High-precision encoder control system based on artificial intelligence

Abstract

The invention discloses a high-precision encoder control system based on artificial intelligence, which comprises an information interconnection module, a blade attitude prediction module and a high-precision control module, and is characterized in that the information interconnection module is used for realizing information data interconnection among a plurality of groups of wind driven generators through a high-speed communication network; the blade attitude prediction module is used for analyzing and predicting the optimal attitude angle of the blades of the wind driven generator group in real time, and the high-precision control module is used for performing high-precision control on the attitude of the blades of the target wind driven generator group according to the analysis result of the blade attitude prediction module. The information interconnection module is in network connection with the blade attitude prediction module, and the blade attitude prediction module is in network connection with the high-precision control module.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a high-precision encoder control system based on artificial intelligence. Background Art

[0002] In the field of wind power generation, precise control of blade attitude is a key factor in ensuring efficient operation of the unit and maximizing energy conversion efficiency. Traditional blade attitude control systems rely on simple sensors and fixed algorithms, which are difficult to cope with complex and changing wind conditions, resulting in limited control accuracy, which in turn affects power generation efficiency and unit stability.

[0003] With the advancement of science and technology, especially the rapid development of artificial intelligence technology, the blade attitude control of wind turbines has undergone revolutionary changes. By combining artificial intelligence algorithms with high-precision sensors, more intelligent and precise control strategies can be achieved. Among them, the 23-bit absolute encoder, as a high-precision position sensor, has shown great potential in the blade attitude control of wind turbines due to its high resolution, high reliability and strong anti-interference ability. Summary of the invention

[0004] The purpose of the present invention is to provide a high-precision encoder control system based on artificial intelligence to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a high-precision encoder control system based on artificial intelligence, including an information interconnection module, a blade attitude prediction module and a high-precision control module, the information interconnection module is used to interconnect information data between several groups of wind turbine groups through a high-speed communication network, the blade attitude prediction module is used to analyze and predict the optimal attitude angle of the blades of the wind turbine group in real time, the high-precision control module is used to control the high-precision control of the blade attitude of the target wind turbine group according to the analysis results of the blade attitude prediction module, the information interconnection module is network-connected to the blade attitude prediction module, and the blade attitude prediction module is network-connected to the high-precision control module.

[0006] According to the above technical solution, the information interconnection module includes a cluster equipment information collection module, a cluster position acquisition module and a regional terrain capture module. The cluster equipment information collection module is used to collect the operating data of each wind turbine group in real time, the cluster position acquisition module is used to locate and obtain the position of each wind turbine group connected to the information interconnection, and the regional terrain capture module is used to capture the overall terrain of the area where each wind turbine group is located.

[0007] According to the above technical solution, the blade attitude prediction module includes an influencing condition analysis module and a blade angle adjustment module. The influencing condition analysis module is used to analyze the influence of the terrain on the airflow between each wind turbine group and preset the mutual influencing parameters. The blade angle adjustment module is used to analyze the blade attitude adjustment data corresponding to each wind turbine group when the airflow direction changes.

[0008] According to the above technical solution, the blade angle adjustment module further includes an adjustment angle calculation submodule and an adjustment timing calculation submodule. The adjustment angle calculation submodule is used to analyze and calculate the target angle value for optimal adjustment of the blades of the target wind turbine generator, and the adjustment timing calculation submodule is used to analyze and calculate the optimal start time for controlling the blade adjustment of the target wind turbine generator.

[0009] According to the above technical solution, the high-precision control module includes a trigger timing control module and a blade angle control module. The trigger timing control module is used to control the timing of starting the drive mechanism of the target wind turbine group to adjust the blade posture, and the blade angle control module is used to control the adjustment of the blade angle to achieve the precise command target angle.

[0010] According to the above technical solution, the cluster equipment information collection module further includes a wind speed collection submodule, a wind direction collection submodule and a blade angle monitoring submodule. The wind speed collection submodule is used to monitor and collect the ambient airflow wind speed data received by the wind turbine group, the wind direction collection submodule is used to monitor and collect the ambient airflow wind direction data received by the wind turbine group, and the blade angle monitoring submodule is used to monitor and collect the rotation angle data of the blades.

[0011] According to the above technical solution, the operating method of the system includes:

[0012] Step S1: Open up the server information channels of multiple wind turbine groups connected to the system through the information interconnection module, establish an efficient information integrated management platform, and carry out data intercommunication and information sharing within the platform;

[0013] Step S2: respectively collecting the ambient air flow wind speed value v and wind direction angle value j monitored in real time for each wind turbine group, and monitoring and collecting the precise angle value u of the blades of each wind turbine group;

[0014] Step S3: further obtaining the location of each wind turbine group, selecting all wind turbine group location areas in the efficient information integrated management platform, and then using Internet information to capture the topography of the selected area;

[0015] Step S4: Taking the area where each wind turbine group is located as the prediction target, combined with the captured regional topographic features, fitting analysis is performed on the wind direction angle value interval (j) of the airflow in different wind directions from each wind turbine group area to the prediction target area under the influence of the regional topography. x ,j y );

[0016] Step S5: When one of the wind turbines in the selected area detects a wind direction angle value j1 and satisfies j1-j a |>Δj, the blade attitude prediction angle adjustment instruction is started, where j a is the optimal wind direction angle value corresponding to the current blade angle of one of the wind turbine groups, and Δj is the minimum wind speed angle difference required for blade attitude prediction control, which is preset by the system; among all the wind turbine groups connected to the system, the wind turbine groups that meet the blade attitude angle adjustment conditions are taken as prediction targets one by one to perform blade angle analysis and calculation;

[0017] Step S6: Obtain the blade angle analysis calculation results, and start the adjustment program at the best time according to the calculation results. During the adjustment, the measured blade angle information is converted into an electrical signal output through a 23-bit absolute encoder. After the signal is transmitted to the system, the precise position of the blade is understood in real time by decoding the signal.

[0018] According to the above technical solution, step S5 further includes:

[0019] Step S51: The wind direction angle value j1 is compared with the affected wind direction angle value interval (j xi ,j yi ) one by one matching, filter out The corresponding wind turbine groups are marked, and all the marked wind turbine groups are taken as prediction targets one by one to perform blade angle analysis and calculation, where i = 1, 2, ..., n;

[0020] Step S52: Establish a large database, compare and collate historical wind energy capture data, and obtain the predicted wind direction angle value j of the target wind turbine group and the precise blade angle value u of the wind turbine group to convert the parameter value δ i ; where i = 1, 2, ..., n;

[0021] Step S53: By formula u ci =δ i ·j Calculate and obtain the target angle value of the blade adjustment of the wind turbine group;

[0022] Step S54: The adjustment timing calculation submodule calculates the blade adjustment timing of the current wind turbine group using a formula including:

[0023] Δu i =u ci -u i ...Formula (1)

[0024]

[0025] In the above formula, u ci The target angle value for adjusting the blades of the current wind turbine group, u i The exact blade angle value of the current wind turbine group, Δu i is the angle difference value that needs to be adjusted for the blades at present, t i is the time value of the current blade adjustment, f is the blade adjustment speed value, T i is the time value of the airflow from the first sensing area to the current predicted target area, L i is the distance from the first sensing area to the current predicted target area, v is the current ambient airflow speed, θ is the angle between the straight line from the first sensing area to the current predicted target area and the airflow direction straight line, T ti Adjust the timing of blades for the current fleet of wind turbines.

[0026] According to the above technical solution, step S6 further includes:

[0027] Step S61: When T i >t i When the trigger timing control module controls the driving mechanism of the wind turbine group in the current prediction target area, starting from the moment when the wind direction in the first sensing area changes, T ti After a few seconds, the program starts to execute, causing the wind turbine blades to start rotating and adjusting the blade angle. i When the adjustment procedure is completed;

[0028] Step S62: When T i ≤t i When the wind direction changes, the trigger timing control module controls the driving mechanism of the wind turbine group in the current predicted target area, and starts to execute the program immediately from the moment when the wind direction changes in the first sensing area, so that the wind turbine blades start to rotate and the blade angles are adjusted. When the airflow with changing wind direction reaches the wind turbine group in the current predicted target area, the current group equipment collects data in real time, adjusts to the optimal target angle value under the measured wind direction angle value, and completes the adjustment program.

[0029] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, by providing an information interconnection module, a blade attitude prediction module and a high-precision control module, can analyze and predict the optimal attitude of the blades of the wind turbine generator set when the airflow and wind direction change, as well as the optimal time to adjust the blades, and can use a 23-bit absolute value encoder to capture slight changes in the blades, so that the control system can achieve high-precision control of the blade attitude based on the data provided by the encoder, thereby optimizing the efficiency of capturing wind energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figure 1 The present invention provides a technical solution: a high-precision encoder control system based on artificial intelligence, including an information interconnection module, a blade attitude prediction module and a high-precision control module, the information interconnection module is used to interconnect information data between several groups of wind turbine groups through a high-speed communication network, the blade attitude prediction module is used to analyze and predict the optimal attitude angle of the blades of the wind turbine group in real time, the high-precision control module is used to control the high-precision control of the blade attitude of the target wind turbine group according to the analysis result of the blade attitude prediction module, the information interconnection module is network-connected to the blade attitude prediction module, and the blade attitude prediction module is network-connected to the high-precision control module; by providing the information interconnection module, the blade attitude prediction module and the high-precision control module, the optimal attitude of the corresponding blade of the wind turbine group when the airflow and wind direction change and the optimal time to adjust the blade can be analyzed and predicted, and the 23-bit absolute value encoder can be used to capture the slight changes in the blade, so that the control system can achieve high-precision control of the blade attitude based on the data provided by the encoder, thereby optimizing the capture efficiency of wind energy.

[0034] The information interconnection module includes a cluster equipment information collection module, a cluster location acquisition module and a regional terrain capture module. The cluster equipment information collection module is used to collect the operating data of each wind turbine group in real time. The cluster location acquisition module is used to locate and obtain the position of each wind turbine group connected to the information interconnection. The regional terrain capture module is used to capture the overall terrain of the area where each wind turbine group is located.

[0035] The blade attitude prediction module includes an influencing condition analysis module and a blade angle adjustment module. The influencing condition analysis module is used to analyze the impact of terrain on the airflow between each wind turbine group and preset the mutual influencing parameters. The blade angle adjustment module is used to analyze the blade attitude adjustment data corresponding to each wind turbine group when the airflow direction changes.

[0036] The blade angle adjustment module further includes an adjustment angle calculation submodule and an adjustment timing calculation submodule. The adjustment angle calculation submodule is used to analyze and calculate the target angle value for optimal adjustment of the blades of the target wind turbine. The adjustment timing calculation submodule is used to analyze and calculate the optimal start time for controlling the blade adjustment of the target wind turbine.

[0037] The high-precision control module includes a trigger timing control module and a blade angle control module. The trigger timing control module is used to control the timing of starting the drive mechanism of the target wind turbine group to adjust the blade posture. The blade angle control module is used to control the adjustment of the blade angle to achieve the precise command target angle. During this period, a 23-bit absolute encoder is used to capture tiny changes in the blades to achieve high-precision control of the blade posture, thereby optimizing the efficiency of wind energy capture.

[0038] The cluster equipment information collection module further includes a wind speed collection submodule, a wind direction collection submodule and a blade angle monitoring submodule. The wind speed collection submodule is used to monitor and collect the ambient airflow wind speed data received by the wind turbine group, the wind direction collection submodule is used to monitor and collect the ambient airflow wind direction data received by the wind turbine group, and the blade angle monitoring submodule is used to monitor and collect the rotation angle data of the blades.

[0039] The system's method of operation includes:

[0040] Step S1: Open up the server information channels of multiple wind turbine groups connected to the system through the information interconnection module, establish an efficient information integrated management platform, and carry out data intercommunication and information sharing within the platform; one wind turbine group is composed of several groups of wind turbine groups distributed in the same area, while multiple wind turbine groups are distributed in different areas;

[0041] Step S2: respectively collecting the ambient air flow wind speed value v and wind direction angle value j monitored in real time for each wind turbine group, and monitoring and collecting the precise angle value u of the blades of each wind turbine group;

[0042] Step S3: further obtaining the location of each wind turbine group, selecting all wind turbine group location areas in the efficient information integrated management platform, and then using Internet information to capture the topography of the selected area;

[0043] Step S4: Taking the area where each wind turbine group is located as the prediction target, combined with the captured regional topographic features, fitting analysis is performed on the wind direction angle value interval (j) of the airflow in different wind directions from each wind turbine group area to the prediction target area under the influence of the regional topography. x ,j y ); the affected wind direction angle value range (j x ,j y ) indicates that the wind turbines that sense wind direction changes and the predicted wind turbines that are predicted are firstly predicted at the wind direction angle value j∈(j x ,j y ), the two areas are affected by the terrain, and the airflow in the area where the wind direction change is first sensed is difficult to effectively reach the predicted target wind turbine group area; for example, there are high mountains between the first sensing area and the predicted target area, and the airflow wind direction is just perpendicular to the mountains and blows toward the predicted target wind turbine group area, and the two areas are far apart, and the airflow affected by the mountain barrier cannot affect the change of airflow wind direction in the predicted target area; therefore, by repeating step S4, all wind turbine groups can be preset in the system respectively;

[0044] Step S5: When one of the wind turbines in the selected area detects a wind direction angle value j1 and satisfies j1-j a |>Δj, the blade attitude prediction angle adjustment instruction is started, where j a is the optimal wind direction angle value corresponding to the current blade angle of one of the wind turbine groups, and Δj is the minimum wind speed angle difference value required for blade attitude prediction control, which is preset by the system; among all the wind turbine groups connected to the system, the wind turbine groups that meet the blade attitude angle adjustment conditions are taken as prediction targets one by one to perform blade angle analysis and calculation; when the wind direction change angle is less than the preset value Δj, because the adjustment angle is relatively small, the adjustment cycle is correspondingly short, and the smaller angle deviation has a smaller impact on the wind energy capture efficiency of other wind turbine groups in the area. Furthermore, a smaller wind direction angle deviation is used to perform a prediction program, which is bound to cause a surge in system computing power and increase hardware costs, resulting in the economic benefits of wind energy efficiency being less than the additional costs; therefore, through this step, only when the wind direction change angle is greater than the preset value Δj, the blade attitude prediction program is controlled to start, thereby optimizing the wind energy capture efficiency and enhancing the reliability of system operation;

[0045] Step S6: Obtain the blade angle analysis calculation results, and start the adjustment program at the best time according to the calculation results. During the adjustment, the measured blade angle information is converted into an electrical signal output through a 23-bit absolute encoder. After the signal is transmitted to the system, the precise position of the blade is understood in real time by decoding the signal.

[0046] Step S5 further comprises:

[0047] Step S51: The wind direction angle value j1 is compared with the affected wind direction angle value interval (j xi ,j yi ) one by one matching, filter out The corresponding wind turbine groups are marked, and all the marked wind turbine groups are taken as prediction targets one by one for blade angle analysis and calculation, where i=1, 2, ..., n; through this step, the wind turbine groups that are affected by topographic factors and have mutual influence under specific wind direction angles can be further screened out, and the wind turbine groups that have influence on the prediction target area under the current wind direction angle can be directly screened out, thereby effectively reducing the subsequent unnecessary blade posture analysis and calculation procedures, reducing the maximum computing load of the system, optimizing the program algorithm, and further increasing the practicality of the present invention;

[0048] Step S52: Establish a large database, compare and collate historical wind energy capture data, and obtain the predicted wind direction angle value j of the target wind turbine group and the precise blade angle value u of the wind turbine group to convert the parameter value δ i ; where i = 1, 2, ..., n;

[0049] Step S53: By formula u ci =δ i ·j Calculate and obtain the target angle value of the blade adjustment of the wind turbine group;

[0050] Step S54: The adjustment timing calculation submodule calculates the blade adjustment timing of the current wind turbine group using a formula including:

[0051] Δu i =u ci -u i ...Formula (1)

[0052]

[0053] In the above formula, u ci The target angle value for adjusting the blades of the current wind turbine group, u i The exact blade angle value of the current wind turbine group, Δu i is the angle difference value that needs to be adjusted for the blades at present, t iis the time value of the current blade adjustment, f is the blade adjustment speed value, T i is the time value of the airflow from the first sensing area to the current predicted target area, L i is the distance from the first sensing area to the current predicted target area, v is the current ambient airflow speed, θ is the angle between the straight line from the first sensing area to the current predicted target area and the airflow direction straight line, T ti Adjust the timing of blades for the current fleet of wind turbines;

[0054] The angle difference and adjustment Δu required for blade adjustment are calculated by formula (1) and formula (2) respectively. i The required equipment working time value; then, through formula (3), v·cosθ is used to first calculate the effective wind speed between the airflow direction from the first sensing area to the current predicted target area, and then the distance between the two is combined to accurately calculate and predict the time value T for the airflow to reach the current predicted target area from the first sensing area i ; Finally, formula (4) is used to accurately calculate the blade adjustment timing of the current wind turbine group; and thus the blade adjustment timing of the current wind turbine group can be effectively analyzed and predicted, so that after the airflow with changed wind direction is detected by the first sensing area, the area composed of several groups of wind turbine groups can be synchronously controlled and managed comprehensively, so that no matter how far or near the distance is, as long as it does not belong to the area affected by topographic factors, it can be adjusted at the corresponding time, and the airflow with changed wind direction arrives at the corresponding area just when the adjustment is completed, so as to maximize the efficiency of wind turbine group in capturing wind energy. With the 23-bit absolute encoder as a high-precision position sensor, it can effectively avoid the serious adjustment lag, poor adjustment accuracy, program confusion and other problems of the existing blade attitude control system when dealing with complex and changeable wind conditions, thereby greatly improving the power generation efficiency and unit stability of wind power generation.

[0055] Step S6 further comprises:

[0056] Step S61: When T i >t i When the trigger timing control module controls the driving mechanism of the wind turbine group in the current prediction target area, starting from the moment when the wind direction in the first sensing area changes, T ti After a few seconds, the program starts to execute, causing the wind turbine blades to start rotating and adjusting the blade angle. i When the adjustment procedure is completed;

[0057] Step S62: When T i ≤t iWhen the predicted airflow arrival time value is greater than the blade adjustment time value, the blades will not be adjusted immediately when the wind direction is sensed to change in the area, so as to avoid the blades still capturing the wind energy before the wind direction changes and failing to maximize the energy capture efficiency. When the predicted airflow arrival time value is less than the blade adjustment time value, the blades will be adjusted immediately, and the maximum angle adjustment will be completed as soon as possible when the airflow with the changed wind direction arrives at the current wind turbine group, thereby increasing the energy replenishment efficiency. After the airflow with the changed wind direction arrives, there is no need to continue to execute according to the predicted angle, but to adjust according to the actual monitoring data, so that the adjustment angle is more accurate than the predicted value.

[0058] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0059] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-precision encoder control system based on artificial intelligence, characterized in that: The system includes an information interconnection module, a blade attitude prediction module and a high-precision control module. The information interconnection module is used to interconnect information data between several groups of wind turbine groups through a high-speed communication network. The blade attitude prediction module is used to analyze and predict the optimal attitude angle of the blades of the wind turbine group in real time. The high-precision control module is used to control the high-precision control of the blade attitude of the target wind turbine group according to the analysis results of the blade attitude prediction module. The information interconnection module is connected to the blade attitude prediction module network, and the blade attitude prediction module is connected to the high-precision control module network.

2. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: The information interconnection module includes a cluster equipment information collection module, a cluster location acquisition module and a regional terrain capture module. The cluster equipment information collection module is used to collect the operating data of each wind turbine group in real time, the cluster location acquisition module is used to locate and obtain the position of each wind turbine group connected to the information interconnection, and the regional terrain capture module is used to capture the overall terrain of the area where each wind turbine group is located.

3. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: The blade attitude prediction module includes an influencing condition analysis module and a blade angle adjustment module. The influencing condition analysis module is used to analyze the influence of terrain on airflow between each wind turbine group and preset the mutual influencing parameters. The blade angle adjustment module is used to analyze the blade attitude adjustment data corresponding to each wind turbine group when the airflow direction changes.

4. The high-precision encoder control system based on artificial intelligence according to claim 3 is characterized in that: The blade angle adjustment module further includes an adjustment angle calculation submodule and an adjustment timing calculation submodule. The adjustment angle calculation submodule is used to analyze and calculate the target angle value for optimal adjustment of the blades of the target wind turbine. The adjustment timing calculation submodule is used to analyze and calculate the optimal start time for controlling the blade adjustment of the target wind turbine.

5. The high-precision encoder control system based on artificial intelligence according to claim 1, characterized in that: The high-precision control module includes a trigger timing control module and a blade angle control module. The trigger timing control module is used to control the timing of starting the drive mechanism of the target wind turbine group to adjust the blade posture, and the blade angle control module is used to control and adjust the blade angle to achieve the precise command target angle.

6. The high-precision encoder control system based on artificial intelligence according to claim 2, characterized in that: The cluster equipment information collection module further includes a wind speed collection submodule, a wind direction collection submodule and a blade angle monitoring submodule. The wind speed collection submodule is used to monitor and collect the ambient airflow wind speed data received by the wind turbine group, the wind direction collection submodule is used to monitor and collect the ambient airflow wind direction data received by the wind turbine group, and the blade angle monitoring submodule is used to monitor and collect the rotation angle data of the blades.

7. The high-precision encoder control system based on artificial intelligence according to claim 6, characterized in that: The system's method of operation includes: Step S1: Open up the server information channels of multiple wind turbine groups connected to the system through the information interconnection module, establish an efficient information integrated management platform, and carry out data intercommunication and information sharing within the platform; Step S2: respectively collecting the ambient air flow wind speed value v and wind direction angle value j monitored in real time for each wind turbine group, and monitoring and collecting the precise angle value u of the blades of each wind turbine group; Step S3: further obtaining the location of each wind turbine group, selecting all wind turbine group location areas in the efficient information integrated management platform, and then using Internet information to capture the topography of the selected area; Step S4: Taking the area where each wind turbine group is located as the prediction target, combined with the captured regional topographic features, fitting analysis is performed on the wind direction angle value interval (j) of the airflow in different wind directions from each wind turbine group area to the prediction target area under the influence of the regional topography. x ,j y ); Step S5: When one of the wind turbines in the selected area detects a wind direction angle value j1 and satisfies |j1-j a |>Δj, the blade attitude prediction angle adjustment instruction is started, where j a is the optimal wind direction angle value corresponding to the current blade angle of one of the wind turbine groups, and Δj is the minimum wind speed angle difference required for blade attitude prediction control, which is preset by the system; among all the wind turbine groups connected to the system, the wind turbine groups that meet the blade attitude angle adjustment conditions are taken as prediction targets one by one to perform blade angle analysis and calculation; Step S6: Obtain the blade angle analysis calculation results, and start the adjustment program at the best time according to the calculation results. During the adjustment, the measured blade angle information is converted into an electrical signal output through a 23-bit absolute encoder. After the signal is transmitted to the system, the precise position of the blade is understood in real time by decoding the signal.

8. The high-precision encoder control system based on artificial intelligence according to claim 7, characterized in that: The step S5 further comprises: Step S51: The wind direction angle value j1 is compared with the affected wind direction angle value interval (j xi ,j yi ) one by one matching, filter out The corresponding wind turbine groups are marked, and all the marked wind turbine groups are taken as prediction targets one by one to perform blade angle analysis and calculation, where i = 1, 2, ..., n; Step S52: Establish a large database, compare and collate historical wind energy capture data, and obtain the predicted wind direction angle value j of the target wind turbine group and the precise blade angle value u of the wind turbine group to convert the parameter value δ i ; where i = 1, 2, ..., n; Step S53: By formula u ci =δ i ·j Calculate and obtain the target angle value of the blade adjustment of the wind turbine group; Step S54: The adjustment timing calculation submodule calculates the blade adjustment timing of the current wind turbine group using a formula including: Δu i =u ci -u i …Official(1) In the above formula, u ci The target angle value for adjusting the blades of the current wind turbine group, u i The exact blade angle value of the current wind turbine group, Δu i is the angle difference value that needs to be adjusted for the blades at present, t i is the time value of the current blade adjustment, f is the blade adjustment speed value, T i is the time value of the airflow from the first sensing area to the current predicted target area, L i is the distance from the first sensing area to the current predicted target area, v is the current ambient airflow speed, θ is the angle between the straight line from the first sensing area to the current predicted target area and the airflow direction straight line, T ti Adjust the timing of blades for the current fleet of wind turbines.

9. The high-precision encoder control system based on artificial intelligence according to claim 8, characterized in that: The step S6 further comprises: Step S61: When T i >t i When the trigger timing control module controls the driving mechanism of the wind turbine group in the current prediction target area, starting from the moment when the wind direction in the first sensing area changes, T ti After a few seconds, the program starts to execute, causing the wind turbine blades to start rotating and adjusting the blade angle. i When the adjustment procedure is completed; Step S62: When T i ≤t i When the wind direction changes, the trigger timing control module controls the driving mechanism of the wind turbine group in the current predicted target area, and starts to execute the program immediately from the moment when the wind direction changes in the first sensing area, so that the wind turbine blades start to rotate and the blade angles are adjusted. When the airflow with changing wind direction reaches the wind turbine group in the current predicted target area, the current group equipment collects data in real time, adjusts to the optimal target angle value under the measured wind direction angle value, and completes the adjustment program.

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