data.

[0046] The local pressure is monitored in real time by the pressure sensor installed on the blade surface, and the optimal pressure distribution for the current working condition is obtained based on historical data and aerodynamic simulation to evaluate whether the airflow characteristics on the blade surface are close to the ideal state. The pitch angle is dynamically adjusted by the blade's pitch system and compared with the optimal pitch for the current working condition to evaluate the suitability of the current blade angle. The generator speed is provided by the speed sensor, which, together with the power optimization setpoint, forms a comprehensive monitoring of the wind turbine output performance.

[0047] Through the comprehensive calculation of these parameters, the system is able to generate a real-time dynamic adjustment value, which is calculated through a specific algorithm model and used to evaluate and adjust the operating status of the wind turbine in real time to ensure that each component operates at optimal efficiency. The model usually includes weight distribution to quantify the impact of different parameters, so that the control system can flexibly adjust according to the actual operating conditions and optimize the performance of the whole machine. The implementation of this process significantly improves the adaptability and efficiency of the wind turbine under changing environmental conditions, while also helping to reduce wear, extend equipment life, and ensure optimal power generation efficiency and stability at all wind speeds.

[0048] In this implementation scheme, through the process of comprehensive analysis of the local feedback results of the blades, the system can effectively achieve precise adjustment of the operating state of the wind turbine. The process involves collecting and calculating key parameters such as local pressure, optimal pressure distribution of the current working condition, pitch angle, optimal pitch of the current working condition, generator speed, and power optimization setting value. These parameters are monitored in real time by high-precision sensors and compared with the preset optimal working conditions. Through the comprehensive analysis of these data, the system generates real-time dynamic adjustment values, which guide the wind turbine to fine-tune various control parameters to ensure that the blades and generators are in the best operating state at any given moment. This measure not only optimizes the aerodynamic performance of the blades, improves the energy efficiency and output power of the wind turbine, but also significantly reduces the performance degradation caused by environmental changes or equipment aging, thereby improving the stability and reliability of the system and effectively extending the service life of the equipment.

[0049] Specifically, the real-time dynamic adjustment value is obtained by: Where DA represents the real-time dynamic adjustment value, which is used to evaluate and guide the real-time adjustment strategy of blades and generators. m Indicates the local pressure, P opt represents the optimal pressure distribution of the current working condition, α represents the local pressure weight coefficient, φ opt Indicates the optimal pitch for the current working condition, φ m represents the pitch angle, β is the pitch angle weight coefficient, ω set represents the power optimization setting value, ω m represents the generator speed, γ represents the generator speed weight coefficient, the weight coefficient is obtained from the operating condition database, and the value range is usually between 0 and 1.

[0050] In this embodiment, by realizing the calculation method of real-time dynamic adjustment value, it is possible to effectively monitor and respond to the deviation between the key parameters of the wind turbine and its ideal state. The method covers the comparison of the real-time measurement of local pressure, pitch angle and generator speed with their optimal setting values, and ensures the proper attention and response to the deviations of different parameters through the set weight factors α, β and γ. This calculation not only provides a quantitative evaluation index to guide the immediate adjustment of the wind turbine, but also enables the system to dynamically adjust the blade angle or generator setting according to the actual operating conditions to optimize performance and adapt to changing environmental conditions. The application of real-time dynamic adjustment values ​​significantly improves the adaptability of the system, optimizes the power generation efficiency, and reduces the mechanical fatigue or loss that may be caused by the deviation of parameters from the optimal state, thereby improving the operating stability and economy of the overall system. This step effectively solves the problem of efficiency loss caused by the untimely response to parameter deviations in the prior art, and ensures the optimal operation of the wind turbine in a changing environment.

[0051] Specifically, the specific process of optimizing the blade power generation efficiency according to the blade local feedback result is: quickly adjusting the local angle of the smart flap or vortex generator through real-time dynamic adjustment value, so as to make a small but accurate correction to the local aerodynamic state of the blade. This adjustment relies on real-time data obtained from sensors to ensure that each adjustment is based on the current wind turbine operating conditions.

[0052] In addition, if the local adjustment exceeds the predetermined safety or efficiency range, the actuator will automatically return the flaps or vortex generators to the preset safe minimum interference position. This safety mechanism prevents structural abnormalities or damage that may be caused by excessive adjustment. At the same time, the system will fine-tune the pitch angle in a timely manner according to the overall power output requirements of the wind turbine, which not only optimizes the power output, but also maintains the safety margin of the system.

[0053] Further adjustments include adjustments to the generator control strategy and converter parameters, which are designed to pre-expand or shrink the speed adjustment range according to load demand and grid voltage fluctuations. This pre-adjustment enables the wind turbine to proactively adapt to the grid's needs and provide necessary frequency regulation services when abnormal grid frequency or voltage fluctuations occur. Through these comprehensive measures, the system not only improves the wind turbine's power generation efficiency, but also ensures system safety and stability under changing environments and unstable grid conditions. This optimization strategy based on real-time dynamic adjustment effectively solves the response problem of traditional wind turbines in dealing with rapidly changing aerodynamic and grid conditions, and improves operating efficiency and system reliability.

[0054] In this implementation scheme, by optimizing and adjusting the power generation efficiency based on the local feedback results of the blades, the system significantly improves the adaptability and power generation efficiency of the wind turbine in complex environments. In this process, the real-time dynamic adjustment value is used to accurately control the angles of the intelligent flaps and vortex generators to fine-tune the local aerodynamic state of the blades to ensure that each adjustment accurately responds to the current wind turbine operating conditions. When the local adjustment reaches or exceeds the safety limit, the system can automatically call back to the minimum interference position to prevent structural damage. At the same time, this system adjusts the pitch angle according to the overall power demand to balance power output and mechanical safety. In addition, by adjusting the generator control strategy and converter parameters, the system pre-adapts to changes in load and grid voltage, and provides the necessary frequency regulation services to cope with abnormal fluctuations in grid frequency or voltage. These measures not only optimize the performance of the wind turbine, but also ensure its stable operation in an unstable environment, and effectively address the problems of response lag and inaccurate adjustment in traditional wind turbine technology.

[0055] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0056] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A flow control system for improving the power generation efficiency of wind turbine blades, characterized in that: include: Data acquisition and preprocessing module: collects blade surface and surrounding environment data in real time, stores the blade surface and surrounding environment data in the working condition database, and preprocesses the blade surface and surrounding environment data; Dynamic flow control module: performs dynamic flow evaluation on the pre-processed blade surface and surrounding environment data, and performs dynamic flow control according to the dynamic flow evaluation results; Wind speed and airflow prediction module: predicts the wind speed and airflow status based on the real-time blade surface and surrounding environment data, and pre-adjusts the vortex generator and flaps according to the wind speed and airflow status prediction results; Multi-layer feedback optimization module: Comprehensively analyze the local feedback results of the blades, and optimize and adjust the blade power generation efficiency based on the local feedback results of the blades.

2. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 1, characterized in that: The specific process of collecting blade surface and surrounding environment data in real time and storing the blade surface and surrounding environment data in the working condition database is as follows: The blade surface and surrounding environment data include: current wind speed, local pressure, current blade lift-to-drag ratio, pitch angle and generator speed, and the blade surface and surrounding environment data are stored in a working condition database; The current wind speed is obtained through the wind speed sensor, the local pressure is obtained through the pressure sensor and the current blade lift-to-drag ratio is calculated, the pitch angle is obtained through the self-pitch actuator position sensor, and the generator speed, output power, voltage and current are obtained through the electrical measurement unit.

3. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 1, characterized in that: The specific process of preprocessing the blade surface and surrounding environment data is as follows: The blade surface and surrounding environment data are aligned according to the same timestamp, obvious noise and invalid data are eliminated, the blade surface and surrounding environment data are normalized, and the historical blade surface and surrounding environment data in the working condition database are calculated through the edge algorithm to obtain the optimal lift-to-drag ratio of the current working condition, the optimal pressure distribution of the current working condition, the standard deviation of wind speed fluctuation, the wind speed change rate, the optimal blade pitch of the current working condition, the acceptable fluctuation threshold of the current working condition and the power optimization setting value.

4. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 1, characterized in that: The specific process of performing dynamic flow evaluation on the pre-processed blade surface and surrounding environment data and performing dynamic flow control according to the dynamic flow evaluation result is as follows: The lift-to-drag ratio of the current blade, the optimal lift-to-drag ratio of the current working condition, the local pressure, the optimal pressure distribution of the current working condition, the standard deviation of wind speed fluctuation and the acceptable fluctuation threshold of the current working condition are obtained, and the dynamic flow control evaluation value is obtained by comprehensive calculation of the lift-to-drag ratio of the current blade, the optimal lift-to-drag ratio of the current working condition, the local pressure, the optimal pressure distribution of the current working condition, the standard deviation of wind speed fluctuation and the acceptable fluctuation threshold of the current working condition; Several dynamic flow control thresholds are set to determine whether the current local startup is in a reasonable range. When the dynamic flow control evaluation value exceeds the set dynamic flow control threshold, a small angle position adjustment instruction is sent to the local "smart flap" and "vortex generator", and the dynamic flow control threshold is adjusted according to the change trend.

5. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 4, characterized in that: The specific method for obtaining the dynamic flow control evaluation value is: Where η DFC represents the dynamic flow control evaluation value, R m Indicates the lift-to-drag ratio of the current blade, R t represents the optimal lift-to-drag ratio of the current working condition, w1 represents the weight coefficient of the lift-to-drag ratio, P m Indicates the local pressure, P opt represents the optimal pressure distribution of the current working condition, w2 represents the weight coefficient of pressure, σ represents the standard deviation of wind speed fluctuation, U t It indicates the acceptable fluctuation threshold of the current working condition, and w3 indicates the weight coefficient of wind speed.

6. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 1, characterized in that: The specific process of predicting the wind speed and airflow state according to the real-time blade surface and surrounding environment data and pre-adjusting the vortex generator and the flap according to the wind speed and airflow state prediction result is as follows: Obtain the current wind speed, wind speed fluctuation standard deviation and wind speed change rate, and obtain the wind speed and airflow prediction value through comprehensive calculation of the current wind speed, wind speed fluctuation standard deviation and wind speed change rate; The vortex generator and flap angle are adjusted according to the wind speed and airflow prediction value to predict whether the future wind speed will increase or decrease; Vortex generators: If wind speed is predicted to increase, they are extended in advance to enhance boundary layer control; if wind speed is predicted to decrease, they can be retracted to reduce drag; Flap angle: Fine-tune the angle based on the predicted wind speed and airflow to avoid aerodynamic shock due to lag when the actual wind speed arrives.

7. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 6, characterized in that: The specific method for obtaining the wind speed and airflow prediction value is: In the formula, ξ represents the wind speed and airflow prediction value, U(t) represents the current wind speed, and λ1 represents the wind speed level weight coefficient. represents the wind speed change rate, λ2 represents the wind speed change weight coefficient, σ represents the wind speed fluctuation standard deviation, and λ3 represents the wind speed fluctuation weight coefficient.

8. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 1, characterized in that: The specific process of comprehensively analyzing the blade local feedback results is as follows: The local pressure, the optimal pressure distribution of the current working condition, the pitch angle, the optimal pitch of the current working condition, the generator speed and the power optimization setting value are obtained, and the real-time dynamic adjustment value is obtained through comprehensive calculation of the local pressure, the optimal pressure distribution of the current working condition, the pitch angle, the optimal pitch of the current working condition, the generator speed and the power optimization setting value.

9. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 8, characterized in that: The specific method for obtaining the real-time dynamic adjustment value is: Where DA represents the real-time dynamic adjustment value, P m Indicates the local pressure, P opt represents the optimal pressure distribution of the current working condition, α represents the local pressure weight coefficient, φ opt Indicates the optimal pitch for the current working condition, φ m represents the pitch angle, β is the pitch angle weight coefficient, ω set represents the power optimization setting value, ω m represents the generator speed, and γ represents the generator speed weight coefficient.

10. A flow control system for improving the power generation efficiency of wind turbine blades according to claim 1, characterized in that: The specific process of optimizing and adjusting the blade power generation efficiency according to the blade local feedback result is as follows: The local angle of the intelligent flap or vortex generator is quickly adjusted according to the real-time dynamic adjustment value to make a slight correction to the local aerodynamic state. When the local adjustment exceeds the limit, the actuator automatically returns to the preset safe minimum interference position to avoid structural abnormalities. Combined with the overall power output demand of the wind turbine, the pitch angle is fine-tuned in time to achieve a balance between power and safety boundaries. By adjusting the generator control strategy, converter parameters, etc., the speed adjustment range is expanded or contracted in advance to adapt to fluctuations in load and grid-connected voltage. When the grid frequency or voltage fluctuates abnormally, the wind turbine actively cooperates and provides frequency modulation services to ensure system safety and stability.

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