A flow control system for improving the power generation efficiency of a wind turbine blade

Abstract

The present invention discloses a flow control system for improving the power generation efficiency of a wind turbine blade, which relates to the technical field of wind power generation. The flow control system for improving the power generation efficiency of the wind turbine blade includes a data acquisition and preprocessing module: to collect the data on the blade surface and the surrounding environment in real time; a dynamic flow control module: to perform dynamic flow evaluation on the data of the blade surface and the surrounding environment after preprocessing, and perform dynamic flow control according to the results of the dynamic flow evaluation; a wind speed and air flow prediction module: to predict the wind speed and air flow state according to the real-time data of the blade surface and the surrounding environment, and perform advance pre-adjustment on the vortex generator and the flap according to the prediction results of the wind speed and air flow state; a multi-layer feedback optimization module: to comprehensively analyze the local feedback results of the blade, and perform optimization adjustment on the power generation efficiency of the blade according to the local feedback results of the blade. It solves the problem of the aerodynamic performance imbalance of the wind turbine blade caused by the drastic changes in the wind speed and air flow state.

Description

Technical Field

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

[0002] At present, wind power generation, as a clean and renewable energy source, has been widely used globally. 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 dependent on offline CFD simulations, wind tunnel tests, and a large amount of historical operating condition data to establish an operating condition database, and the adjustment of blade angles and power outputs is achieved 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 conditions to a certain extent.

[0003] However, due to the frequent and rapid changes in wind speed and airflow conditions in actual operating conditions, the traditional methods have obvious deficiencies in terms of response speed, local aerodynamic adjustment accuracy, and overall coordination. This deficiency not only makes it difficult for the aerodynamic characteristics of the blade to always remain in the optimal state, but also when the wind speed suddenly changes, the control system often shows a response lag, resulting in local airflow separation and abnormal pressure distribution, thereby reducing the overall power generation efficiency. At the same time, it may also exacerbate mechanical loads and fatigue effects, shortening the service life of the equipment.

[0004] In addition, the existing system lacks an effective feedforward and feedback cooperation mechanism between each module, and cannot dynamically optimize the control by comprehensively considering the operating states of local, overall, and grid-connected in real time. This greatly reduces the operating stability and energy capture ability of the wind turbine in a complex environment.

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

[0006] Technical Problems to be Solved

[0007] Aiming at the deficiencies of the prior art, the present invention provides a flow control system for improving the power generation efficiency of wind turbine blades, which solves the problem of the aerodynamic performance imbalance of wind turbine blades caused by the drastic changes in wind speed and airflow conditions.

[0008] Technical Solutions

[0009] To achieve the above object, the present invention is implemented through the following technical solutions: A flow control system for improving the power generation efficiency of a fan blade, comprising a data acquisition and preprocessing module: real-time collecting data on the blade surface and the surrounding environment, storing the data on the blade surface and the surrounding environment in a working condition database, and preprocessing the data on the blade surface and the surrounding environment; a dynamic flow control module: dynamically evaluating the flow on the blade surface and the surrounding environment after preprocessing, and performing dynamic flow control according to the results of the dynamic flow evaluation; a wind speed and air flow prediction module: predicting the wind speed and air flow state based on the real-time data on the blade surface and the surrounding environment, and pre-adjusting the vortex generator and the flap in advance according to the prediction results of the wind speed and air flow state; a multi-layer feedback optimization module: comprehensively analyzing the local feedback results of the blade, and optimizing and adjusting the power generation efficiency of the blade according to the local feedback results of the blade.

[0010] Further, the specific process of real-time collecting data on the blade surface and the surrounding environment and storing the data on the blade surface and the surrounding environment in the working condition database is as follows: The data on the blade surface and the surrounding environment includes: the current wind speed, local pressure, current blade lift-drag ratio, pitch angle, and generator speed, and the data on the blade surface and the surrounding environment is stored in the working 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-drag ratio is calculated, the pitch angle is obtained through a self-pitching actuator position sensor, and the generator speed, output power, voltage, and current are obtained through an electrical measurement unit.

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

[0012] Further, the specific process of dynamically evaluating the flow of the pre-processed blade surface and the surrounding environment data and performing dynamic flow control according to the dynamic flow evaluation results is as follows: Obtain the current blade lift-drag ratio, the optimal lift-drag ratio under the current working condition, the local pressure, the optimal pressure distribution under the current working condition, the standard deviation of wind speed fluctuations, and the acceptable fluctuation threshold under the current working condition, and comprehensively calculate the dynamic flow control evaluation value through the current blade lift-drag ratio, the optimal lift-drag ratio under the current working condition, the local pressure, the optimal pressure distribution under the current working condition, the standard deviation of wind speed fluctuations, and the acceptable fluctuation threshold under the current working condition; Set several dynamic flow control thresholds, and judge whether the current local start is within a reasonable range. When the dynamic flow control evaluation value exceeds the set dynamic flow control threshold, send a small-angle position adjustment instruction to the local "smart flap" and "vortex generator", and adjust the dynamic flow control threshold according to the change trend.

[0013] Further, the specific method for obtaining the dynamic flow control evaluation value is as follows: In the formula, η DFC represents the dynamic flow control evaluation value, R m represents the current blade lift-drag ratio, R t represents the optimal lift-drag ratio under the current working condition, w1 represents the weight coefficient of the lift-drag ratio, P m represents the local pressure, P opt represents the optimal pressure distribution under the current working condition, w2 represents the weight coefficient of the pressure, σ represents the standard deviation of wind speed fluctuations, U t represents the acceptable fluctuation threshold under the current working condition, w3 represents the weight coefficient of the wind speed.

[0014] Further, the specific process of predicting the wind speed and air flow state based on the real-time blade surface and surrounding environment data and pre-adjusting the vortex generator and flap in advance according to the wind speed and air flow state prediction results is as follows: Obtain the current wind speed, the standard deviation of wind speed fluctuations, and the wind speed change rate, and comprehensively calculate the wind speed and air flow prediction value through the current wind speed, the standard deviation of wind speed fluctuations, and the wind speed change rate; Predict whether the future wind speed will rise or fall based on the wind speed and air flow prediction value to adjust the angles of the vortex generator and the flap; Vortex generator: If the wind speed is predicted to rise, extend it in advance to enhance boundary layer control; If it is predicted to fall, it can be retracted to reduce drag; Flap angle: Determine the fine-tuning angle according to the wind speed and air flow prediction value to avoid aerodynamic shock caused by lag when the real wind speed arrives.

[0015] Further, the specific method for obtaining the wind speed and air flow prediction value is as follows: In the formula, ξ represents the wind speed and air flow prediction value, U(t) represents the current wind speed, 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 standard deviation of wind speed fluctuations, and λ3 represents the wind speed fluctuation weight coefficient.

[0016] Further, the specific process of comprehensively analyzing the local feedback result of the blade is as follows: Obtain the local pressure, the optimal pressure distribution under the current working condition, the pitch angle, the optimal blade pitch under the current working condition, the generator speed, and the power optimization set value, and comprehensively calculate the real-time dynamic adjustment value through the local pressure, the optimal pressure distribution under the current working condition, the pitch angle, the optimal blade pitch under the current working condition, the generator speed, and the power optimization set value.

[0017] Further, the specific method for obtaining the real-time dynamic adjustment value is as follows: In the formula, DA represents the real-time dynamic adjustment value, P m represents the local pressure, P opt represents the optimal pressure distribution under the current working condition, α represents the local pressure weight coefficient, φ opt represents the optimal blade pitch under the current working condition, φ m represents the pitch angle, β is the pitch angle weight coefficient, ω set represents the power optimization set value, ω m represents the generator speed, and γ represents the generator speed weight coefficient.

[0018] Further, the specific process of optimizing and adjusting the power generation efficiency of the blade according to the local feedback result of the blade is as follows: Rapidly adjust the local angle of the intelligent flap or the vortex generator according to the real-time dynamic adjustment value to slightly correct 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. Combining with the overall power output requirement of the wind turbine, timely fine-tune the pitch angle to achieve the balance between power and the safety boundary. By adjusting the generator control strategy, converter parameters, etc., pre-expand or contract the speed adjustment range in advance to adapt to the fluctuations of the load and grid voltage. When the grid frequency or voltage fluctuates abnormally, the wind turbine actively cooperates and provides frequency modulation services to ensure the safety and stability of the system.

[0019] Beneficial effects

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

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

[0022] (2). In the present invention, through the online fine-tuning strategy based on the dynamic flow control evaluation value, the effect of precise real-time control of the local aerodynamic state of the blade is realized, effectively solving the problem that it is difficult to timely correct the local aerodynamic performance imbalance in the prior art.

[0023] (3) In the present invention, the future wind speed change is pre-adjusted by the wind speed and air flow prediction module, thereby achieving the effect of automatically adjusting the eddy current generator and flap angle before the wind speed suddenly changes, effectively solving the problem of pneumatic shock caused by response lag in the prior art.

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

[0025] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1 , the embodiments of the present invention provide a technical solution: a flow control system for improving the power generation efficiency of a wind turbine blade, including a data acquisition and preprocessing module: real-time collecting the data of the blade surface and the surrounding environment, storing the data of the blade surface and the surrounding environment in the working condition database, and preprocessing the data of the blade surface and the surrounding environment; a dynamic flow control module: dynamically evaluating the flow of the preprocessed blade surface and the surrounding environment data, and performing dynamic flow control according to the dynamic flow evaluation result; a wind speed and air flow prediction module: predicting the wind speed and air flow state according to the real-time blade surface and the surrounding environment data, and pre-adjusting the eddy current generator and the flap in advance according to the wind speed and air flow state prediction result; a multi-level feedback optimization module: comprehensively analyzing the local feedback result of the blade, and optimizing and adjusting the power generation efficiency of the blade according to the local feedback result of the blade.

[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 preprocessed data are further processed by edge computing algorithms. The system analyzes historical blade surface and ambient environment data in the working condition database, which includes 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-drag ratio under the current working condition, which is an important indicator to measure the aerodynamic efficiency of the blade; the optimal pressure distribution under the current working condition, 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 trend of wind speed changes; the optimal pitch under the current working condition, which critically affects the wind energy capture efficiency of the blade; and the acceptable fluctuation threshold and power optimization setting value under the current working condition. 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 enhancing the operating efficiency and response speed of the entire system.

[0033] In this implementation plan, the steps of preprocessing the blade surface and ambient environment data are extremely crucial. Its main effect is to ensure the data quality and consistency from acquisition to control through precise data cleaning, synchronization, and normalization. Specifically, by aligning all data according to the same timestamp and removing obvious noise and invalid data, this process significantly improves the reliability of the data and the accuracy of analysis. The normalization process enables data from different sensors and different environmental conditions to be fairly compared and analyzed. In addition, by using edge computing algorithms to analyze the preprocessed data, the system can extract key performance indicators such as the optimal lift-drag ratio, the optimal pressure distribution, and the optimal pitch under the current working condition. These calculation results directly affect subsequent dynamic control decisions. Through this step, the system not only realizes real-time monitoring and precise control of the 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 delay and performance degradation caused by improper data processing.

[0034] Specifically, a dynamic flow assessment is performed on the preprocessed blade surface and ambient environment data, and dynamic flow control is carried out according to the dynamic flow assessment results. The specific process is as follows: First, key aerodynamic and environmental parameters are obtained: the current blade lift-drag ratio, the optimal lift-drag ratio under the current working condition, the local pressure, the optimal pressure distribution, the standard deviation of wind speed fluctuations, and the acceptable fluctuation threshold under the current working condition. These parameters are extracted from the preprocessed data, where the current blade lift-drag ratio directly reflects the aerodynamic efficiency of the blade, and the optimal lift-drag ratio and the optimal pressure distribution are target values set based on historical data and simulation results, used to evaluate whether the current blade performance reaches or approaches the ideal state.

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

[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 within a reasonable operating range. When the dynamic flow control evaluation value exceeds the preset threshold, it indicates that the blade performance deviates from the optimal operating condition, and immediate measures need to be taken for adjustment. At this time, the system sends adjustment instructions of small angles or positions to the local "intelligent flaps" and "vortex generators". These instructions are used to fine-tune the airflow state on the blade surface to restore or optimize the aerodynamic performance. At the same time, the system adjusts the dynamic flow control threshold in a timely manner according to the change trend to ensure the flexibility and real-time nature of the control strategy.

[0037] In this implementation scheme, by performing dynamic flow evaluation and implementing dynamic flow control on the preprocessed blade surface and surrounding environment data, the system effectively realizes the precise regulation of the aerodynamic performance of the wind turbine blade. This process covers obtaining and comprehensively considering key parameters such as the current lift-drag ratio of the blade, the optimal lift-drag ratio, local pressure, optimal pressure distribution, standard deviation of wind speed fluctuation, and acceptable fluctuation threshold. The dynamic flow control evaluation value is calculated through these parameters. The analysis result of this 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 intelligent flaps and vortex generators. This real-time dynamic adjustment not only improves the adaptability of the wind turbine under unstable airflow conditions but also optimizes the lift-drag ratio by precisely controlling the aerodynamic state of the blade, thereby achieving higher power generation efficiency and system stability. This step significantly enhances the response ability of the wind turbine to complex airflow changes and effectively solves the problem of reduced aerodynamic efficiency caused by the lag of the wind turbine response in the existing technology.

[0038] Specifically, the specific method for obtaining the dynamic flow control evaluation value is as follows: In the formula, η DFC represents the dynamic flow control evaluation value, which reflects the aerodynamic efficiency of the blade under actual working conditions. R m represents the current lift-drag ratio of the 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. R t represents the optimal lift-drag ratio under the current working condition, which is usually determined based on historical data, CFD simulation, or wind tunnel test results and serves as a performance benchmark. w1 represents the weight coefficient of the lift-drag ratio. P m represents the local pressure, which is obtained by pressure sensors installed at key positions on the blade. P optRepresents the optimal pressure distribution under the current operating condition, which is usually provided by wind tunnel tests and numerical simulations and stored in the operating condition database for real-time query. w2 represents the weight coefficient of pressure, and σ represents the standard deviation of wind speed fluctuation, which is continuously monitored by a high-precision wind speed sensor. U t Represents the acceptable fluctuation threshold under the current operating condition, which is usually set according to the blade design specifications. w3 represents the weight coefficient of wind speed, and the weight coefficient is obtained from the operating condition database, and its value range is usually between 0 and 1. The specific value is optimized according to the design characteristics of the blade and historical operation data.

[0039] In this implementation scheme, by calculating the evaluation value of dynamic flow control, the difference between the current aerodynamic state of the blade and the ideal state is effectively quantified, and then refined dynamic flow control is realized. This evaluation value combines the real-time data of the current lift-to-drag ratio of the blade, the optimal lift-to-drag ratio, the local pressure, the optimal pressure distribution, and the wind speed fluctuation. These parameters are weighted and synthesized through the set weight factors to obtain a specific value. The magnitude of this value indicates the urgency and direction of blade performance adjustment, enabling the control system to make rapid responses based on real-time data. For example, when the evaluation value of dynamic flow control exceeds the preset dynamic flow control threshold, the system will automatically adjust the angles of the smart flaps and vortex generators 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 a timely manner according to the change trend of the evaluation value of dynamic flow control to ensure the flexibility and effectiveness of the control strategy. This step not only improves the response speed of the wind turbine to environmental changes but also enhances the stable operation ability of the system under complex wind conditions, effectively solving the problems of efficiency loss and increased mechanical load caused by reaction lag in traditional technologies.

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

[0041] Based on the predicted value of wind speed and air flow, the system can predict whether the future wind speed will tend to increase or decrease. This information is crucial for dynamically adjusting the angles of vortex generators and flaps. In terms of the operation strategy, if the prediction result shows that the wind speed will increase, the system will instruct the vortex generator to extend in advance to enhance boundary layer control, which helps to increase the adhesion on the blade surface, thereby increasing lift and reducing the stall risk that may be caused by the increasing wind speed. On the contrary, if the predicted wind speed decreases, the vortex generator will retract to reduce unnecessary drag and energy consumption. At the same time, the angle adjustment of the flap is also based on the predicted value of wind speed and air flow. By finely tuning the angle of the flap, the aerodynamic characteristics of the blade can be effectively adjusted to ensure that the blade can maintain the best aerodynamic performance when the wind speed changes and avoid aerodynamic shocks caused by response lags.

[0042] In this implementation plan, by implementing the prediction of wind speed and air flow state and pre-adjusting the vortex generator and flap in advance based on the prediction result, the adaptability and operation efficiency of the wind turbine under changing wind speed conditions are significantly improved. In this process, the system comprehensively calculates the predicted value of wind speed and air flow by using the real-time collected wind speed, standard deviation of wind speed fluctuation, and wind speed change rate, and accurately predicts the rising or falling trend of the wind speed. Based on this prediction, the angles of the vortex generator and the flap can be adjusted in advance accordingly: when the wind speed is expected to increase, the vortex generator extends to enhance boundary layer control, increase lift and prevent stall; when the wind speed is expected to decrease, the vortex generator retracts to reduce drag and energy consumption. The fine adjustment of the flap angle further optimizes the aerodynamic performance of the blade, ensuring that the wind turbine is fully prepared before receiving the real wind speed change and avoiding the loss of aerodynamic efficiency caused by response lags. This strategy not only improves the power generation efficiency of the wind turbine but also reduces mechanical wear and load caused by environmental changes, thereby optimizing the overall performance and reliability of the wind turbine.

[0043] Specifically, the specific method for obtaining the predicted value of wind speed and air flow is as follows: In the formula, ξ represents the predicted value of wind speed and air flow, U(t) represents the current wind speed, ensuring the real-time and accuracy of the data, λ1 represents the wind speed level weight coefficient, represents the wind speed change rate, which helps to predict the trend change of the wind speed, λ2 represents the wind speed change weight coefficient, σ represents the standard deviation of wind speed fluctuation, which reflects the instability of the wind speed, and λ3 represents the wind speed fluctuation weight coefficient. λ1, λ2, and λ3 are obtained from the working condition database, and their value ranges are usually between 0 and 1.

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

[0045] Specifically, the specific process of comprehensively analyzing the local feedback results of the blade is as follows: Collect key parameters related to the blade and the generator, such as local pressure, the optimal pressure distribution under the current working condition, pitch angle, the optimal pitch under the current working condition, generator speed, and power optimization setting value. These parameters are obtained from different monitoring systems and sensors respectively, ensuring the real-time and accuracy of the data.

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

[0047] Through the comprehensive calculation of these parameters, the system can generate a real-time dynamic adjustment value, which is calculated by a specific algorithm model and used to evaluate and adjust the operating state of the fan in real time to ensure that each component operates at the best efficiency. This model usually includes weight allocation to quantify the influence 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 fan under changing environmental conditions, and also helps to reduce wear, extend the equipment life, and ensure the best power generation efficiency and stability at various wind speeds.

[0048] In this implementation, through the process of comprehensively analyzing the local feedback results of the blades, the system can effectively achieve precise adjustment of the operating state of the wind turbine. This process involves collecting and calculating key parameters such as local pressure, the optimal pressure distribution under the current working conditions, pitch angle, the optimal blade pitch under the current working conditions, generator speed, and power optimization set value. These parameters are real-time monitored 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 finely adjust various control parameters to ensure that the blades and the generator 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 specific method for obtaining the real-time dynamic adjustment value is as follows: In the formula, DA represents the real-time dynamic adjustment value, which is used to evaluate and guide the real-time adjustment strategy of the blades and the generator, P m represents the local pressure, P opt represents the optimal pressure distribution under the current working conditions, α represents the local pressure weight coefficient, φ opt represents the optimal blade pitch under the current working conditions, φ m represents the pitch angle, β represents the pitch angle weight coefficient, ω set represents the power optimization set value, ω m represents the generator speed, γ represents the generator speed weight coefficient, and the weight coefficients are obtained from the working condition database, and the value range is usually between 0 and 1.

[0050] In this implementation, by implementing the calculation method of the 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 their ideal states. This method covers the real-time measurement of local pressure, pitch angle, and generator speed and their comparison with the optimal set values. Through the set weight factors α, β, and γ, appropriate attention and response to different parameter deviations are ensured. 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 settings according to the actual operating conditions to optimize performance and adapt to changing environmental conditions. The application of the real-time dynamic adjustment value significantly improves the adaptability of the system, optimizes the power generation efficiency, and reduces mechanical fatigue or losses that may be caused by parameter deviation from the best state, thereby improving the overall operating stability and economy of the system. This step effectively solves the problem of efficiency loss caused by untimely response to parameter deviation in the prior art and ensures the optimal operation of the wind turbine in a changing environment.

[0051] Specifically, the specific process of optimizing and adjusting the power generation efficiency of the blade according to the local blade feedback result is as follows: The local angle of the intelligent flap or the vortex generator is quickly adjusted through real-time dynamic adjustment values, so as to make a small but precise 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 operating conditions of the wind turbine.

[0052] In addition, if the local adjustment exceeds the predetermined safety or efficiency range, the actuator will automatically return the flap or the vortex generator to the preset safe minimum interference position. This safety mechanism prevents structural abnormalities or damages 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 demand 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 aimed at pre-expanding or contracting the speed regulation range according to the fluctuations of the load demand and grid voltage. When abnormal fluctuations occur in the grid frequency or voltage, this pre-adjustment enables the wind turbine to actively adapt to the grid demand and provide necessary frequency regulation services. Through these comprehensive measures, the system not only improves the power generation efficiency of the wind turbine but also ensures the system safety and stability under variable environments and unstable grid conditions. This optimization strategy based on real-time dynamic adjustment effectively solves the response problems of traditional wind turbines in dealing with rapidly changing aerodynamic and grid conditions and improves the operating efficiency and system reliability.

[0054] In this implementation scheme, through the optimization and adjustment of the power generation efficiency based on the local blade feedback result, the system significantly improves the adaptability and power generation efficiency of the wind turbine in complex environments. During this process, real-time dynamic adjustment values are used to precisely control the angles of the intelligent flap and the vortex generator to fine-tune the local aerodynamic state of the blade, ensuring that each adjustment accurately responds to the current operating conditions of the wind turbine. When the local adjustment reaches or exceeds the safety limit, the system can automatically return 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 the power output and mechanical safety. In addition, by adjusting the generator control strategy and converter parameters, the system pre-adapts to the changes in load and grid voltage and provides 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 unstable environments, effectively addressing the problems of response lag and inaccurate adjustment existing in traditional wind turbine technologies.

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

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

Claims

1. A flow control system for improving the power generation efficiency of a fan blade, 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; 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; Set several dynamic flow control thresholds 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, send a small angle position adjustment instruction to the local "smart flap" and "vortex generator", and adjust the dynamic flow control threshold according to the change trend; The specific method for obtaining the dynamic flow control evaluation value is: In the formula, η DFC represents the dynamic flow control evaluation value, R m represents the current blade lift-drag ratio, R t represents the optimal lift-drag ratio under the current working condition, w1 represents the weight coefficient of the lift-drag ratio, P m represents the local pressure, P opt represents the optimal pressure distribution under the current working condition, w2 represents the weight coefficient of the pressure, σ represents the standard deviation of wind speed fluctuation, U t represents the acceptable fluctuation threshold under the current working condition, w3 represents the weight coefficient of the wind speed; 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. The flow control system for improving the power generation efficiency of a fan blade according to claim 1, wherein: 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 a fan blade 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 a fan blade 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 generator: If the predicted wind speed increases, it extends in advance to enhance boundary layer control; if the prediction decreases, it can be retracted to reduce drag. Flap angle: Determine the fine-tuning angle according to the predicted value of wind speed and airflow to avoid aerodynamic shock caused by lag when the actual wind speed arrives.

5. The flow control system for improving the power generation efficiency of a fan blade according to claim 4, wherein: The specific method for obtaining the predicted value of wind speed and airflow is as follows: Where ξ represents the predicted value of the wind speed airflow, U(t) represents the current wind speed, 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.

6. The flow control system for improving the power generation efficiency of a fan blade according to claim 1, wherein: The specific process of comprehensively analyzing the local feedback results of the blade is as follows: Obtain the local pressure, the optimal pressure distribution under the current working condition, the pitch angle, the optimal blade pitch under the current working condition, the generator speed, and the optimized setting value of power. Calculate the real-time dynamic adjustment value through comprehensive calculation of the local pressure, the optimal pressure distribution under the current working condition, the pitch angle, the optimal blade pitch under the current working condition, the generator speed, and the optimized setting value of power.

7. The flow control system for improving the power generation efficiency of a fan blade according to claim 6, characterized in that: The specific method for obtaining the real-time dynamic adjustment value is as follows: where DA represents the real-time dynamic adjustment value, P m represents the local pressure, P opt represents the optimal pressure distribution under the current working condition, α represents the local pressure weight coefficient, φ opt represents the optimal pitch distance under the current working condition, φ m represents the pitch angle, β is the pitch angle weight coefficient, ω set represents the power optimization set value, ω m represents the generator speed, γ represents the generator speed weight coefficient.

8. A flow control system for improving the power generation efficiency of a fan blade according to claim 1, characterized in that: The specific process of optimizing and adjusting the power generation efficiency of the blade according to the local feedback results of the blade is as follows: Quickly adjust the local angle of the intelligent flap or vortex generator according to the real-time dynamic adjustment value to slightly correct 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. Combine the overall power output demand of the wind turbine, and timely fine-tune the pitch angle to achieve the balance between power and safety boundaries. By adjusting the generator control strategy, converter parameters, etc., pre-expand or contract the speed regulation range to adapt to the fluctuations of load and grid voltage. When there are abnormal fluctuations in the grid frequency or voltage, the wind turbine actively cooperates and provides frequency modulation services to ensure the safety and stability of the system.

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