Intelligent control system and method for wind generating set, medium and equipment

Abstract

The invention discloses an intelligent control system and method for a wind generating set, a medium and equipment, and the system comprises a wind measurement module which is used for obtaining wind speed and wind direction data and transmitting the data to a main control module; the running state monitoring module is used for recording running state data of the wind generating set and transmitting the running state data to the main control module; the main control module is used for learning the input running state data through a neural network algorithm and a deep learning model, calculating optimal control parameters in combination with the wind speed and wind direction data and preset design data, and controlling the wind generating set to perform independent variable pitch action according to the optimal control parameters; the stall state and the clearance state of the wind generating set during operation are adjusted; according to the method, the running state data of the wind generating set is adopted as input, learning judgment is carried out through the neural network algorithm and the deep learning model, the control parameters during running of the wind generating set are changed, additional sensors do not need to be added, and the economical efficiency of the whole machine is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of stall and clearance control of wind turbine generator sets, and in particular to an intelligent control system, method, medium and equipment for wind turbine generator sets. Background Art

[0002] A wind turbine is a power machine that captures wind energy through a wind rotor. The wind energy drives the wind rotor to rotate and drives the generator to work through the transmission system to generate electricity. In order to capture more wind energy and reduce the cost of electricity, the large-scale and lightweight wind turbines have become the only way for the development of wind power technology. Due to the demand for large-scale and lightweight wind turbines, the lightweight and increased height of the tower, the lightweight and increased length of the blades will reduce the stiffness, and the corresponding deformation will increase, increasing the risk of stalling of the wind turbine and blade sweeping the tower. There is no good solution to the stall problem of wind turbines in this field. The spread of the stall problem will affect the power generation and safety of the unit.

[0003] The main solutions to the problem of wind turbine blade tower scanning in the existing technology are: 1) Laser rangefinder for clearance distance measurement, which emits a laser beam in the cabin and measures the blade distance by detecting the echo. The laser rangefinder has the advantages of simple results, low cost, strong anti-interference ability, and is suitable for outdoor operations. However, the measurement performance is greatly affected by weather and environment, and the measurement performance is reduced in rain, fog, and dust environments; 2) The clearance measurement method based on image ranging, which calculates the clearance distance by taking pictures of blade deformation, has high requirements on camera performance and image processing algorithm, and the measurement performance is reduced at night and in low visibility environments. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and propose an intelligent control system, method, medium and equipment for a wind turbine. The system can calculate the operating status data of the wind turbine, control the wind turbine to perform independent pitch change actions according to the calculation results, and adjust the stall state and clearance state of the wind turbine during operation. It can effectively solve the problems existing in the above-mentioned current technology and provide convenience for more users.

[0005] The purpose of the present invention is achieved through the following technical solutions: A wind turbine intelligent control system, comprising:

[0006] The wind measurement module is installed at the center of the hub of the wind turbine generator set to obtain wind speed and direction data and transmit them to the main control module;

[0007] An operation status monitoring module is used to record the operation status data of the wind turbine generator set and transmit the operation status data to the main control module;

[0008] The main control module learns the input operating status data through neural network algorithms and deep learning models, calculates the optimal control parameters based on wind speed and direction data and preset design data, controls the wind turbine to perform independent pitch control actions according to the optimal control parameters, and adjusts the stall state and clearance state of the wind turbine during operation.

[0009] Furthermore, the main control module includes an operation status analysis module and an operation status control module.

[0010] Furthermore, the operation status analysis module includes:

[0011] The input operation status data is learned through neural network algorithms and deep learning models, including: learning and judging the stall state of wind speed-power, learning and judging the clearance state of wind speed-load, and learning and judging the clearance state of wind shear-independent pitch control;

[0012] The stall state learning judgment of wind speed-power inputs the operating state data and the wind speed-power designed by the wind turbine generator set into the deep learning model, obtains an importance value through parameter comparison training and learning to determine the control instruction of the pitch action, combines other control instructions of the wind turbine generator set for optimization processing, and outputs the operating state control instruction of the wind turbine generator set;

[0013] The wind speed-load clearance state learning judgment inputs the operating state data and the wind speed-load designed by the wind turbine generator set into the deep learning model, obtains an importance value through parameter comparison training and learning to determine the control instruction of the pitch action, combines other control instructions of the wind turbine generator set for optimization processing, and outputs the wind turbine generator set operating state control instruction;

[0014] The wind shear-independent pitch control clearance state learning judgment is obtained by calculating the wind speed and wind direction data. When the wind shear is less than 0, it is judged that the wind rotor azimuth is parallel to the tower, and the independent pitch control is used to achieve early pitch retraction action;

[0015] By further updating the deep learning model, correcting the deviation between the design data and the measured data, debugging the deviation level, load deviation level, blade and tower angle, and corresponding propeller speed, the control parameters are optimized and the optimal control parameters are calculated.

[0016] Further, the wind speed-power stall state learning judgment includes:

[0017] Based on the comparison between the design data of the wind turbine generator set and the measured operating status data, the power in the measured operating status data adopts the inverter online data of the wind turbine generator set. When the wind speed is the same, after the deviation value of the wind speed-power correspondence exceeds the deviation level, it proves that a stall problem has occurred. When the deviation level is A, it is a mild stall, and the pitch rate is 0.5° / s; when the deviation level is B, it is a mild stall, and the pitch rate is 1° / s; when the deviation level is C, it is a mild stall, and the pitch rate is 2° / s; when the deviation level is D, it is a mild stall, and the pitch rate is 3° / s; when the deviation level is S, it is a mild stall, and the pitch rate is 5° / s for feathering standby idling;

[0018] The deviation value of the deviation level is calculated as:

[0019]

[0020] When 10%≤deviation value<15%, the deviation grade is A; when 15%≤deviation value<20%, the deviation grade is B; when 20%≤deviation value<30%, the deviation grade is C; when 30%≤deviation value<50%, the deviation grade is D; when the deviation value ≥50%, the deviation grade is S.

[0021] Further, the wind speed-load clearance state learning judgment includes:

[0022] Based on the comparison between the design data of the wind turbine and the measured operating status data, the load in the measured operating status data adopts the independent variable pitch load sensor data of the wind turbine. When the wind speed is the same, after the deviation of the wind speed-load correspondence exceeds the load deviation level, it proves that there is a tower sweep risk; when the load deviation level is A-load, it is a normal deviation, the pitch rate is ±0.1° / s, when the test load is greater than the design load, the pitch rate is 0.1° / s; when the test load is less than the design load, the pitch rate is -0.1° / s; when the load deviation level is B-load, it is a slight deviation, the pitch rate is ±0.5° / s, when the test load is greater than the design load, the pitch rate is The speed is 0.5° / s; when the test load is less than the design load, the pitch rate is -0.5° / s; when the load deviation level is C-load, it is a moderate deviation, the pitch rate is ±1° / s, when the test load is greater than the design load, the pitch rate is 1° / s; when the test load is less than the design load, the pitch rate is 1° / s; when the load deviation level is D-load, it is a severe deviation, the pitch rate is ±2° / s, when the test load is greater than the design load, the pitch rate is 2° / s; when the test load is less than the design load, the pitch rate is 2° / s; when the load deviation level is S-load, it is an extreme deviation, the pitch rate is 3° / s, and the wind turbine is idle in the feathering standby state;

[0023] The deviation calculation of the load deviation level is as follows: Deviation value calculation:

[0024]

[0025] When 5%≤deviation value<10%, the load deviation grade is A-load; when 10%≤deviation value<20%, the load deviation grade is B-load; when 20%≤deviation value<30%, the load deviation grade is C-load; when 30%≤deviation value<50%, the load deviation grade is D-load; when the deviation value ≥50%, the load deviation grade is S-load.

[0026] Further, the wind shear-independent pitch control clearance state learning judgment includes:

[0027] When the wind shear is calculated to be less than 0 through wind speed and wind direction data, it is judged that the azimuth of the wind rotor is parallel to the tower, and the early retraction of the pitch is achieved through independent pitch control; when -0.2<wind shear≤-0.05, when the angle between the blade and the tower is 30°, the pitch rate is 0.5° / s for retraction; when -0.4<wind shear≤-0.2, when the angle between the blade and the tower is 60°, the pitch rate is 1° / s for retraction; when -0.6<wind shear≤-0.4, when the angle between the blade and the tower is 90°, the pitch rate is 1.5° / s for retraction; when -1<wind shear≤-0.6, when the angle between the blade and the tower is 90°, the pitch rate is 2° / s for retraction; when wind shear≥-1, when the angle between the blade and the tower is 90°, the pitch rate is 3° / s for retraction.

[0028] Furthermore, the wind measurement module includes a 3D scanning radar, and the operating status data includes instantaneous wind speed, average wind speed, instantaneous wind direction, average wind direction, generator speed, generator torque, pitch angle, yaw angle, wind rotor azimuth and inverter online power.

[0029] A method for intelligent control of a wind generator set is implemented by a processor calling a wind measurement module, an operation status monitoring module and a main control module in the above-mentioned intelligent control system of the wind generator set.

[0030] A non-transitory computer-readable medium storing instructions, when the instructions are executed by a processor, executes the above-mentioned intelligent control method for a wind turbine generator set.

[0031] A computing device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned intelligent control method for a wind turbine generator set is implemented.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0033] 1. The present invention uses the operating status data of wind turbines and wind farms as input, performs learning and judgment through neural network algorithms and deep learning models, and changes the control parameters of wind turbines during operation without adding additional sensors, thus saving the cost of the entire machine and improving the economy of the entire machine.

[0034] 2. Through the neural network algorithm, the wind speed-power stall state learning and judgment are carried out to adjust the control parameters, avoiding the occurrence of stall problems during operation and ensuring the power generation and safety of the unit.

[0035] 3. Through the neural network algorithm, the wind speed-load stall state learning and judgment are used to adjust the control parameters, avoiding the occurrence of tower sweeping problems due to low clearance during operation and ensuring the safety of the unit.

[0036] 4. By combining 3D scanning radar and independent variable pitch technology, control parameters are adjusted to avoid the adverse effects of negative wind shear on clearance during operation and ensure the safety of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the architecture diagram of the intelligent control system of a wind turbine generator set.

[0038] Figure 2 This is a schematic diagram of the installation structure of the intelligent control system of a wind turbine generator set.

[0039] Figure 3 The figure is a flow chart for learning and judging the stall state of wind speed-power.

[0040] Figure 4 Flow chart for learning and judging the wind speed-load clearance state. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with specific embodiments.

[0042] Example 1

[0043] See also Figure 1 to Figure 2 As shown, the wind turbine generator set intelligent control system provided in this embodiment includes:

[0044] Wind measuring module 2, which is installed at the center of the hub 4 of the wind turbine generator set. Wind measuring module 2 includes a 3D scanning radar to avoid the influence of the wind wheel wake, and is used to obtain wind speed and wind direction data and transmit them to the main control module 3;

[0045] The operation status monitoring module 1 is used to record the operation status data of the wind turbine generator set and transmit the operation status data to the main control module 3; wherein the operation status data includes instantaneous wind speed, average wind speed, instantaneous wind direction, average wind direction, generator speed, generator torque, pitch angle, yaw angle, wind rotor azimuth and inverter grid power;

[0046] The main control module 3 is installed in the wheel hub 4. The operating status data input by the operating status monitoring module 1 is learned through a recurrent neural network, and the optimal control parameters are calculated in combination with the wind speed and wind direction data input by the wind measurement module 2 and the preset design data. The wind turbine is controlled to perform independent pitch control actions according to the optimal control parameters, and the stall state and clearance state of the wind turbine are adjusted during operation.

[0047] Further, the main control module 3 includes an operation status analysis module 301 and an operation status control module 302, wherein:

[0048] The operation status analysis module 301 is responsible for using a recurrent neural network to learn the operation status data of the wind turbine generator set, and combining the actual wind speed and wind direction data with the simulation design data, calculates and outputs the optimal control parameters to the operation status control system;

[0049] The operation state control module 302 controls the pitch change action of the wind turbine generator set, and adjusts the stall problem and clearance problem of the wind turbine generator set during operation through independent pitch change.

[0050] Furthermore, the operation status analysis module 301 includes:

[0051] The input operating status data is learned through neural network algorithms and deep learning models, including: learning and judging the stall state of wind speed-power, learning and judging the clearance state of wind speed-load, and learning and judging the clearance state of wind shear-independent pitch control; by updating the deep learning model, correcting the deviation between the design data and the measured data, debugging the deviation level, load deviation level, blade and tower angle, and corresponding pitch control rate, the control parameters are optimized and the optimal control parameters are calculated.

[0052] Furthermore, the stall state learning judgment of wind speed-power inputs the operating state data and the wind speed-power designed by the wind turbine generator set into the deep learning model, obtains an importance value through parameter comparison training and learning to determine the control instruction of the pitch action, combines the torque control and yaw control instructions of the wind turbine generator set for optimization processing, and outputs the operating state control instruction of the wind turbine generator set, which is as follows:

[0053] See also Figure 3As shown, based on the comparison between the design data of the wind turbine generator set and the measured operating status data, the power in the measured operating status data adopts the inverter online data of the wind turbine generator set. When the wind speed is the same, after the deviation value of the wind speed-power correspondence relationship exceeds the deviation level, it proves that a stall problem occurs. When the deviation level is A, it is a mild stall, and the pitch rate is 0.5° / s; when the deviation level is B, it is a mild stall, and the pitch rate is 1° / s; when the deviation level is C, it is a mild stall, and the pitch rate is 2° / s; when the deviation level is D, it is a mild stall, and the pitch rate is 3° / s; when the deviation level is S, it is a mild stall, and the pitch rate is 5° / s for feathering standby idling;

[0054] The deviation value of the deviation level is calculated as:

[0055]

[0056] When 10%≤deviation value<15%, the deviation grade is A; when 15%≤deviation value<20%, the deviation grade is B; when 20%≤deviation value<30%, the deviation grade is C; when 30%≤deviation value<50%, the deviation grade is D; when the deviation value ≥50%, the deviation grade is S.

[0057] Furthermore, the wind speed-load clearance state learning judgment inputs the operating state data and the wind speed-load designed by the wind turbine generator set into the deep learning model, obtains an importance value through parameter comparison training and learning to determine the control instruction of the pitch action, combines the torque control and yaw control instructions of the wind turbine generator set for optimization processing, and outputs the wind turbine generator set operating state control instruction, which is as follows:

[0058] See also Figure 4As shown, based on the comparison between the design data of the wind turbine and the measured operating status data, the load in the measured operating status data adopts the independent pitch load sensor data of the wind turbine. When the wind speed is the same, after the deviation of the wind speed-load correspondence exceeds the load deviation level, it proves that there is a tower sweep risk; when the load deviation level is A-load, it is a normal deviation, the pitch rate is ±0.1° / s, when the test load is greater than the design load, the pitch rate is 0.1° / s; when the test load is less than the design load, the pitch rate is -0.1° / s; when the load deviation level is B-load, it is a slight deviation, the pitch rate is ±0.5° / s, when the test load is greater than the design load, The pitch rate is 0.5° / s; when the test load is less than the design load, the pitch rate is -0.5° / s; when the load deviation level is C-load, it is a moderate deviation, the pitch rate is ±1° / s, when the test load is greater than the design load, the pitch rate is 1° / s; when the test load is less than the design load, the pitch rate is 1° / s; when the load deviation level is D-load, it is a severe deviation, the pitch rate is ±2° / s, when the test load is greater than the design load, the pitch rate is 2° / s; when the test load is less than the design load, the pitch rate is 2° / s; when the load deviation level is S-load, it is an extreme deviation, the pitch rate is 3° / s, and the wind turbine is idle in the feathering standby state;

[0059] The deviation calculation of the load deviation level is as follows: Deviation value calculation:

[0060]

[0061] When 5%≤deviation value<10%, the load deviation grade is A-load; when 10%≤deviation value<20%, the load deviation grade is B-load; when 20%≤deviation value<30%, the load deviation grade is C-load; when 30%≤deviation value<50%, the load deviation grade is D-load; when the deviation value ≥50%, the load deviation grade is S-load.

[0062] Furthermore, the wind shear-independent pitch control clearance state learning judgment is calculated by wind speed and wind direction data. When the wind shear is less than 0, it is judged that the wind rotor azimuth is parallel to the tower, and the independent pitch control is used to achieve early pitch retraction action;

[0063] When the wind shear is calculated to be less than 0 through wind speed and wind direction data, it is judged that the azimuth angle of the wind rotor is when blade 5 is parallel to tower 6, and the early retraction of the pitch is achieved through independent pitch change; when -0.2<wind shear≤-0.05, when the angle between blade 5 and tower 6 is 30°, the pitch rate is 0.5° / s for retraction; when -0.4<wind shear≤-0.2, when the angle between blade 5 and tower 6 is 60°, the pitch rate is 1° / s for retraction; when -0.6<wind shear≤-0.4, when the angle between blade 5 and tower 6 is 90°, the pitch rate is 1.5° / s for retraction; when -1<wind shear≤-0.6, when the angle between blade 5 and tower 6 is 90°, the pitch rate is 2° / s for retraction; when wind shear≥-1, when the angle between blade 5 and tower 6 is 90°, the pitch rate is 3° / s for retraction.

[0064] Example 2

[0065] This embodiment discloses an intelligent control method for a wind turbine generator set, which is implemented by a processor calling a wind measurement module, an operation status monitoring module, and a main control module in the intelligent control system of the wind turbine generator set described in Embodiment 1.

[0066] Example 3

[0067] This embodiment discloses a non-transitory computer-readable medium storing instructions. When the instructions are executed by a processor, the steps of intelligent control of the wind turbine generator set according to Embodiment 2 are performed.

[0068] The non-temporary computer-readable medium in this embodiment may be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or other media.

[0069] Example 4

[0070] This embodiment discloses a computing device, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the intelligent control method for a wind turbine generator set described in Embodiment 2 is implemented.

[0071] The computing device described in this embodiment may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with a processor function.

[0072] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control system for a wind turbine generator set, characterized in that: include: The wind measurement module is installed at the center of the hub of the wind turbine generator set to obtain wind speed and direction data and transmit them to the main control module; An operation status monitoring module is used to record the operation status data of the wind turbine generator set and transmit the operation status data to the main control module; The main control module learns the input operating status data through neural network algorithms and deep learning models, calculates the optimal control parameters based on wind speed and direction data and preset design data, controls the wind turbine to perform independent pitch control actions according to the optimal control parameters, and adjusts the stall state and clearance state of the wind turbine during operation.

2. The intelligent control system for a wind turbine generator set according to claim 1, characterized in that: The main control module includes an operation status analysis module and an operation status control module.

3. The intelligent control system of a wind turbine generator set according to claim 2, characterized in that: The operation status analysis module includes: The input operation status data is learned through neural network algorithms and deep learning models, including: learning and judging the stall state of wind speed-power, learning and judging the clearance state of wind speed-load, and learning and judging the clearance state of wind shear-independent pitch control; The stall state learning judgment of wind speed-power inputs the operating state data and the designed wind speed-power of the wind turbine generator set into the deep learning model, obtains an importance value through parameter comparison training and learning to determine the control instruction of the pitch action, combines other preset control instructions of the wind turbine generator set for optimization processing, and outputs the operating state control instruction of the wind turbine generator set; The wind speed-load clearance state learning judgment inputs the operating state data and the wind speed-load designed by the wind turbine generator set into the deep learning model, obtains an importance value through parameter comparison training and learning to determine the control instruction of the pitch action, combines other preset control instructions of the wind turbine generator set for optimization processing, and outputs the wind turbine generator set operating state control instruction; The wind shear-independent pitch control clearance state learning judgment is obtained by calculating the wind speed and wind direction data. When the wind shear is less than 0, it is judged that the wind rotor azimuth is parallel to the tower, and the independent pitch control is used to achieve early pitch retraction action; By further updating the deep learning model, correcting the deviation between the design data and the measured data, debugging the deviation level, load deviation level, blade and tower angle, and corresponding propeller speed, the control parameters are optimized and the optimal control parameters are calculated.

4. The intelligent control system for wind turbine generator set according to claim 3, characterized in that: The wind speed-power stall state learning judgment includes: Based on the comparison between the design data of the wind turbine generator set and the measured operating status data, the power in the measured operating status data adopts the inverter online data of the wind turbine generator set. When the wind speed is the same, after the deviation value of the wind speed-power correspondence exceeds the deviation level, it proves that a stall problem has occurred. When the deviation level is A, it is a mild stall, and the pitch rate is 0.5° / s; when the deviation level is B, it is a mild stall, and the pitch rate is 1° / s; when the deviation level is C, it is a mild stall, and the pitch rate is 2° / s; when the deviation level is D, it is a mild stall, and the pitch rate is 3° / s; when the deviation level is S, it is a mild stall, and the pitch rate is 5° / s for feathering standby idling; The deviation value of the deviation level is calculated as: When 10%≤deviation value<15%, the deviation grade is A; when 15%≤deviation value<20%, the deviation grade is B; when 20%≤deviation value<30%, the deviation grade is C; when 30%≤deviation value<50%, the deviation grade is D; when the deviation value ≥50%, the deviation grade is S.

5. The intelligent control system of a wind turbine generator set according to claim 3, characterized in that: The wind speed-load clearance state learning judgment includes: Based on the comparison between the design data of the wind turbine and the measured operating status data, the load in the measured operating status data adopts the independent variable pitch load sensor data of the wind turbine. When the wind speed is the same, after the deviation of the wind speed-load correspondence exceeds the load deviation level, it proves that there is a tower sweep risk; when the load deviation level is A-load, it is a normal deviation, the pitch rate is ±0.1° / s, when the test load is greater than the design load, the pitch rate is 0.1° / s; when the test load is less than the design load, the pitch rate is -0.1° / s; when the load deviation level is B-load, it is a slight deviation, the pitch rate is ±0.5° / s, when the test load is greater than the design load, the pitch rate is The speed is 0.5° / s; when the test load is less than the design load, the pitch rate is -0.5° / s; when the load deviation level is C-load, it is a moderate deviation, the pitch rate is ±1° / s, when the test load is greater than the design load, the pitch rate is 1° / s; when the test load is less than the design load, the pitch rate is 1° / s; when the load deviation level is D-load, it is a severe deviation, the pitch rate is ±2° / s, when the test load is greater than the design load, the pitch rate is 2° / s; when the test load is less than the design load, the pitch rate is 2° / s; when the load deviation level is S-load, it is an extreme deviation, the pitch rate is 3° / s, and the wind turbine is idle in the feathering standby state; The deviation calculation of the load deviation level is as follows: Deviation value calculation: When 5%≤deviation value<10%, the load deviation grade is A-load; when 10%≤deviation value<20%, the load deviation grade is B-load; when 20%≤deviation value<30%, the load deviation grade is C-load; when 30%≤deviation value<50%, the load deviation grade is D-load; when the deviation value ≥50%, the load deviation grade is S-load.

6. The intelligent control system of a wind turbine generator set according to claim 3, characterized in that: The wind shear-independent pitch control clearance state learning judgment includes: When the wind shear is calculated to be less than 0 through wind speed and wind direction data, it is judged that the azimuth of the wind rotor is parallel to the tower, and the early retraction of the pitch is achieved through independent pitch control; when -0.2<wind shear≤-0.05, when the angle between the blade and the tower is 30°, the pitch rate is 0.5° / s for retraction; when -0.4<wind shear≤-0.2, when the angle between the blade and the tower is 60°, the pitch rate is 1° / s for retraction; when -0.6<wind shear≤-0.4, when the angle between the blade and the tower is 90°, the pitch rate is 1.5° / s for retraction; when -1<wind shear≤-0.6, when the angle between the blade and the tower is 90°, the pitch rate is 2° / s for retraction; when wind shear≥-1, when the angle between the blade and the tower is 90°, the pitch rate is 3° / s for retraction.

7. The intelligent control system for a wind turbine generator set according to claim 1, characterized in that: The wind measurement module includes a 3D scanning radar, and the operating status data includes instantaneous wind speed, average wind speed, instantaneous wind direction, average wind direction, generator speed, generator torque, pitch angle, yaw angle, wind wheel azimuth and inverter online power.

8. A wind turbine intelligent control method, characterized in that: The method is implemented by a processor calling a wind measurement module, an operation status monitoring module and a main control module in the intelligent control system of a wind turbine generator set according to any one of claims 1 to 7.

9. A non-transitory computer-readable medium storing instructions, characterized in that: When the instruction is executed by the processor, the intelligent control method for the wind turbine generator set according to claim 8 is executed.

10. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the intelligent control method for the wind turbine generator set described in claim 8 is implemented.

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