A control method and system of a surge alleviating low noise fan with adjustable blade angle

Abstract

The application relates to the technical field of fan control, in particular to a control method and system of a surge-reducing and low-noise fan with adjustable blade angles, which comprises the following steps: determining airflow sensitive adjustment nodes and airflow stable adjustment nodes based on the current airflow response rates of each blade adjustment unit in the fan; performing angle increment simulation on blade driving corresponding to each airflow sensitive adjustment node, and performing angle decrement simulation on blade stable driving corresponding to each airflow stable adjustment node, so as to obtain target increment angles of each blade driving and target decrement angles of each blade stable driving. According to the airflow response rates of each blade in the fan, the airflow sensitive adjustment nodes and the airflow stable adjustment nodes are determined, the blade angles can be more accurately controlled, over-adjustment can be avoided, energy consumption can be reduced, and the efficiency of the fan can be improved.

Description

Technical Field

[0001] This invention relates to the field of wind turbine control technology, specifically to a control method and system for a low-noise, surge-reducing wind turbine with adjustable blade angle. Background Technology

[0002] As the global energy structure gradually shifts towards renewable energy, wind energy, as a part of clean energy, is receiving increasing attention; wind power, as a form of green energy, is widely used in electricity production; wind turbine generators (wind turbines), as the core of wind power, directly affect the utilization efficiency of renewable energy in terms of their operating efficiency and stability; however, the high noise and surge problems of traditional wind turbines have become a major challenge in practical applications, especially in densely populated areas and around wind power plants, where noise pollution and operational instability pose challenges to the long-term operation of the surrounding environment and equipment.

[0003] Currently, traditional wind turbine control methods typically control blade angles based on fixed adjustment strategies, lacking dynamic monitoring and fine-tuning of the airflow response of each blade. This approach may lead to over-adjustment or under-adjustment, thereby affecting wind turbine efficiency, increasing energy consumption, and often failing to adjust according to real-time airflow fluctuations. This can easily cause surge phenomena during wind turbine operation, which not only increases noise and vibration but also leads to reduced wind turbine efficiency and increases the risk of mechanical wear and failure.

[0004] Furthermore, traditional methods are usually static, meaning that the blade angle adjustment strategy is set during the design phase without real-time adjustments based on airflow and noise data during wind turbine operation. This makes it impossible for the wind turbine to optimize its operating state in different operating cycles, which may lead to decreased efficiency and increased noise during long-term operation. Moreover, the adjustment of individual blades often does not take into account their mutual influence, which may lead to uncoordinated adjustments between individual blades, resulting in unstable airflow field, and may even cause local vibration or uneven airflow, affecting the overall performance of the wind turbine. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: a control method for a low-noise, surge-reducing fan with adjustable blade angle, comprising:

[0006] Based on the current airflow response rate of each blade adjustment unit in the wind turbine, airflow sensitive adjustment nodes and airflow stable adjustment nodes are determined; the airflow response rate is the rate of change of airflow field parameters after the blade adjustment unit angle changes, the airflow sensitive adjustment node is the adjustment unit whose airflow response rate exceeds a preset response threshold, and the airflow stable adjustment node is the adjustment unit whose airflow response rate is lower than the preset response threshold.

[0007] An angle increment simulation is performed on the blade drive corresponding to each of the airflow-sensitive adjustment nodes, and an angle decrement simulation is performed on the blade stabilization drive corresponding to each of the airflow-stabilization adjustment nodes, to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive.

[0008] Based on the predicted airflow fluctuation probability of each airflow stabilization adjustment node during the operating cycle, and the target reduction angle of the corresponding blade stabilization drive, the risk prediction of the blade stabilization drive to the corresponding airflow stabilization adjustment node is performed to obtain the risk prediction result of the surge and noise reduction.

[0009] In response to the risk prediction results of surge reduction and noise reduction, which characterize the risk of insufficient regulation of the blade stabilization drive, the target reduction angle of each blade stabilization drive is adjusted.

[0010] Based on the target incremental angle driven by each blade and the target reduction angle after the stable drive adjustment of each blade, the wind turbine blade angle is controlled to adjust within the operating cycle.

[0011] Preferably, the blade drive corresponding to each of the airflow-sensitive adjustment nodes is simulated with an angle increment, and the blade drive corresponding to each of the airflow-stabilizing adjustment nodes is simulated with an angle decrement, to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilizing drive, including:

[0012] Based on the spatial distance between the airflow-sensitive adjustment node and each of the airflow-stabilizing adjustment nodes, the airflow interference coefficient between the blade stabilizing drive branch corresponding to each of the airflow-stabilizing adjustment nodes and the blade drive corresponding to the airflow-sensitive adjustment node is determined respectively.

[0013] Based on the airflow interference coefficient from each of the blade stable drive branches to the blade drive, and the predicted airflow change amplitude of the airflow sensitive adjustment node in the operating cycle, the blade drive is simulated in angle increment to obtain the target incremental angle of the blade drive.

[0014] Based on the target incremental angle of each blade drive, the target reduction angle of each blade stable drive branch is determined.

[0015] Preferably, based on the airflow interference coefficient from each of the blade stabilization drive branches to the blade drive, and the predicted airflow change amplitude of the airflow-sensitive adjustment node during the operating cycle, the blade drive is simulated in an angle increment to obtain the target increment angle of the blade drive, including:

[0016] Based on the current blade angle driven by the blade corresponding to the airflow-sensitive adjustment node, and the predicted airflow change amplitude of the airflow-sensitive adjustment node in the operating cycle, a first predicted surge noise curve of the airflow-sensitive adjustment node in the operating cycle is determined; wherein, the first predicted surge noise curve includes surge risk value and noise decibel value at different time points;

[0017] For each candidate incremental angle, the blade drive is controlled to operate at the candidate incremental angle, and the airflow-sensitive adjustment node is controlled to change the airflow state according to the predicted airflow change amplitude within the duration corresponding to the operating cycle. The effect of the adjustment of each blade stable drive on the blade drive under its corresponding airflow disturbance coefficient is simulated, and the simulated surge noise curve of the airflow-sensitive adjustment node within the duration corresponding to the operating cycle is obtained.

[0018] Based on the deviation between the first predicted surge noise curve and the simulated surge noise curve corresponding to each of the candidate incremental angles, the candidate incremental angle that minimizes the deviation is determined as the target incremental angle for the blade drive among the candidate incremental angles.

[0019] Preferably, the target reduction angle of each blade stabilization drive branch is determined based on the target increment angle of each blade drive, including:

[0020] Based on the target incremental angle driven by each blade, and combined with the overall airflow field balance coefficient of the fan, the total incremental adjustment demand is calculated; wherein, the airflow field balance coefficient is a weighting parameter of the mutual influence of airflow at each adjustment node of the fan.

[0021] Based on the total incremental adjustment requirement, the target reduction angle of each blade stabilization drive is allocated according to the airflow contribution ratio of each airflow stabilization adjustment node corresponding to the blade stabilization drive; wherein, the airflow contribution ratio is the proportion of the influence of a single airflow stabilization adjustment node on the overall airflow field of the fan.

[0022] Preferably, based on the predicted airflow fluctuation probability of each of the airflow stabilization adjustment nodes during the operating cycle, and the target reduction angle of the corresponding blade stabilization drive, the blade stabilization drive is used to predict the surge and noise reduction risk of the corresponding airflow stabilization adjustment node, resulting in a surge and noise reduction risk prediction result, including:

[0023] Based on the predicted airflow fluctuation probability of the airflow stabilization adjustment node in the operating cycle, and the current blade angle of the blade stabilization drive corresponding to the airflow stabilization adjustment node, the second predicted surge noise curve of the blade stabilization drive in the operating cycle is predicted.

[0024] Based on the predicted airflow fluctuation probability of the airflow stabilization adjustment node during the operating cycle, and the target reduction angle of the blade stabilization drive corresponding to the airflow stabilization adjustment node, the third predicted surge noise curve of the blade stabilization drive during the operating cycle is predicted.

[0025] Preferably, based on the predicted airflow fluctuation probability of each of the airflow stabilization adjustment nodes during the operating cycle, and the target reduction angle of the corresponding blade stabilization drive, the blade stabilization drive is used to predict the surge and noise reduction risk of the corresponding airflow stabilization adjustment node, and the surge and noise reduction risk prediction result is obtained. The method further includes:

[0026] Calculate the surge risk difference and noise difference at each time point in the second predicted surge noise curve and the third predicted surge noise curve. Based on the cumulative sum of the surge risk difference and noise difference, determine the insufficient control risk value of the blade stabilization drive for reducing surge and noise at the airflow stabilization adjustment node; wherein, the higher the risk value, the more severe the degree of insufficient control.

[0027] Preferably, based on the cumulative sum of the surge risk difference and the noise difference, the insufficient risk value of the blade stabilization drive for reducing surge and noise at the airflow stabilization adjustment node is determined, including:

[0028] A first risk threshold and a second risk threshold are preset, and the first risk threshold is greater than the second risk threshold;

[0029] If the under-regulation risk value of the blade stabilization drive is greater than the first risk threshold, the difference between the under-regulation risk value and the first risk threshold is calculated, and the difference is multiplied by the preset angle adjustment standard range to obtain the angle compensation amount; based on the angle compensation amount, the target reduction angle of the blade stabilization drive is increased to obtain the adjusted target reduction angle.

[0030] Preferably, determining the insufficient risk value of the blade stabilization drive's regulation of the airflow stabilization adjustment node for surge reduction and noise reduction based on the cumulative sum of the surge risk difference and the noise difference further includes:

[0031] When the risk value of insufficient regulation of the blade stabilization drive is between the second risk threshold and the first risk threshold, the target reduction angle of the blade stabilization drive remains unchanged.

[0032] If the under-regulation risk value of the blade stabilization drive is less than the second risk threshold, the difference between the second risk threshold and the under-regulation risk value is calculated. The difference is multiplied by the preset angle adjustment standard range to obtain the angle reduction amount. Based on the angle reduction amount, the target reduction angle of the blade stabilization drive is reduced to obtain the adjusted target reduction angle.

[0033] Preferably, after controlling the adjustment of the fan blade angle during the operating cycle, the method further includes:

[0034] The actual surge data and actual noise data of the wind turbine are collected during the operating cycle; wherein, the actual surge data includes the surge frequency and surge intensity, and the actual noise data includes the noise decibel values ​​at different time periods;

[0035] The actual surge data and actual noise data are compared with the preset surge control target and noise control target, respectively, and the surge control deviation and noise control deviation are calculated.

[0036] Based on the surge control deviation and noise control deviation, update the judgment thresholds for the airflow-sensitive adjustment node and the airflow-stabilized adjustment node;

[0037] Based on the updated threshold, the blade angle adjustment control for the next operating cycle is executed to achieve iterative optimization of wind turbine surge reduction and low-noise operation.

[0038] A control system for an adjustable blade angle low-surge, low-noise fan, applicable to the aforementioned control method for such a fan, includes:

[0039] The node classification module is used to determine airflow-sensitive adjustment nodes and airflow-stable adjustment nodes based on the current airflow response rate of each blade adjustment unit in the wind turbine. The airflow response rate is the rate of change of airflow field parameters after the blade adjustment unit angle changes. The airflow-sensitive adjustment node is the adjustment unit whose airflow response rate exceeds a preset response threshold, and the airflow-stable adjustment node is the adjustment unit whose airflow response rate is lower than the preset response threshold.

[0040] The first calculation module is used to perform angle increment simulation on the blade drive corresponding to each of the airflow-sensitive adjustment nodes, and to perform angle decrement simulation on the blade stabilization drive corresponding to each of the airflow-stabilization adjustment nodes, so as to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive.

[0041] The risk prediction module is used to predict the risk of surge reduction and noise reduction for the blade stabilization drive to the corresponding airflow stabilization control node based on the predicted airflow fluctuation probability of each of the airflow stabilization control nodes during the operating cycle and the target reduction angle of the blade stabilization drive for each node, and to obtain the risk prediction result of surge reduction and noise reduction.

[0042] The second calculation module is used to adjust the target reduction angle of each blade stabilization drive in response to the risk prediction result of surge reduction and noise reduction, which represents the risk of insufficient control of the blade stabilization drive.

[0043] An angle adjustment module is used to control the wind turbine blade angle adjustment within the operating cycle based on the target incremental angle driven by each blade and the target reduction angle after the stable drive adjustment of each blade.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] (1) This invention determines the airflow sensitive adjustment node and the airflow stable adjustment node according to the airflow response rate of each blade in the fan, which can more accurately control the blade angle, avoid over-adjustment, reduce energy consumption and improve fan efficiency. By predicting the probability of airflow fluctuation and simulating the effects of different angle increments and decrements, the surge phenomenon during fan operation can be effectively reduced. In particular, iterative optimization based on surge risk and noise data can minimize the noise and vibration of the fan, thereby achieving low-noise operation.

[0046] (2) By controlling the blade angle during the initial setting, the present invention can also dynamically adjust the control strategy according to the actual surge data and noise data in the operation cycle, ensuring that the fan can optimize its operating state in each operation cycle. This dynamic feedback mechanism enables the fan to maintain high efficiency and low noise performance in long-term operation. Furthermore, by considering the airflow interference coefficient, the adjustment between each blade drive will not interfere with each other, and the work between each adjustment node can be better coordinated to ensure the balance of the overall airflow field and reduce the instability caused by local over-adjustment. Attached Figure Description

[0047] Figure 1 This is a schematic flowchart of the overall method in one embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the overall system architecture in one embodiment of the present invention.

[0049] In the diagram: 1. Node classification module; 2. First calculation module; 3. Risk prediction module; 4. Second calculation module; 5. Angle adjustment module. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1, please refer to Figure 1This invention provides a technical solution: a control method for a low-noise, surge-reducing fan with adjustable blade angle, comprising:

[0052] S1. Based on the current airflow response rate of each blade adjustment unit in the wind turbine, determine the airflow sensitive adjustment node and the airflow stable adjustment node; the airflow response rate is the rate of change of airflow field parameters after the blade adjustment unit angle changes, the airflow sensitive adjustment node is the adjustment unit whose airflow response rate exceeds the preset response threshold, and the airflow stable adjustment node is the adjustment unit whose airflow response rate is lower than the preset response threshold.

[0053] S2. Perform angle increment simulation for the blade drive corresponding to each airflow-sensitive adjustment node, and perform angle decrement simulation for the blade stabilization drive corresponding to each airflow-stabilization adjustment node, to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive.

[0054] S3. Based on the predicted airflow fluctuation probability of each airflow stabilization adjustment node during the operating cycle, and the target reduction angle of the corresponding blade stabilization drive, the risk prediction of surge reduction and noise reduction of the airflow stabilization adjustment node corresponding to the blade stabilization drive is carried out, and the risk prediction result of surge reduction and noise reduction is obtained.

[0055] S4. In response to the risk prediction results of surge reduction and noise reduction, which characterize the risk of insufficient control of blade stabilization drive, the target reduction angle of each blade stabilization drive is adjusted.

[0056] S5. Based on the target incremental angle driven by each blade and the target reduction angle after stable driving adjustment of each blade, control the adjustment of the wind turbine blade angle within the operating cycle.

[0057] It should be noted that airflow response rate refers to the rate at which airflow field parameters (such as wind speed and air pressure) change after the angle of the fan blade adjustment unit changes. If the airflow changes significantly when the angle of a certain blade is adjusted, then the "airflow response rate" of that blade is high. Airflow-sensitive adjustment nodes: These are adjustment units whose response rate exceeds a certain preset threshold. In other words, when the angle of the fan blade changes, these adjustment units have a significant impact on the airflow, and may be areas of drastic airflow changes, requiring more precise control. Airflow-stable adjustment nodes: These are adjustment units whose response rate is below the threshold. That is, these areas have a smaller impact on airflow changes and may be relatively stable, allowing for relatively simple adjustment strategies. For example, assuming a fan has multiple blades, in some areas, such as the center of the fan, the airflow changes significantly after blade adjustment, therefore these areas belong to "airflow-sensitive adjustment nodes"; while at the edge of the fan, the airflow changes less, and may belong to "airflow-stable adjustment nodes".

[0058] Angle increment simulation: For airflow-sensitive regulating nodes, the fan blade actuator increases its angle to simulate the airflow response of the fan blades after the angle is increased; Angle decrement simulation: For airflow-stabilized regulating nodes, the fan blade actuator decreases its angle to simulate the airflow response of the fan blades after the angle is decreased; Example: For airflow-sensitive regulating nodes, simulate increasing the blade angle by 5 degrees and observe how the airflow changes; while for airflow-stabilized regulating nodes, simulate decreasing the blade angle by 3 degrees and observe the changes in airflow.

[0059] Predicting the probability of airflow fluctuations during the wind turbine's operating cycle, combined with the adjusted blade angles, allows for risk prediction. This helps predict whether the wind turbine will experience surge or generate excessive noise under certain conditions. Risk prediction: Based on the probability of airflow fluctuations and the adjusted blade angles, the risk of surge or high noise levels in the wind turbine is calculated. Example: Suppose it is predicted that the probability of airflow fluctuations is high at certain nodes during wind turbine operation. Then, it is necessary to predict the risk in these areas, whether they may cause surge or excessive noise, thereby affecting the wind turbine's efficiency and noise control.

[0060] Based on the predicted risk results, the angle of the wind turbine blades is adjusted for stable drive. If the risk of insufficient control is predicted at certain nodes, the system will automatically adjust the blade angle to avoid problems. For example, if the risk of airflow fluctuation at a certain airflow stabilization node is predicted to be high, and the blade angle adjustment is insufficient at this time, the system will increase the blade angle adjustment to ensure stable airflow and avoid surge.

[0061] Finally, the system controls the angle adjustment of the wind turbine blades throughout the entire operating cycle based on the target increment angle and the target decrement angle to ensure stable wind turbine operation and achieve the purpose of reducing surge and noise. For example, by simulating the angle increment and decrement of airflow-sensitive and stable nodes, the optimal angle of each blade during wind turbine operation is finally obtained to ensure that the wind turbine can output the best performance with the lowest noise and surge.

[0062] In an optional embodiment, angle increment simulation is performed on the blade drive corresponding to each airflow-sensitive adjustment node, and angle decrement simulation is performed on the blade stabilization drive corresponding to each airflow-stabilization adjustment node, to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive, including:

[0063] Based on the spatial distance between the airflow-sensitive control node and each airflow-stabilizing control node, the airflow interference coefficient between the blade stabilizing drive branch corresponding to each airflow-stabilizing control node and the blade drive corresponding to the airflow-sensitive control node is determined.

[0064] Based on the airflow interference coefficient from each blade stable drive branch to the blade drive, and the predicted airflow change amplitude of the airflow sensitive adjustment node during the operating cycle, the blade drive is simulated in angle increment to obtain the target incremental angle of the blade drive.

[0065] Based on the target increment angle driven by each blade, the target reduction angle of each blade's stable drive branch is determined.

[0066] It should be noted that the airflow interference coefficient refers to the degree to which the wind turbine blades influence each other due to changes in airflow. Because of the airflow between the blades, changes in the angle of some blades may affect the airflow distribution of other blades; therefore, the degree of airflow interference between different blades must be calculated. Spatial distance: The distance between the airflow-sensitive adjustment node and the airflow-stabilizing adjustment node affects the airflow interference coefficient. The farther apart the blades are, the smaller the airflow interference may be; while blades that are closer together may have a greater mutual influence. Example: Suppose wind turbine blades A and B are in different positions. Changes in the angle of blade A may have a greater impact on the airflow-sensitive adjustment node; however, if blades A and B are far apart, then adjusting the angle of blade A will have a smaller impact on blade B. Therefore, by calculating the airflow interference coefficient between these two blades, the impact of adjusting blade A on blade B can be determined.

[0067] Based on the airflow disturbance coefficient and the predicted airflow change amplitude, the blade angle is adjusted. The goal is to ensure that airflow changes at the airflow-sensitive control node can be effectively controlled through precise angle adjustments during wind turbine operation. Airflow change amplitude: Predicting airflow changes is to understand in advance which blades may be significantly affected and which blades require larger angle adjustments. Example: Suppose a large airflow change amplitude is predicted at a certain moment, then the blade drive system related to that node must be simulated with angle increments. For example, increasing the blade angle by 3 degrees can adjust the airflow to mitigate the impact of airflow fluctuations on the wind turbine.

[0068] Blade stabilization drive branch: This refers to the stabilization control system of the wind turbine blades. Its function is to reduce vibration and noise during wind turbine operation. For airflow stabilization adjustment nodes, the stabilization drive system maintains smooth airflow by reducing the blade angle. Target reduction angle: Based on the angle increment of the airflow-sensitive adjustment node and the airflow disturbance coefficient, the required angle reduction is calculated to ensure that the airflow stabilization adjustment node is not excessively disturbed, thereby reducing surge and noise. Example: Suppose that the blade angle needs to be reduced at certain nodes (i.e., reduction simulation) to reduce the impact of airflow fluctuations on the stabilization adjustment node. If a stabilization adjustment node is located relatively close to the airflow-sensitive adjustment node, it may be necessary to reduce the blade angle by 2 degrees to avoid unnecessary airflow disturbance.

[0069] Airflow interference coefficient calculation: Calculate the airflow interference coefficient based on the distance between blades to assess the impact of blade adjustments on other blades; Incremental simulation: Perform incremental angle simulations of blades at airflow-sensitive control nodes based on the airflow interference coefficient and airflow change predictions; Reduction angle determination: Calculate the target reduction angle of the blades at the stable control node based on the incremental adjustments of the sensitive node and the requirements of the stable node; Final control: Adjust the blade angle based on the simulation results to achieve airflow balance, reduce surge, and reduce noise.

[0070] Practical Application Example: Assume a wind turbine has 5 blades. Blades A, B, and C are located in the core area of ​​the turbine and are significantly affected by airflow changes, making them airflow-sensitive control nodes. Blades D and E are located at the edge of the turbine and experience less airflow change, making them airflow-stable control nodes. Airflow Interference Coefficient: If the distance between blades A and D is large, their airflow interference coefficient is small, and adjusting blade A has little impact on blade D. Conversely, if the distance between blades B and C is small, their airflow interference coefficient is large. Angle Increment Simulation: For blades A and B at airflow-sensitive nodes, it is assumed that their angle needs to be increased by 5 degrees to cope with drastic airflow changes. For blades D and E at stable nodes, their angle may need to be reduced by 3 degrees to reduce airflow fluctuations. Target Reduction Angle: Based on the above simulation, the target reduction angle for blades D and E is finally determined to ensure that the airflow at stable nodes remains stable and to avoid excessive interference.

[0071] In an optional embodiment, based on the airflow interference coefficient from each blade stable drive branch to the blade drive, and the predicted airflow change amplitude of the airflow-sensitive adjustment node during the operating cycle, an angle increment simulation of the blade drive is performed to obtain the target increment angle of the blade drive, including:

[0072] Based on the current blade angle driven by the blade corresponding to the airflow-sensitive control node, and the predicted airflow change amplitude of the airflow-sensitive control node during the operating cycle, the first predicted surge noise curve of the airflow-sensitive control node during the operating cycle is determined; wherein, the first predicted surge noise curve includes the surge risk value and noise decibel value at different time points;

[0073] For each candidate incremental angle, the blade drive is controlled to run at the candidate incremental angle, and the airflow sensitive adjustment node is controlled to change the airflow state according to the predicted airflow change amplitude within the corresponding duration of the running cycle. The effect of the adjustment of each blade stable drive on the blade drive under its corresponding airflow disturbance coefficient is simulated, and the simulated surge noise curve of the airflow sensitive adjustment node within the corresponding duration of the running cycle is obtained.

[0074] Based on the deviation between the first predicted surge noise curve and the simulated surge noise curve corresponding to each candidate incremental angle, the candidate incremental angle that minimizes the deviation is determined as the target incremental angle for blade drive among all candidate incremental angles.

[0075] It should be noted that the current blade angle corresponding to the airflow-sensitive control node refers to the current angle of the turbine blades at the airflow-sensitive control node, which is usually closely related to the turbine's operating status and airflow environment; the predicted airflow change amplitude is a prediction of airflow changes, such as possible changes in wind speed, airflow direction, and other factors in the future; this prediction helps to understand how future airflow will affect the blade angle and operating status; the first predicted surge noise curve is generated based on the current blade angle and the predicted airflow change amplitude. This curve represents the change in surge risk and noise at different time points. Surge risk value: refers to the surge (i.e., high-frequency vibration) risk caused by airflow fluctuations during wind turbine operation, which is usually closely related to the wind turbine's operating conditions and blade angle; Noise decibel value: represents the noise level of the wind turbine at different points in time, which is usually related to blade angle, airflow fluctuations, and vibration; Example: Suppose a wind turbine is currently running with blade A angle of 15°, and the airflow change amplitude prediction shows that the airflow fluctuation will be large in the next 10 minutes; Based on this information, the generated first predicted surge noise curve shows that at some points in the future, the surge risk of the wind turbine may be high, and the noise may reach more than 80 decibels;

[0076] Candidate incremental angles: These refer to the different angle adjustment options provided for blade drive, usually obtained through simulation or empirical calculation. Each candidate incremental angle represents a possible adjustment scheme aimed at reducing surge noise. Blade drive operation at candidate incremental angles: Each candidate angle is applied to the blade drive system to simulate its operation and observe the impact of airflow changes on the blades. Airflow state changes: The airflow-sensitive adjustment node is controlled to change according to the predicted airflow change amplitude to simulate the actual impact of airflow on the blades. This yields a simulated surge noise curve for evaluating the effect of each candidate incremental angle. Example: Suppose there are three candidate incremental angles (5°, 10°, and 15°), which are applied to the blade drive system to simulate airflow changes and surge noise during the wind turbine's operating cycle. Assume that during the simulation, a 5° incremental angle can make the airflow smoother and reduce surge noise, while 10° and 15° incremental angles may increase airflow fluctuations, leading to higher noise.

[0077] Simulated surge noise curve: This is the simulation result obtained after applying a certain candidate incremental angle, reflecting the impact of airflow changes on surge noise after the angle adjustment; Deviation: This refers to the difference between the first predicted surge noise curve and the simulated surge noise curve; the goal is for the simulation results to be as close as possible to the prediction results to ensure that the surge noise can be controlled more accurately during actual operation of the fan; Example: Simulations were performed for three candidate incremental angles (5°, 10°, 15°), and the simulated surge noise curves obtained were as follows: For the 5° incremental angle, the simulated surge noise curve is very close to the predicted curve, with a small deviation; For the 10° and 15° incremental angles, the deviation between the simulated surge noise curve and the predicted curve is large, and the noise increases significantly at certain times;

[0078] By comparing the deviation between the simulation results of each candidate incremental angle and the first predicted surge noise curve, the angle that minimizes the deviation is selected as the target incremental angle for blade drive. This is to ensure that the wind turbine can minimize surge noise during actual operation. For example, assuming that in the above example, the simulated surge noise curve generated by the incremental angle of 5° is closest to the first predicted curve, 5° is selected as the target incremental angle for blade drive. In this way, the wind turbine can effectively control surge noise during operation, ensuring smooth and quiet operation.

[0079] In an optional embodiment, the target reduction angle of each blade's stable drive branch is determined based on the target increment angle of each blade drive, including:

[0080] Based on the target incremental angle driven by each blade, and combined with the overall airflow field balance coefficient of the fan, the total incremental adjustment demand is calculated; where the airflow field balance coefficient is the weight parameter of the mutual influence of airflow at each adjustment node of the fan.

[0081] Based on the total incremental adjustment demand, the target reduction angle of each blade's stable drive is allocated according to the airflow contribution ratio of the airflow stable adjustment node corresponding to each blade's stable drive; where the airflow contribution ratio is the proportion of the influence of a single airflow stable adjustment node on the overall airflow field of the wind turbine.

[0082] It should be noted that the target incremental angle refers to the angle change set for each blade drive system, used to adjust the angle of the wind turbine blades to meet the needs of wind speed or other airflow changes; the target incremental angle of each blade is obtained through calculation or simulation; the airflow field balance coefficient is a weighting parameter used to represent the mutual influence of airflow between different regulating nodes of the wind turbine (such as individual blades, regulators, etc.); each regulating node has a different impact on the overall airflow field, some nodes have a larger impact on the airflow, and some have a smaller impact on the airflow; this coefficient helps to quantify this mutual influence so that the airflow regulation needs can be reasonably allocated when adjusting the blade angle; Example: Suppose a wind turbine has four blades, A, B, C, and D, each with a target increment angle; for example, the target increment angle for blade A is 5°, for blade B it is 3°, for blade C it is 2°, and for blade D it is 4°. These target increment angles represent the angle adjustment amount of each blade under specific conditions. Suppose the airflow balance coefficients are: 0.4 for blade A, 0.3 for blade B, 0.2 for blade C, and 0.1 for blade D. This coefficient indicates that the airflow influence of blade A is greater than that of other blades, so the adjustment of blade A contributes more to the overall airflow balance.

[0083] Based on the target incremental angle driven by each blade and the airflow field balance coefficient, the total incremental regulation requirement of the entire fan can be calculated. This requirement is the total airflow regulation required by the fan under a specific operating condition to ensure airflow stability and reduce unnecessary fluctuations. The calculation formula is: Total incremental regulation requirement = (Target incremental angle A × Airflow field balance coefficient A) + (Target incremental angle B × Airflow field balance coefficient B). For example: Suppose the total incremental adjustment demand is calculated as follows: Total incremental adjustment demand = (5° × 0.4) + (3° × 0.3) + (2° × 0.2) + (4° × 0.1) = 3.7°. This total incremental adjustment demand means that in order to maintain the stability of the airflow, all the blades of the fan need to be adjusted by a total of 3.7°.

[0084] Airflow contribution percentage: This represents the proportion of influence of each airflow stabilization adjustment node (i.e., each blade) on the overall airflow field of the fan. For example, some blades contribute more to the airflow, and their adjustment has a more significant impact on the airflow, while other blades contribute less. Target reduction angle allocation: Based on the airflow contribution percentage of each blade, the total incremental adjustment requirement is allocated to each blade. This is because different blades have different airflow contributions, and the reduction angle needs to be adjusted according to the influence of each blade on the overall airflow. Allocation formula: Target reduction angle = Total incremental adjustment requirement × Airflow contribution percentage. Example: Assume the airflow contribution percentages of each blade are as follows: Blade A's airflow contribution percentage is 0.5, Blade B's airflow contribution percentage is... The airflow contribution ratio of blade A is 0.3, blade C's airflow contribution ratio is 0.15, and blade D's airflow contribution ratio is 0.05. Therefore, the target reduction angle allocation is calculated as follows: Target reduction angle of blade A: 3.7° × 0.5 = 1.85°, Target reduction angle of blade B: 3.7° × 0.3 = 1.11°, Target reduction angle of blade C: 3.7° × 0.15 = 0.56°, Target reduction angle of blade D: 3.7° × 0.05 = 0.19°. The result is that during the fan adjustment process, blade A needs to be reduced by 1.85°, blade B by 1.11°, blade C by 0.56°, and blade D by 0.19°.

[0085] In an optional embodiment, based on the predicted airflow fluctuation probability of each airflow stabilization node during its operating cycle and the target reduction angle of its corresponding blade stabilization drive, a surge reduction and noise reduction risk prediction is performed on the blade stabilization drive for the corresponding airflow stabilization node, resulting in a surge reduction and noise reduction risk prediction result, including:

[0086] Based on the predicted airflow fluctuation probability of the airflow stabilization node during the operating cycle, and the current blade angle of the blade stabilization drive corresponding to the airflow stabilization node, the second predicted surge noise curve of the blade stabilization drive during the operating cycle is predicted.

[0087] Based on the predicted airflow fluctuation probability of the airflow stabilization node during the operating cycle, and the target reduction angle of the blade stabilization drive corresponding to the airflow stabilization node, the third predicted surge noise curve of the blade stabilization drive during the operating cycle is predicted.

[0088] It should be noted that the surge noise curve for the entire operating cycle is predicted based on the predicted airflow fluctuation probability of the airflow stabilization adjustment node and the current blade angle of the corresponding blade stabilization drive. The airflow fluctuation probability of each airflow stabilization adjustment node represents the trend of airflow change, and airflow fluctuation may cause the blade to surge. The current blade angle affects the airflow state, thus affecting the magnitude of surge noise. Blades with different angles will cause different disturbances to the airflow, thereby affecting the frequency and intensity of surge. For example, assuming that the angle of blade A at the current moment is 10°, the airflow fluctuation probability of the airflow stabilization adjustment node is 0.3, which means that the airflow instability is relatively high. Through model prediction, the surge noise of blade A in the current state will fluctuate within a specific frequency range. This fluctuation forms the second predicted surge noise curve, which represents the change of the surge noise of the blade in the current operating state over time.

[0089] Based on the same airflow fluctuation probability and the target reduction angle of the blades, the surge noise curve that the blades may generate after the angle is adjusted is predicted. The target reduction angle of each blade represents the new angle of the blade after adjustment. The new angle will produce different disturbances to the airflow, thereby changing the frequency and amplitude of the surge noise. By adjusting the blade angle, the airflow fluctuation may be suppressed or enhanced, thus affecting the magnitude of the surge noise. For example, assuming that the target reduction angle of blade A is 2°, it means that the blade will reduce its angle to reduce the impact on the airflow. Assuming that the airflow fluctuation probability is still 0.3, after the blade angle is adjusted, the surge noise is predicted to decrease, forming the third predicted surge noise curve. By adjusting the angle, the disturbance of the blade to the airflow is reduced, and the amplitude of the surge noise fluctuation is reduced.

[0090] The surge reduction effect after blade angle adjustment is evaluated by comparing the changes in the second and third predicted surge noise curves. Surge risk difference: This represents the change in surge risk before and after angle adjustment. By calculating the surge risk difference at each time point, we can understand whether the surge risk has been effectively reduced after angle adjustment. Noise difference: This represents the difference in noise change before and after adjustment. The larger the noise difference, the more significant the noise reduction effect. Example: Suppose at a certain time point, the second predicted surge noise curve shows a surge risk of 0.6, while the third predicted surge noise curve shows a surge risk of 0.3 after adjustment. Then, the surge risk difference is 0.6 - 0.3 = 0.3, meaning that the adjusted angle effectively reduced the surge risk. Similarly, suppose the noise value of the second predicted surge noise curve is 80 dB, and the noise value of the third predicted surge noise curve is 60 dB, the noise difference is 20 dB, indicating a noise reduction of 20 dB.

[0091] In an optional embodiment, based on the predicted airflow fluctuation probability of each airflow stabilization node during its operating cycle and the target reduction angle of its corresponding blade stabilization drive, the risk prediction of surge reduction and noise reduction for the blade stabilization drive on the corresponding airflow stabilization node is performed to obtain the surge reduction and noise reduction risk prediction result, and the method further includes:

[0092] The difference in surge risk and noise at each time point between the second and third predicted surge noise curves are calculated. Based on the cumulative sum of the difference in surge risk and noise, the risk value of insufficient control of the blade stabilization drive on the airflow stabilization adjustment node for surge reduction and noise reduction is determined. The higher the risk value, the more severe the degree of insufficient control.

[0093] It should be noted that the cumulative difference between the second and third predicted surge noise curves is used to determine the risk of insufficient regulation. Cumulative risk calculation: By calculating the cumulative sum of the surge risk difference and noise difference across all time points, a total "insufficient regulation risk value" is obtained. Meaning of the risk value: A high insufficient regulation risk value indicates that the effect of adjusting the blade angle on surge and noise control is not significant, and the stability of the wind turbine remains poor. Conversely, a lower risk value indicates a better regulation effect, and the blade angle adjustment can effectively reduce surge and noise. Example: Assume the surge risk difference and noise difference at multiple time points are as follows: Time point 1: Surge risk difference is 0.2, noise difference is 10 dB; Time point 2: Surge risk difference is 0.3, noise difference is 15 dB; Time point 3: Surge risk difference is 0.1, noise difference is 5 dB. Calculate the cumulative values: Total surge risk difference cumulative: 0.2 + 0.3 + 0.1 = 0.6; Total noise difference cumulative: 10 + 15 + 5 = 30 dB. Assuming based on these cumulative values, an under-regulation risk value of 0.8 is obtained. This risk value indicates that the wind turbine still experiences some degree of surge and noise issues under the current angle adjustment. A lower risk value (e.g., 0.2) indicates better regulation and lower risk.

[0094] In an optional embodiment, based on the cumulative sum of the surge risk difference and the noise difference, the insufficient control risk value of the blade stabilization drive for reducing surge and noise at the airflow stabilization adjustment node is determined, including:

[0095] A first risk threshold and a second risk threshold are preset, and the first risk threshold is greater than the second risk threshold;

[0096] When the risk value of insufficient control in blade stabilization drive exceeds the first risk threshold, the difference between the risk value of insufficient control and the first risk threshold is calculated. The difference is multiplied by the preset angle adjustment standard range to obtain the angle compensation amount. Based on the angle compensation amount, the target reduction angle of blade stabilization drive is increased to obtain the adjusted target reduction angle.

[0097] It should be noted that two risk thresholds are preset: the first risk threshold is a higher risk threshold, and when the risk value of insufficient regulation exceeds this value, a larger angle adjustment is required; the second risk threshold is a lower risk threshold, and when the risk value of insufficient regulation is lower than this value, the angle adjustment will be reduced; and the first risk threshold is greater than the second risk threshold.

[0098] In an optional embodiment, determining the insufficient risk value of blade stabilization drive for reducing surge and noise at the airflow stabilization adjustment node based on the cumulative sum of the surge risk difference and the noise difference further includes:

[0099] When the risk value of insufficient regulation of blade stabilization drive is between the second risk threshold and the first risk threshold, the target reduction angle of blade stabilization drive remains unchanged.

[0100] If the risk value of insufficient control in blade stabilization drive is less than the second risk threshold, the difference between the second risk threshold and the risk value of insufficient control is calculated. The difference is multiplied by the preset angle adjustment standard range to obtain the angle reduction amount. Based on the angle reduction amount, the target reduction angle of blade stabilization drive is reduced to obtain the adjusted target reduction angle.

[0101] It should be noted that, based on the under-control risk value of the blade's stable drive system, the system will take different adjustment measures according to the following conditions: When the under-control risk value is greater than the first risk threshold: In this case, the under-control risk value is high, meaning that the current blade angle has not effectively reduced surge and noise; calculate the difference between the under-control risk value and the first risk threshold; multiply the difference by the preset angle adjustment standard range to obtain an angle compensation amount; use this angle compensation amount to increase the target reduction angle (i.e., adjust the blade angle to make it more tilted, so as to more effectively reduce surge and noise); Example: Under-control risk value = 0.8, first risk threshold = 0.7, preset angle adjustment standard range = 5°, difference between under-control risk value and first risk threshold = 0.8 - 0.7 = 0.1, angle compensation amount = 0.1 × 5° = 0.5°, therefore, the target reduction angle of the blade needs to be increased by 0.5° to more effectively regulate the airflow and reduce surge;

[0102] When the under-control risk value is between the second risk threshold and the first risk threshold: In this case, the under-control risk value is moderate, and the blade angle does not need to be adjusted significantly; the target reduction angle for stable blade drive remains unchanged, and no angle adjustment is made; Example: under-control risk value = 0.65, second risk threshold = 0.6, first risk threshold = 0.7. At this time, the under-control risk value is between the second and first risk thresholds, so the system decides to keep the target reduction angle unchanged;

[0103] When the under-adjustment risk value is less than the second risk threshold: In this case, the under-adjustment risk value is low, meaning the blade angle is too tilted, which may have effectively reduced surge and noise. Calculate the difference between the second risk threshold and the under-adjustment risk value. Multiply the difference by the preset angle adjustment standard range to obtain an angle reduction amount. Use this angle reduction amount to reduce the target reduction angle (i.e., adjust the blade angle to slightly restore it to a flatter position, thereby avoiding excessive tilting that could cause other problems). Example: Under-adjustment risk value = 0.4, second risk threshold = 0.6, preset angle adjustment standard range = 5°, difference between the second risk threshold and the under-adjustment risk value = 0.6 - 0.4 = 0.2, angle reduction amount = 0.2 × 5° = 1°. Therefore, the target reduction angle of the blade needs to be reduced by 1° to avoid over-adjustment causing airflow instability.

[0104] In an optional embodiment, after controlling the adjustment of the fan blade angle during the operating cycle, the method further includes:

[0105] Collect actual surge data and actual noise data of the wind turbine during the operating cycle; the actual surge data includes the surge frequency and surge intensity, and the actual noise data includes the noise decibel values ​​at different time periods;

[0106] The actual surge data and actual noise data are compared with the preset surge control target and noise control target, respectively, and the surge control deviation and noise control deviation are calculated.

[0107] Based on surge control deviation and noise control deviation, update the judgment thresholds for airflow-sensitive regulation nodes and airflow-stabilized regulation nodes;

[0108] Based on the updated threshold, the blade angle adjustment control for the next operating cycle is executed to achieve iterative optimization of wind turbine surge reduction and low-noise operation.

[0109] It should be noted that, based on surge control deviation and noise control deviation, the system needs to update the judgment thresholds of the airflow-sensitive adjustment node and the airflow-stabilizing adjustment node. These thresholds determine the adjustment range of the blade angle, thereby optimizing the fan's operating performance. A large surge control deviation indicates that the current blade angle may not be sufficiently reducing surge, requiring an increase in the adjustment amplitude. A large noise control deviation may necessitate further adjustment of the blade angle to reduce noise. For example, suppose the update rules are as follows: when the surge frequency deviation exceeds 10 cycles, the threshold of the airflow-stabilizing adjustment node needs to be reduced by 5% for more precise angle adjustment; when the noise deviation exceeds 5 dB, the threshold of the airflow-sensitive adjustment node needs to be increased by 5% to reduce noise. Therefore, the surge frequency deviation is 10 cycles, exceeding the 10-cycle threshold, thus requiring adjustment of the airflow-stabilizing adjustment node threshold; the noise deviation at noon is 5 dB, exceeding... If the threshold of 5dB is exceeded, the airflow-sensitive adjustment node threshold needs to be adjusted. With the updated threshold, the system will execute blade angle adjustment control for the next operating cycle. This means that in the new operating cycle, the turbine blade angle adjustment will be optimized based on the updated threshold to achieve reduced surge and lower noise operation. For example, based on the new threshold adjustment: assuming the airflow stability adjustment node threshold is reduced by 5%, the blade angle adjustment in the next cycle will be more sensitive to reduce surge; if the airflow-sensitive adjustment node threshold is increased by 5%, a larger adjustment range will be used in the next cycle to reduce noise. This process is the core of iterative optimization. Each operating cycle adjusts the blade angle based on the latest actual data, control deviations, and updated thresholds. In this way, through continuous monitoring, comparison, and adjustment, the turbine can gradually optimize its operating performance, achieving better surge reduction and lower noise effects.

[0110] Example 2, please refer to Figure 2 This invention provides a technical solution: a control system for an adjustable blade angle low-surge, low-noise fan, applicable to the aforementioned control method for such a fan, comprising:

[0111] Node classification module 1 is used to determine airflow-sensitive adjustment nodes and airflow-stable adjustment nodes based on the current airflow response rate of each blade adjustment unit in the wind turbine. The airflow response rate is the rate of change of airflow field parameters after the blade adjustment unit angle changes. The airflow-sensitive adjustment node is the adjustment unit whose airflow response rate exceeds the preset response threshold, and the airflow-stable adjustment node is the adjustment unit whose airflow response rate is lower than the preset response threshold.

[0112] The first calculation module 2 is used to perform angle increment simulation of the blade drive corresponding to each airflow sensitive adjustment node, and to perform angle decrement simulation of the blade stabilization drive corresponding to each airflow stabilization adjustment node, so as to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive.

[0113] Risk prediction module 3 is used to predict the risk of surge reduction and noise reduction for the blade stabilization drive on the corresponding airflow stabilization control node based on the predicted airflow fluctuation probability of each airflow stabilization control node during the operating cycle and the target reduction angle of each corresponding blade stabilization drive, and obtain the risk prediction result of surge reduction and noise reduction.

[0114] The second calculation module 4 is used to adjust the target reduction angle of each blade's stabilization drive in response to the risk prediction results of surge reduction and noise reduction, which characterize the insufficient control risk of blade stabilization drive.

[0115] Angle adjustment module 5 is used to control the wind turbine blade angle adjustment within the operating cycle based on the target incremental angle driven by each blade and the target reduction angle after stable driving adjustment of each blade.

[0116] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A control method for a low-noise, surge-reducing fan with adjustable blade angle, characterized in that, include: Based on the current airflow response rate of each blade adjustment unit in the wind turbine, airflow sensitive adjustment nodes and airflow stable adjustment nodes are determined; the airflow response rate is the rate of change of airflow field parameters after the blade adjustment unit angle changes, the airflow sensitive adjustment node is the adjustment unit whose airflow response rate exceeds a preset response threshold, and the airflow stable adjustment node is the adjustment unit whose airflow response rate is lower than the preset response threshold. An angle increment simulation is performed on the blade drive corresponding to each of the airflow-sensitive adjustment nodes, and an angle decrement simulation is performed on the blade stabilization drive corresponding to each of the airflow-stabilization adjustment nodes, to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive. Based on the predicted airflow fluctuation probability of each airflow stabilization adjustment node during the operating cycle, and the target reduction angle of the corresponding blade stabilization drive, the risk prediction of surge reduction and noise reduction of the airflow stabilization adjustment node corresponding to the blade stabilization drive is performed to obtain the risk prediction result of surge reduction and noise reduction. In response to the risk prediction results of surge reduction and noise reduction, which characterize the risk of insufficient regulation of the blade stabilization drive, the target reduction angle of each blade stabilization drive is adjusted. Based on the target incremental angle driven by each blade and the target reduction angle after the stable drive adjustment of each blade, the wind turbine blade angle is controlled to adjust within the operating cycle.

2. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 1, characterized in that, An angle increment simulation is performed on the blade drive corresponding to each of the airflow-sensitive adjustment nodes, and an angle decrement simulation is performed on the blade stabilization drive corresponding to each of the airflow-stabilization adjustment nodes, to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive, including: Based on the spatial distance between the airflow-sensitive adjustment node and each of the airflow-stabilizing adjustment nodes, the airflow interference coefficient between the blade stabilizing drive branch corresponding to each of the airflow-stabilizing adjustment nodes and the blade drive corresponding to the airflow-sensitive adjustment node is determined respectively. Based on the airflow interference coefficient from each of the blade stable drive branches to the blade drive, and the predicted airflow change amplitude of the airflow sensitive adjustment node in the operating cycle, the blade drive is simulated in angle increment to obtain the target incremental angle of the blade drive. Based on the target incremental angle of each blade drive, the target reduction angle of each blade stable drive branch is determined.

3. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 2, characterized in that, Based on the airflow interference coefficient from each of the blade stable drive branches to the blade drive, and the predicted airflow change amplitude of the airflow sensitive adjustment node during the operating cycle, the blade drive is simulated in angle increment to obtain the target increment angle of the blade drive, including: Based on the current blade angle driven by the blade corresponding to the airflow-sensitive adjustment node, and the predicted airflow change amplitude of the airflow-sensitive adjustment node in the operating cycle, a first predicted surge noise curve of the airflow-sensitive adjustment node in the operating cycle is determined; wherein, the first predicted surge noise curve includes surge risk value and noise decibel value at different time points; For each candidate incremental angle, the blade drive is controlled to operate at the candidate incremental angle, and the airflow-sensitive adjustment node is controlled to change the airflow state according to the predicted airflow change amplitude within the duration corresponding to the operating cycle. The effect of the adjustment of each blade stable drive on the blade drive under its corresponding airflow disturbance coefficient is simulated, and the simulated surge noise curve of the airflow-sensitive adjustment node within the duration corresponding to the operating cycle is obtained. Based on the deviation between the first predicted surge noise curve and the simulated surge noise curve corresponding to each of the candidate incremental angles, the candidate incremental angle that minimizes the deviation is determined as the target incremental angle for the blade drive among the candidate incremental angles.

4. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 3, characterized in that, Based on the target incremental angle of each blade drive, the target reduction angle of each blade stable drive branch is determined, including: Based on the target incremental angle driven by each blade, and combined with the overall airflow field balance coefficient of the fan, the total incremental adjustment demand is calculated; wherein, the airflow field balance coefficient is a weighting parameter of the mutual influence of airflow at each adjustment node of the fan. Based on the total incremental adjustment requirement, the target reduction angle of each blade stabilization drive is allocated according to the airflow contribution ratio of each airflow stabilization adjustment node corresponding to the blade stabilization drive; wherein, the airflow contribution ratio is the proportion of the influence of a single airflow stabilization adjustment node on the overall airflow field of the fan.

5. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 4, characterized in that, Based on the predicted airflow fluctuation probability of each of the aforementioned airflow stabilization adjustment nodes during its operating cycle, and the target reduction angle of the corresponding blade stabilization drive, a surge reduction and noise reduction risk prediction is performed on the airflow stabilization adjustment node corresponding to the blade stabilization drive, resulting in surge reduction and noise reduction risk prediction results, including: Based on the predicted airflow fluctuation probability of the airflow stabilization adjustment node in the operating cycle, and the current blade angle of the blade stabilization drive corresponding to the airflow stabilization adjustment node, the second predicted surge noise curve of the blade stabilization drive in the operating cycle is predicted. Based on the predicted airflow fluctuation probability of the airflow stabilization adjustment node during the operating cycle, and the target reduction angle of the blade stabilization drive corresponding to the airflow stabilization adjustment node, the third predicted surge noise curve of the blade stabilization drive during the operating cycle is predicted.

6. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 5, characterized in that, Based on the predicted airflow fluctuation probability of each of the airflow stabilization adjustment nodes during its operating cycle, and the target reduction angle of the corresponding blade stabilization drive, the risk prediction for surge reduction and noise reduction of the airflow stabilization adjustment node corresponding to the blade stabilization drive is performed to obtain the surge reduction and noise reduction risk prediction result, which also includes: Calculate the surge risk difference and noise difference at each time point in the second predicted surge noise curve and the third predicted surge noise curve. Based on the cumulative sum of the surge risk difference and noise difference, determine the insufficient control risk value of the blade stabilization drive for reducing surge and noise at the airflow stabilization adjustment node; wherein, the higher the risk value, the more severe the degree of insufficient control.

7. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 6, characterized in that, Based on the cumulative sum of the surge risk difference and the noise difference, the insufficient control risk value of the blade stabilization drive for the airflow stabilization adjustment node to reduce surge and noise is determined, including: A first risk threshold and a second risk threshold are preset, and the first risk threshold is greater than the second risk threshold; If the under-regulation risk value of the blade stabilization drive is greater than the first risk threshold, the difference between the under-regulation risk value and the first risk threshold is calculated, and the difference is multiplied by the preset angle adjustment standard range to obtain the angle compensation amount; based on the angle compensation amount, the target reduction angle of the blade stabilization drive is increased to obtain the adjusted target reduction angle.

8. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 7, characterized in that, Based on the cumulative sum of the surge risk difference and the noise difference, the insufficient risk value of the blade stabilization drive in reducing surge and noise at the airflow stabilization adjustment node is determined, further including: When the risk value of insufficient regulation of the blade stabilization drive is between the second risk threshold and the first risk threshold, the target reduction angle of the blade stabilization drive remains unchanged. If the under-regulation risk value of the blade stabilization drive is less than the second risk threshold, the difference between the second risk threshold and the under-regulation risk value is calculated. The difference is multiplied by the preset angle adjustment standard range to obtain the angle reduction amount. Based on the angle reduction amount, the target reduction angle of the blade stabilization drive is reduced to obtain the adjusted target reduction angle.

9. The control method for an adjustable blade angle low-surge, low-noise fan according to claim 8, characterized in that, After controlling the adjustment of the fan blade angle during the operating cycle, the method further includes: The actual surge data and actual noise data of the wind turbine are collected during the operating cycle; wherein, the actual surge data includes the surge frequency and surge intensity, and the actual noise data includes the noise decibel values ​​at different time periods; The actual surge data and actual noise data are compared with the preset surge control target and noise control target, respectively, and the surge control deviation and noise control deviation are calculated. Based on the surge control deviation and noise control deviation, update the judgment thresholds for the airflow sensitive adjustment node and the airflow stable adjustment node; Based on the updated threshold, the blade angle adjustment control for the next operating cycle is executed to achieve iterative optimization of wind turbine surge reduction and low-noise operation.

10. A control system for a low-noise, surge-reducing fan with adjustable blade angle, applicable to the control method for a low-noise, surge-reducing fan with adjustable blade angle as described in any one of claims 1-9, characterized in that... include: The node classification module is used to determine airflow-sensitive adjustment nodes and airflow-stable adjustment nodes based on the current airflow response rate of each blade adjustment unit in the wind turbine. The airflow response rate is the rate of change of airflow field parameters after the blade adjustment unit angle changes. The airflow-sensitive adjustment node is the adjustment unit whose airflow response rate exceeds a preset response threshold, and the airflow-stable adjustment node is the adjustment unit whose airflow response rate is lower than the preset response threshold. The first calculation module is used to perform angle increment simulation on the blade drive corresponding to each of the airflow-sensitive adjustment nodes, and to perform angle decrement simulation on the blade stabilization drive corresponding to each of the airflow-stabilization adjustment nodes, so as to obtain the target increment angle of each blade drive and the target decrement angle of each blade stabilization drive. The risk prediction module is used to predict the risk of surge reduction and noise reduction for the airflow stabilization adjustment node corresponding to the blade stabilization drive based on the predicted airflow fluctuation probability of each of the airflow stabilization adjustment nodes in the operating cycle and the target reduction angle of the blade stabilization drive corresponding to each node, and to obtain the risk prediction result of surge reduction and noise reduction. The second calculation module is used to adjust the target reduction angle of each blade stabilization drive in response to the risk prediction result of surge reduction and noise reduction, which represents the risk of insufficient control of the blade stabilization drive. An angle adjustment module is used to control the wind turbine blade angle adjustment within the operating cycle based on the target incremental angle driven by each blade and the target reduction angle after the stable drive adjustment of each blade.

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