Individual pitch adjustment method adjustment system for wind driven generator

Abstract

The present invention provides an individual pitch adjustment method and adjustment system for a wind driven generator. The adjustment method comprises: monitoring and obtaining a blade root load condition of each blade of a wind driven generator within a period of time, performing machine learning, determining an optimal pitch angle at each wind speed, and writing a correspondence between the blade root load and the optimal pitch angle into a control function; monitoring and obtaining the blade root load condition of each blade in real time, extracting rotating frequency information from the blade root load condition, and according to the rotating frequency information, determining whether the wind driven generator is currently in a full-power state; if the wind driven generator is currently in a non-full-power state, performing an individual pitch operation on the current blade according to a blade root load monitoring result of each blade and the control function; and if the wind driven generator is currently in the full-power state, according to the blade root load monitoring result of each blade, determining whether the blade root loads of all the blades are consistent, and if not, performing the individual pitch operation on the current blade in real time according to the blade root load monitoring result of the blade. Therefore, the purpose of increasing the generating capacity of the wind driven generator is achieved.

Description

Wind turbine independent pitch adjustment method and adjustment system Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method and system for adjusting independent pitch of a wind turbine. Background Art

[0002] In the current mainstream wind turbine design, the pitch angles of the three blades are synchronously adjusted based on real-time wind speed monitoring. That is, during operation, the three blades adjust their pitch angles in real time based on wind speed, and the adjusted angles are consistent across all three blades. The advantage of this pitch adjustment method is that the adjustment strategy is simple and easy to operate, and it effectively ensures proper operation and efficiency control of the wind turbine within a certain error range. However, as wind turbines become larger, the wind speed distribution within the rotor's rotation plane becomes increasingly variable. For example, for large wind turbines, the wind speed at the top of the rotor can differ by 3-5 m / s from that at the bottom. Therefore, the wind speeds experienced by the three blades at different heights within the rotor's rotation plane vary significantly, resulting in significantly different optimal pitch angles for blades at different positions within the rotor's rotation plane. Consequently, the demand for independent pitch adjustment based on the blade's position within the rotor's rotation plane is increasing.

[0003] However, wind speed monitoring of wind turbines relies on a single anemometer on top of the turbine hub, which makes it impossible to determine the actual wind speed of each blade during wind turbine operation, and unable to determine the optimal pitch angle that the blades should be adjusted to. Therefore, it is difficult to adjust the pitch angle in a targeted manner, and thus it is impossible to ensure that the power of the blades is in the optimal state, resulting in difficulty in maximizing the power generation of the wind turbine.

[0004] Summary of the Invention

[0005] The purpose of the present invention is to provide a wind turbine independent pitch adjustment method and adjustment system, which can specifically design independent pitch adjustment strategies to keep the blades in the optimal efficiency state at all times and improve the power generation of the wind turbine.

[0006] In order to achieve the above object, the present invention provides a method for adjusting the independent pitch of a wind turbine, comprising:

[0007] Monitor and obtain the blade root load of each wind turbine blade over a period of time and perform machine learning to determine the optimal pitch angle for each blade at each wind speed. The corresponding relationship between the blade root load and the optimal pitch angle is then written into a control function.

[0008] Monitor and obtain the root load of each blade in real time, extract the rotational frequency information therefrom, and determine whether the wind turbine is currently in a full power state based on the rotational frequency information;

[0009] If the wind turbine is currently in an under-powered state, performing independent pitch control on the current blade according to the blade root load monitoring result of each blade and the control function;

[0010] If the wind turbine is currently in full power state, it is determined whether the root loads of all blades are consistent based on the root load monitoring results of each blade. If they are inconsistent, the current blade is independently pitched in real time based on the root load monitoring results of the blade to keep the root loads of all blades consistent.

[0011] Optionally, the step of monitoring and obtaining the blade root load condition of each blade specifically includes:

[0012] Several MEMS fiber optic load sensors are arranged at the root of each blade;

[0013] Calibrate the MEMS optical fiber load sensor to obtain a load coefficient of each MEMS optical fiber load sensor;

[0014] The blade root load of each blade is calculated according to the measurement values ​​of all the MEMS optical fiber load sensors and the load coefficient.

[0015] Optionally, while arranging a plurality of MEMS optical fiber load sensors at the blade root, a plurality of optical fiber temperature sensors are configured to perform temperature compensation on the MEMS optical fiber load sensors.

[0016] Optionally, the pitch angle of the blade is monitored by an acceleration sensor or an inclination sensor for measuring the inclination angle.

[0017] Optionally, the wind turbine independent pitch adjustment method further includes acquiring and displaying the current total wind input power value of the wind turbine in real time, specifically including:

[0018] Perform vector decomposition of the root load of each blade according to the rotor inclination angle and the pitch angle of each blade, and calculate the contribution of each blade to the instantaneous torque of the rotor;

[0019] Calculating the input wind power of each blade according to the contribution value of each blade and the rotation frequency information;

[0020] The total wind power input of the wind turbine is obtained by summing the wind power input of each blade.

[0021] Optionally, while judging whether the root loads of all blades are consistent based on the root load monitoring results of each blade, it is also judged whether the root load of each blade is overloaded based on the root load monitoring results of each blade. If overloaded, the current blade is independently pitched to reduce the load.

[0022] Based on this, the present invention also provides a wind turbine independent pitch adjustment system, comprising:

[0023] The blade root load monitoring module is used to monitor and obtain the blade root load of each blade of the wind turbine over a period of time;

[0024] a programming module connected to the blade root load monitoring module, configured to perform machine learning on the blade root load of each blade, determine the optimal pitch angle of each blade at each wind speed, and program the corresponding relationship between the blade root load and the optimal pitch angle into a control function;

[0025] a rotation frequency extraction module, connected to the blade root load monitoring module, for extracting rotation frequency information from the blade root load conditions of each blade monitored in real time, and determining whether the wind turbine is currently in a full power state based on the rotation frequency information;

[0026] a main controller connected to the blade root load monitoring module, the programming module, and the rotational frequency extraction module, and configured to perform independent pitch control on the current blade according to the blade root load monitoring result of each blade and the control function when the wind turbine is currently in a state of not fully generating;

[0027] The main controller is also used to determine whether the root loads of all blades are consistent based on the root load monitoring results of each blade when the wind turbine is currently in full power state. If they are inconsistent, the current blade is independently pitched in real time based on the root load monitoring results of the blade to ensure that the root loads of all blades remain consistent.

[0028] Optionally, the blade root load monitoring module includes a plurality of MEMS optical fiber load sensors arranged circumferentially at the blade root of each blade.

[0029] Optionally, the wind turbine independent pitch adjustment system further includes a plurality of optical fiber temperature sensors disposed near the MEMS optical fiber load sensor, and the optical fiber temperature sensors are used to perform temperature compensation on the MEMS optical fiber load sensor.

[0030] Optionally, the wind turbine independent pitch adjustment system further includes an acceleration sensor or an inclination sensor arranged at the root of the blade, and the acceleration sensor or the inclination sensor is used to monitor the pitch angle of the blade.

[0031] The present invention provides a method and system for adjusting independent pitch of a wind turbine, which has at least one of the following beneficial effects:

[0032] 1) By monitoring the blade root load in real time during wind turbine operation as a means of blade condition monitoring, a targeted independent pitch control strategy can be designed to keep the blades in the optimal efficiency state at all times, thereby improving the wind turbine's power generation;

[0033] 2) A load sensor based on MEMS fiber optic sensing technology is used to achieve real-time monitoring of blade root loads and enable long-term and accurate measurement;

[0034] 3) Based on the root load monitoring results of each blade, it is also possible to determine whether the root loads of all blades are consistent, and thus determine whether the wind turbine has an aerodynamic imbalance problem. Based on the root load monitoring results of the blades, the current blade is independently pitched in real time to ensure that the root loads of all blades remain consistent;

[0035] 4) Based on the root load monitoring results of each blade, it is also possible to determine whether the blade is overloaded so that the pitch can be changed in time to reduce the load. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Those skilled in the art will appreciate that the accompanying drawings are provided for a better understanding of the present invention and do not constitute any limitation to the scope of the present invention.

[0037] FIG1 is a flow chart of a method for adjusting independent pitch of a wind turbine according to an embodiment of the present invention;

[0038] FIG2 is a schematic diagram of a sensor arrangement according to an embodiment of the present invention;

[0039] FIG3 is a schematic diagram of an independent pitch adjustment system for a wind turbine provided by an embodiment of the present invention.

[0040] In the accompanying drawings: 1-blade; 2-MEMS fiber optic load sensor; 3-fiber optic temperature sensor; 4-tilt sensor; 10-blade root load monitoring module; 20-programming module; 30-rotation frequency extraction module; 40-main controller. DETAILED DESCRIPTION

[0041] According to the background technology, the wind speed monitoring of the wind turbine relies on a single anemometer on the top of the wind turbine hub, which makes it impossible to determine the actual wind speed of each blade during the operation of the wind turbine, and it is impossible to judge the optimal pitch angle of the blade. Therefore, it is difficult to adjust the pitch in a targeted manner, and it is impossible to ensure that the power of the blade is in the optimal state, which makes it difficult to maximize the power generation of the wind turbine.

[0042] Based on this, the present invention proposes a wind turbine independent pitch adjustment method and adjustment system. This method can monitor the status of the wind turbine blades in real time during wind turbine operation, thereby designing a targeted independent pitch adjustment strategy to achieve the goal of adjusting the blades to maintain optimal efficiency, thereby improving wind turbine power generation. Furthermore, the present invention uses real-time monitoring of blade root load as a means of blade status monitoring, and uses the real-time blade root load monitoring as a basis for designing independent pitch adjustment strategies.

[0043] While there are many studies and inventions on independent pitch control in the market, most of them are based on independent pitch control methods developed based on wind turbine aerodynamic simulation, real-time wind speed monitoring, and real-time monitoring of wind turbine master control SCADA data. The present invention, however, focuses on an independent pitch control method based on blade root load monitoring. In other words, the basis for independent pitch control in the present invention is blade root load monitoring, which is completely different from the basis for other independent pitch control methods in wind turbines.

[0044] In addition, many wind turbine health status monitoring methods based on blade root load monitoring are currently proposed on the market. The overall goal of these methods is to monitor whether the blades are damaged, whether there are overloads, lightning strikes, icing, and other health status problems. A large number of experiments and actual tests have been carried out to achieve these goals, and a relatively mature control strategy has been developed. The goal of these monitoring methods is to improve the safety of wind turbines, which is essentially different from the goal of improving wind turbine power generation in the present invention.

[0045] To make the objects, advantages, and features of the present invention more clearly apparent, the present invention is further described below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the drawings are all in a very simplified form and are not drawn to scale. They are only used to conveniently and clearly assist in illustrating the purposes of the embodiments of the present invention. In addition, the structures shown in the drawings are often part of the actual structure. In particular, different drawings may need to illustrate different focuses and sometimes use different scales.

[0046] The singular terms "one", "an" and "the" used in the present invention may include plural objects, unless the content clearly indicates otherwise. The term "or" used in the present invention is generally used to include the meaning of "and / or", unless the content clearly indicates otherwise. The term "several" used in the present invention is generally used to include the meaning of "at least one", unless the content clearly indicates otherwise. The term "at least two" used in the present invention is generally used to include the meaning of "two or more", unless the content clearly indicates otherwise. In addition, the terms "first", "second" and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second" and "third" may explicitly or implicitly include one or at least two of the features.

[0047] Please refer to Figure 1, which is a flow chart of a method for adjusting the independent pitch of a wind turbine according to an embodiment of the present invention. This embodiment provides a method for adjusting the independent pitch of a wind turbine, including:

[0048] S1. Monitor and obtain the blade root load of each wind turbine blade over a period of time and perform machine learning to determine the optimal pitch angle of each blade at each wind speed. The corresponding relationship between the blade root load and the optimal pitch angle is written into a control function.

[0049] S2. Real-time monitoring and acquisition of blade root load conditions of each blade, extraction of rotational frequency information therefrom, and determination of whether the wind turbine is currently in a full power state based on the rotational frequency information;

[0050] S3. If the wind turbine is currently in an under-powered state, independently changing the pitch of the current blade according to the blade root load monitoring result of each blade and the control function;

[0051] S4. If the wind turbine is currently in full power state, determine whether the root loads of all blades are consistent based on the root load monitoring results of each blade. If they are inconsistent, perform independent pitch operation on the current blade in real time based on the root load monitoring results of the blade to keep the root loads of all blades consistent.

[0052] Specifically, step S1 is first executed to monitor and obtain the blade root load of each blade of the wind turbine over a period of time and perform machine learning to determine the optimal pitch angle of each blade at each wind speed, and the corresponding relationship between the blade root load and the optimal pitch angle is written into a control function.

[0053] In conjunction with FIG2 , the steps of monitoring and obtaining the root load of each blade specifically include:

[0054] Several MEMS optical fiber load sensors 2 are arranged at the root of each blade 1;

[0055] Calibrate the MEMS optical fiber load sensor 2 to obtain the load coefficient of each MEMS optical fiber load sensor 2;

[0056] The blade root load of each blade 1 is calculated based on the measurement values ​​of all the MEMS optical fiber load sensors 2 and the load coefficient.

[0057] This embodiment uses real-time monitoring of the root load of blade 1 as a means of blade status monitoring, and designs an independent pitch control strategy based on the real-time load monitored at the root of blade 1, thereby achieving the purpose of increasing wind turbine power generation through independent pitch control. However, the conditions for sensors that can be used for blade root load monitoring are very demanding. On the one hand, as a wind turbine is a power generation device, its blades 1 cannot use live equipment, otherwise it is extremely easy to cause safety failures. Therefore, the sensors used must be passive devices that are not powered. On the other hand, during the operation of the wind turbine blade 1, real-time independent pitch control is required, so load monitoring must be carried out in real time and be able to capture subtle load changes at the root of blade 1. This means that indicators such as the sensitivity of the load sensor must meet very high requirements.

[0058] Based on this, this embodiment uses a MEMS fiber optic load sensor 2 as the blade root load monitoring sensor. MEMS fiber optic sensing technology is a 21st-century cutting-edge technology based on micro- and nanomechanics and optics. Its magnetic block, elastic support, optical reflective micromirror, and light input and output waveguide systems are all directly integrated on a tiny chip, enabling true all-optical detection and transmission of signals such as current. The resulting MEMS chip boasts a compact, all-in-one package, excellent parameter consistency, high sensitivity, a wide dynamic range, and excellent linearity. Furthermore, its phase changes linearly, resulting in stable and reliable performance.

[0059] The silicon-based sensitive structure of the MEMS chip is manufactured using micro-electromechanical (MEMS) technology, and the signal is detected and read using fiber optic detection technology. Therefore, it combines the advantages of both MEMS and fiber optic sensing technologies. Furthermore, MEMS fiber optic sensing technology overcomes the constraints of existing sensing technologies, which often require high bandwidth and high precision. It offers passive, wide temperature ranges, miniaturization, electromagnetic interference resistance, portability, ease of networking, and maintenance-free features, fully meeting the monitoring requirements of this application.

[0060] In this embodiment, referring to FIG2 , the wind turbine includes three blades 1. This embodiment is described using one of the blades 1 as an example. Four MEMS fiber optic load sensors 2 are evenly distributed on a circular cross-section near the root of the blade 1 (e.g., approximately 1.5 to 1.8 meters from the root). These MEMS fiber optic load sensors 2 can monitor the root load at the root of the blade 1 in real time. Of course, this application does not impose any restrictions on the number and distribution of the MEMS fiber optic load sensors 2, and they can be adjusted according to actual conditions.

[0061] After installation, the MEMS fiber optic load sensor 2 is calibrated to obtain the load coefficient of each MEMS fiber optic load sensor 2 , and then the blade root load of each blade 1 is calculated based on the measured values ​​of all the MEMS fiber optic load sensors 2 and the blade root load coefficient.

[0062] Furthermore, while arranging a plurality of MEMS fiber optic load sensors 2 at the root of the blade 1, a plurality of fiber optic temperature sensors 3 are configured to perform temperature compensation on the MEMS fiber optic load sensors 2 to reduce the influence of ambient temperature changes on the blade root load measurement results and ensure the accuracy of the data.

[0063] In this embodiment, the number of the optical fiber temperature sensors 3 is between 1 and 4. That is, one optical fiber temperature sensor 3 can be used to compensate one or more MEMS optical fiber load sensors 2 according to actual needs. This application does not impose specific restrictions on this. In this embodiment, the optical fiber temperature sensor 3 is an optical fiber temperature sensor 3.

[0064] After installing the MEMS fiber optic load sensor 2, first collect the root load conditions of each blade 1 over a period of time (such as 1000 hours) and perform machine learning. The load conditions need to cover as many wind speed values ​​as possible, which are usually between 3-13m / s. Then, find the optimal position of the wind turbine operation at each wind speed (that is, the optimal pitch angle of each blade 1), and use this as a basis to write a control function to guide the subsequent adjustment of the pitch angle of each blade 1.

[0065] It should be understood that each blade 1 has an optimal power value under different loads, that is, the value with the maximum power generation, and each optimal power value corresponds to an optimal pitch angle. Therefore, the optimal pitch angle corresponding to each blade 1 under different loads can be obtained by machine learning. Of course, this can also be achieved through simulation, which is not limited in this application.

[0066] Specifically, by performing vector decomposition on the root load of blade 1 based on the rotor inclination angle and the pitch angle of each blade 1, the contribution value of each blade 1 to the instantaneous torque of the rotor can be calculated. Then, based on the contribution value of each blade 1 and the rotational frequency information, the input wind power of each blade 1 can be calculated, thereby establishing a corresponding relationship between blade root load, input wind power, and pitch angle. The rotational frequency information mentioned here can be understood as the rotational speed of the blade 1, which can be extracted from the root load condition based on discrete Fourier transform.

[0067] In this embodiment, the pitch angle of blades 1 is monitored using an accelerometer or tilt sensor 4 that measures inclination. This allows for simultaneous monitoring of the attitude of blades 1 with the MEMS fiber optic load sensor 2, improving system reliability. Furthermore, the accelerometer or tilt sensor 4 can also monitor the azimuth angle of blades 1, which is used to calculate the instantaneous torque exerted by each blade 1 on the rotor.

[0068] Next, step S2 is executed to monitor and obtain the root load of each blade 1 in real time, extract the rotation frequency information therefrom, and determine whether the wind turbine is currently in a full-power state based on the rotation frequency information.

[0069] If the wind turbine is currently in an under-generated state, step S3 is executed to perform independent pitch control on the current blade 1 according to the monitoring results of the blade root load of each blade 1 and the control function. Specifically, when the wind turbine is currently in an under-generated state, the blade root load of each blade 1 can be vector decomposed according to the rotor inclination angle and the pitch angle of each blade 1, and the contribution value of each blade 1 to the instantaneous torque of the rotor can be calculated. Then, the input wind power of each blade 1 can be calculated by multiplying the contribution value of each blade 1 by the rotation speed. Then, based on this information and in conjunction with the control function, the pitch angle of the current blade 1 is adjusted so that when the blade 1 is at different positions in the rotor rotation plane, the maximum torque in the rotor rotation plane can be obtained by adjusting the pitch angle, thereby ensuring that the input wind power of the blade 1 is in the optimal state, which is equivalent to increasing the power generation power of the wind turbine, thereby increasing the power generation capacity of the wind turbine.

[0070] Furthermore, the independent pitch adjustment method of the wind turbine also includes real-time acquisition and display of the total input wind power value of the current wind turbine. After the input wind power of each blade 1 is calculated according to the above method, the input wind power of each blade 1 can be directly summed to obtain the total input wind power value of the wind turbine.

[0071] If the wind turbine is currently in full power, step S4 is executed to determine whether the root loads of all blades 1 are consistent based on the root load monitoring results of each blade 1. If they are inconsistent, the current blade 1 is independently pitched in real time based on the root load monitoring results of the blade 1, so that the root loads of all blades 1 remain consistent. In this embodiment, whether the root loads of all blades 1 are consistent can be determined based on the amplitude and periodic information of the root loads. If they are inconsistent, it indicates that the wind turbine has an aerodynamic imbalance problem. In this case, the blade 1 with inconsistent root load can be independently pitched in real time based on the root load monitoring results of the blade 1, and the pitch angle of the blade 1 can be adjusted to ensure that the root loads of all blades 1 remain consistent. In addition, the root load monitoring results can also be used to determine whether the root load is in a risk state such as overload. If a blade 1 is overloaded, the pitch angle of the blade 1 can be adjusted to reduce the load of the blade 1. By reducing the load, the wind turbine operating time is increased, thereby increasing power generation.

[0072] Based on this, please refer to FIG3 and combine FIG1-FIG2 , the present invention also provides a wind turbine independent pitch adjustment system, including:

[0073] The blade root load monitoring module 10 is used to monitor and obtain the blade root load of each blade 1 of the wind turbine over a period of time;

[0074] a programming module 20 connected to the blade root load monitoring module 10, configured to perform machine learning on the blade root load of each blade 1, determine the optimal pitch angle of each blade 1 at each wind speed, and program the corresponding relationship between the blade root load and the optimal pitch angle into a control function;

[0075] The rotation frequency extraction module 30 is connected to the blade root load monitoring module 10 and is used to extract the rotation frequency information from the blade root load of each blade 1 monitored in real time, and determine whether the wind turbine is currently in a full power state based on the rotation frequency information;

[0076] a main controller 40 connected to the blade root load monitoring module 10, the programming module 20, and the rotational frequency extraction module 30, for performing an independent pitch control operation on the current blade 1 according to the blade root load monitoring result of each blade 1 and the control function when the wind turbine is currently in a state of not fully generating;

[0077] The main controller 40 is further configured to, when the wind turbine is currently in full power, determine whether the root loads of all blades 1 are consistent based on the root load monitoring results of each blade 1. If not, the main controller 40 is configured to independently change the pitch of the current blade 1 in real time based on the root load monitoring results of the blade 1 to maintain consistency of the root loads of all blades 1. Furthermore, the main controller 40 can also determine whether the root load is in a risk state, such as overload, based on the root load monitoring results. If a blade 1 is overloaded, the main controller 40 can adjust the pitch angle of that blade 1 to reduce the load on that blade 1. This load reduction increases the operating time of the wind turbine and improves power generation.

[0078] The blade root load monitoring module 10 includes a plurality of MEMS fiber optic load sensors 2 arranged circumferentially at the root of the blade 1. In this embodiment, in conjunction with FIG2 , the wind turbine includes three blades 1. This embodiment is described by taking one of the blades 1 as an example. Four MEMS fiber optic load sensors 2 are evenly distributed on the circular cross section near the root of the blade 1 (for example, about 1.5m to 1.8m from the root). These MEMS fiber optic load sensors 2 can monitor the root load at the root of the blade 1 in real time. Of course, this application does not impose any restrictions on the number and distribution of the MEMS fiber optic load sensors 2, and they can be adjusted according to actual conditions.

[0079] After installation, the MEMS fiber optic load sensor 2 needs to be calibrated to obtain the load coefficient of each MEMS fiber optic load sensor 2, and then the bending moment load on the blade 1 is calculated based on the measurement values ​​of all the MEMS fiber optic load sensors 2 and the blade root load coefficient.

[0080] The wind turbine independent pitch adjustment system also includes several fiber optic temperature sensors positioned near the MEMS fiber optic load sensor 2. These fiber optic temperature sensors are used to perform temperature compensation on the MEMS fiber optic load sensor 2 to reduce the impact of ambient temperature changes on blade root load measurement results and ensure data accuracy. In this embodiment, the number of fiber optic temperature sensors 3 ranges from 1 to 4. In other words, one fiber optic temperature sensor 3 can be used to compensate one or more MEMS fiber optic load sensors 2 based on actual needs, and this application does not impose specific limitations on this. In this embodiment, the fiber optic temperature sensor 3 is a fiber optic temperature sensor.

[0081] In this embodiment, the wind turbine independent pitch adjustment system further includes an acceleration sensor or an inclination sensor 4 arranged at the root of the blade 1 , and the acceleration sensor or the inclination sensor 4 is used to monitor the pitch angle and azimuth angle of the blade 1 .

[0082] In summary, the embodiments of the present invention provide a method and system for independent pitch adjustment of a wind turbine, which monitors the blade root load in real time during the operation of the wind turbine as a means of monitoring the blade status and then designs an independent pitch strategy in a targeted manner, so as to adjust the blade pitch angle so that it is always in the optimal efficiency state and improve the power generation of the wind turbine.

[0083] The above description is merely a preferred embodiment of the present invention and does not limit the present invention in any way. Any person skilled in the art who, without departing from the scope of the present invention, makes any equivalent substitution, modification, or other changes to the technical solution and technical content disclosed in the present invention shall be deemed to be within the scope of the present invention and still fall within the scope of protection of the present invention.

Claims

1. A method for adjusting the independent pitch of a wind turbine, characterized in that: include: Monitor and obtain the blade root load of each wind turbine blade over a period of time and perform machine learning to determine the optimal pitch angle for each blade at each wind speed. The corresponding relationship between the blade root load and the optimal pitch angle is then written into a control function. Monitor and obtain the root load of each blade in real time, extract the rotational frequency information therefrom, and determine whether the wind turbine is currently in a full power state based on the rotational frequency information; If the wind turbine is currently in an under-powered state, performing independent pitch control on the current blade according to the blade root load monitoring result of each blade and the control function; If the wind turbine is currently in full power state, it is determined whether the root loads of all blades are consistent based on the root load monitoring results of each blade. If the root loads are inconsistent, the current blade is independently pitched in real time based on the root load monitoring results of the blade to ensure that the root loads of all blades remain consistent.

2. The method for adjusting the independent pitch of a wind turbine according to claim 1, wherein: The steps of monitoring and obtaining the root load condition of each blade specifically include: Several MEMS fiber optic load sensors are arranged at the root of each blade; Calibrate the MEMS optical fiber load sensor to obtain a load coefficient of each MEMS optical fiber load sensor; The blade root load of each blade is calculated according to the measurement values ​​of all the MEMS optical fiber load sensors and the load coefficient.

3. The method for adjusting the independent pitch of a wind turbine according to claim 2, wherein: While arranging a plurality of MEMS optical fiber load sensors at the blade root, a plurality of optical fiber temperature sensors are configured to perform temperature compensation on the MEMS optical fiber load sensors.

4. The method for adjusting the independent pitch of a wind turbine according to claim 1, wherein: The pitch angle of the blade is monitored by an acceleration sensor or an inclination sensor for measuring the inclination angle.

5. The method for adjusting the independent pitch of a wind turbine according to claim 1, wherein: The wind turbine independent pitch adjustment method further includes obtaining and displaying the current total wind input power value of the wind turbine in real time, specifically including: According to the wind wheel inclination angle and the pitch angle of each blade, the root load of each blade is vector decomposed and calculated. The contribution of each blade to the instantaneous torque of the wind rotor; Calculating the input wind power of each blade according to the contribution value of each blade and the rotation frequency information; The total wind power input of the wind turbine is obtained by summing the wind power input of each blade.

6. The method for adjusting the independent pitch of a wind turbine according to claim 1, wherein: While judging whether the blade root loads of all blades are consistent based on the blade root load monitoring results of each blade, it is also judged whether the blade root loads of each blade are overloaded based on the blade root load monitoring results of each blade. If overloaded, the current blade is independently pitched to reduce the load.

7. A wind turbine independent pitch adjustment system, characterized in that: include: The blade root load monitoring module is used to monitor and obtain the blade root load of each fan blade over a period of time: a programming module connected to the blade root load monitoring module, configured to perform machine learning on the blade root load of each blade, determine the optimal pitch angle of each blade at each wind speed, and program the corresponding relationship between the blade root load and the optimal pitch angle into a control function; a rotation frequency extraction module, connected to the blade root load monitoring module, for extracting rotation frequency information from the blade root load conditions of each blade monitored in real time, and determining whether the wind turbine is currently in a full power state based on the rotation frequency information; a main controller connected to the blade root load monitoring module, the programming module, and the rotational frequency extraction module, and configured to perform independent pitch control on the current blade according to the blade root load monitoring result of each blade and the control function when the wind turbine is currently in a state of not fully generating; The main controller is also used to determine whether the root loads of all blades are consistent based on the root load monitoring results of each blade when the wind turbine is currently in full power state. If they are inconsistent, the current blade is independently pitched in real time based on the root load monitoring results of the blade to ensure that the root loads of all blades remain consistent.

8. The wind turbine independent pitch adjustment system according to claim 7, characterized in that: The blade root load monitoring module includes a plurality of MEMS optical fiber load sensors arranged circumferentially at the blade roots of each blade.

9. The wind turbine independent pitch adjustment system according to claim 8, characterized in that: The wind turbine independent pitch adjustment system further includes a plurality of optical fiber temperature sensors arranged near the MEMS optical fiber load sensor, and the optical fiber temperature sensors are used to perform temperature compensation on the MEMS optical fiber load sensor.

10. The wind turbine independent pitch adjustment system according to claim 8, characterized in that: The wind turbine independent pitch adjustment system further includes an acceleration sensor or an inclination sensor arranged at the root of the blade, and the acceleration sensor or the inclination sensor is used to monitor the pitch angle of the blade.