Individual pitch adjustment method and adjustment system for wind turbine

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 cond

Description

TECHNICAL FIELD

[01] The present disclosure relates to the technical field of wind power generation, in particular to an individual pitch adjustment method and adjustment system for a wind turbine. BACKGROUND ART

[02] Currently, in the design of mainstream wind turbines, pitch angles of three blades are synchronously adjusted according to wind speeds monitored in real time, that is, during the operating process of the wind turbine, the pitch angles of the three blades are adjusted in real time based on the wind speed, with adjusted angles of the three blades being consistent. The pitch adjusting method has the advantages that the adjusting strategy is simple and convenient, operation is easy, and the normal operation and efficiency control of the wind turbines can be effectively ensured within a certain error range. However, due to the increasing sizes of the wind turbines, the discrepancy in wind speed distribution across a rotor rotation plane becomes increasingly apparent, for example, for large-scale wind turbines, the disparity in the wind speeds between a top and bottom of a rotor surface can reach 3-5 m / s, such that the discrepancy among the wind speeds borne by the three blades of the wind turbine is larger at different heights of the rotor rotation plane, and thus, the optimal pitch angles of the corresponding blades at different positions in the rotor rotation plane are large in difference. Therefore, the requirements of individual pitch operation according to different positions of the rotor rotation plane where the blades are located have been more and more increased.

[03] However, the monitoring of the wind speed of the wind turbine depends on a single anemometer mounted on a top of a hub of the wind turbine, which leads to failure in determining the actual wind speed at which each blade is located during the operating process of the wind turbine, and consequently, precluding an evidence-based determination of the optimal pitch angle adjustment for each blade. Therefore, it is challenging to perform targeted pitch adjustment, and further, it is impossible to ensure that the blade power is in an optimal state, resulting in the inability to maximize the generating capacity of the wind turbine. SUMMARY

[04] The objective of the present disclosure is to provide an individual pitch adjustment method and adjustment system for a wind turbine, which can implement the targeted design of an individual pitch strategy, such that blades can be always in an optimal efficiency state, thereby increasing the generating capacity of the wind turbine.

[05] In order to fulfill the above objective, the present disclosure provides an individual pitch adjustment method for a wind turbine, including the following steps:

[06] monitoring and obtaining a blade root load condition of each blade of the wind turbine within a period of time, performing machine learning, determining the blade root load and optimal pitch angle of each blade at a finite number of wind speeds within a predefined wind speed range, and writing a correspondence between the blade root load and the optimal pitch angle into a control function;

[07] 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 turbine is currently in a fullpower state; 16 01 25

[08] if the wind turbine 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

[09] if the wind turbine 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, such that the consistency of the blade root loads of all the blades is maintained.

[10] Optionally, the monitoring and acquiring a blade root load condition of each blade specifically includes the following steps:

[11] arranging a plurality of micro-electromechanical systems (MEMS) fiber-optic load sensors at a blade root of each blade;

[12] calibrating the MEMS fiber-optic load sensors to obtain a load coefficient of each MEMS fiber-optic load sensor; and

[13] calculating the blade root load of each blade according to measured values of all the MEMS fiber-optic load sensors and the load coefficients.

[14] Optionally, while arranging the plurality of MEMS fiber-optic load sensors at the blade root of each blade, a plurality of fiber-optic temperature sensors are arranged to perform temperature compensation on the MEMS fiber-optic load sensors.

[15] Optionally, the pitch angle of the blade is monitored by an acceleration sensor or a tilt angle sensor for measuring a rotor tilt angle.

[16] Optionally, the individual pitch adjustment method for the wind turbine further includes the step: obtaining and displaying a current total input wind power value of the wind turbine in real time, which specifically includes the following steps:

[17] performing vector decomposition on the blade root load of each blade according to a rotor tilt angle and the pitch angle of each blade, and calculating a contribution value of an instantaneous torque exerted on the rotor by each blade;

[18] calculating input wind power of each blade according to the contribution value and the rotating frequency information of each blade; and

[19] summing the input wind power of each blade to obtain the total input wind power value of the wind turbine.

[20] Optionally, while determining whether the blade root loads of all the blades are consistent according to the blade root load monitoring result of each blade, whether the blade root load of each blade is overloaded is determined according to the blade root load monitoring result of each blade, and if each blade is overloaded, the individual pitch operation is performed on the current blade for load reduction.

[21] Based on this, the present disclosure further provides an individual pitch adjustment system for a wind turbine, including:

[22] a blade root load monitoring module, configured to monitor and obtain a blade root load condition of each blade of the wind turbine within a period of time; 16 01 25

[23] a programming module, connected with the blade root load monitoring module, and configured to perform machine learning on the blade root load condition of each blade, determine the blade root load and optimal pitch angle of each blade at a finite number of wind speeds within a predefined wind speed range, and write a correspondence between the blade root load and the optimal pitch angle into a control function;

[24] a rotating frequency extraction module, connected with the blade root load monitoring module, and configured to extract rotating frequency information from the blade root load condition of each blade that is monitored in real time, and according to the rotating frequency information, determine whether the wind turbine is currently in a full-power state; and

[25] a master controller, connected with the blade root load monitoring module, the programming module, and the rotating frequency extraction module, and configured to perform an individual pitch operation on the current blade according to a blade root load monitoring result of each blade and the control function if the wind turbine is currently in a non-full-power state; wherein

[26] the master controller is further configured to determine whether the blade root loads of all the blades are consistent according to the blade root load monitoring result of each blade if the wind turbine is currently in the full-power state, and if not, to perform the individual pitch operation on the current blade in real time according to the blade root load monitoring result of the corresponding blade, such that the consistency of the blade root loads of all the blades is maintained.

[27] Optionally, the blade root load monitoring module includes a plurality of MEMS fiberoptic load sensors that are circumferentially arranged at the blade root of each blade.

[28] Optionally, the individual pitch adjustment system for the wind turbine further includes a plurality of fiber-optic temperature sensors disposed near the MEMS fiber-optic load sensors, wherein the fiber-optic temperature sensors are configured to perform temperature compensation on the MEMS fiber-optic load sensors.

[29] Optionally, the individual pitch adjustment system for the wind turbine further includes an acceleration sensor or a tilt angle sensor arranged at a blade root of each blade, wherein the acceleration sensor or the tilt angle sensor is configured to monitor the pitch angle of the blade.

[30] The present disclosure provides the individual pitch adjustment method and adjustment system for the wind turbine, which at least have one of the following beneficial effects:

[31] 1) the blade root loads of the blades are monitored in real time during the operating process of the wind turbine as a means for monitoring the states of the blades to perform the targeted design of the individual pitch strategy, such that the blades are always in the optimal efficiency state, thus increasing the generating capacity of the wind turbine;

[32] 2) the load sensors based on the MEMS fiber-optic sensing technology are adopted, such that the real-time monitoring of the blade root loads is realized, thus achieving long-term accurate measurement;

[33] 3) based on the blade root load monitoring result of each blade, whether the root loads of all the blades are consistent can be determined, which, in turn, allows for the determination of any aerodynamic imbalance in the wind turbine. Furthermore, the individual pitch operation 16 01 25 is performed in real time on the current blade according to the blade root load monitoring result of the blade, in order to maintain the consistency of the blade root loads of all the blades; and

[34] 4) based on the blade root load monitoring result of each blade, whether any blade is overloaded can be determined so as to perform the pitch operation in time for load reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[35] Those ordinary skilled in the art will appreciate that the drawings are provided for a better understanding of the present disclosure, and are not intended to limit the scope of the present disclosure. Wherein

[36] FIG. 1 is a flowchart illustrating an individual pitch adjustment method for a wind turbine according to an embodiment of the present disclosure;

[37] FIG. 2 is a schematic diagram illustrating a distribution of sensors according to an embodiment of the present disclosure; and

[38] FIG. 3 is a schematic diagram illustrating an individual pitch adjustment system for a wind turbine according to an embodiment of the present disclosure.

[39] In the appended drawings:

[40] 1 -blade; 2-MEMS fiber-optic load sensor; 3-fiber-optic temperature sensor; 4-tilt angle sensor; 10-blade root load monitoring module; 20-programming module; 30-rotating frequency extraction module; and 40-master controller. DETAILED DESCRIPTION OF THE EMBODIMENTS

[41] As mentioned in the background, the monitoring of a wind speed of a wind turbine depends on a single anemometer mounted on a top of a hub of the wind turbine, which leads to failure in determining the actual wind speed at which each blade is located during the operating process of the wind turbine, and consequently, precluding an evidence-based determination of the optimal pitch angle adjustment for each blade. Therefore, it is challenged to perform targeted pitch adjustment, and further, it is impossible to ensure that the blade power is in an optimal state, resulting in the inability to maximize the generating capacity of the wind turbine.

[42] Based on this, the present disclosure provides an individual pitch adjustment method and adjustment system for a wind turbine, which can monitor states of blades of the wind turbine in real time during the operating process of the wind turbine, such that the targeted design of an individual pitch strategy is performed, where the blades are adjusted to be always in the optimal efficiency state, thus increasing the generating capacity of the power turbine. Further, according to the present disclosure, blade root loads of the blades are monitored in real time as a means for monitoring the states of the blades, and the individual pitch strategy is designed based on the loads monitored in real time at blade roots of the blades.

[43] Despite a plurality of researches and disclosures pertaining to individual pitch in the current market, existing methods are essentially individual pitch methods developed based on aerodynamic-related simulation of the wind turbine, real-time monitoring of the wind speed of the wind turbine, and real-time monitoring of Supervisory Control and Data Acquisition (SCADA) data from a master controller for the wind turbine. The present disclosure focuses on an individual pitch method based on the monitoring of the blade root loads of the blades. That is, the individual pitch adjustment in the present disclosure is implemented based on the 16 01 25 monitoring of the blade root loads of the blades, which is completely different from the basis of the individual pitch operation of other wind turbines.

[44] Additionally, at present, many health state monitoring methods for the wind turbine based on the monitoring of the blade root loads are provided in the market, with their overall objectives to monitor whether the blades are damaged, overloaded, struck by lightning, or ice-coated, and compromised by other health-related problems. A large number of experiments and practical tests have been conducted to develop a set of relatively mature control strategies with respect to the above problems. The objective of these monitoring methods is to enhance the safety of the wind turbine, which is fundamentally different from the objective of the present disclosure, which focuses on increasing the generating capacity of the wind turbine.

[45] To further clarify the objectives, advantages and features of the present disclosure, the present disclosure will be described in detail with reference to accompanying drawings and specific embodiments. It is to be noted that the accompanying drawings are in a greatly simplified form and are not to scale, but are merely intended to facilitate and clarify the explanation of the embodiments of the present disclosure. Furthermore, structures illustrated in the accompanying drawings are often part of actual structures. Particularly, the accompanying drawings may have different emphasis points and may sometimes be scaled differently.

[46] As used herein, singular forms "a", "an" and "the" include plural referents, unless otherwise clearly dictated in the content. As used herein, the term "or" is generally employed in its sense including "and / or", unless otherwise clearly dictated in the content. As used herein, the term "a plurality of is generally employed in its sense including "at least one", unless otherwise clearly dictated in the content. As used herein, the term "at least two" is generally employed in its sense including "two or more", unless otherwise clearly dictated in the content. In addition, the terms "first", "second", and "third" are only for description, and shall not be understood as indication or implication of relative importance or implicit indication of the number of indicated technical features. Thus, the features limited with "first", "second", and "third” can explicitly or implicitly include one or at least two of the features.

[47] Referring to FIG. 1, FIG. 1 is a flowchart illustrating an individual pitch adjustment method for a wind turbine according to an embodiment of the present disclosure. This embodiment provides an individual pitch adjustment method for a wind turbine, including the following steps:

[48] SI, a blade root load condition of each blade of the wind turbine within a period of time is monitored and obtained, machine learning is performed, the blade root load and optimal pitch angle of each blade are determined at a finite number of wind speeds within a predefined wind speed range, and a correspondence between the blade root load and the optimal pitch angle is written into a control function;

[49] S2, the blade root load condition of each blade is monitored and obtained in real time, rotating frequency information is extracted from the blade root load condition, and according to the rotating frequency information, whether the wind turbine is currently in a full-power state is determined;

[50] S3, if the wind turbine is currently in a non-full-power state, an individual pitch operation is performed on the current blade according to a blade root load monitoring result of each blade and the control function; and

[51] S4, if the wind turbine is currently in the full-power state, according to the blade root load monitoring result of each blade, whether the blade root loads of all the blades are consistent 16 01 25 is determined, and if not, the individual pitch operation is performed on the current blade in real time according to the blade root load monitoring result of the blade, such that the consistency of the blade root loads of all the blades is maintained.

[52] Specifically, step SI is executed first, the blade root load condition of each blade of the wind turbine within a period of time is monitored and obtained, the machine learning is performed, the blade root load and optimal pitch angle of each blade are determined at a finite number of wind speeds within a predefined wind speed range, and the correspondence between the blade root load and the optimal pitch angle is written into the control function.

[53] Wherein referring to FIG. 2, the monitoring and acquiring a blade root load condition of each blade specifically includes the following steps:

[54] a plurality of MEMS fiber-optic load sensors 2 are arranged at a blade root of each blade 1;

[55] the MEMS fiber-optic load sensors 2 are calibrated to obtain a load coefficient of each MEMS fiber-optic load sensor 2; and

[56] the blade root load of each blade 1 is calculated according to measured values of all the MEMS fiber-optic load sensors 2 and the load coefficients.

[57] According to this embodiment, the blade root load of each blade 1 is monitored in real time as a means for monitoring the state of the blade, and an individual pitch strategy is designed based on the load monitored in real time at the blade root of the blade 1, thus fulfilling the objective of increasing the generating capacity of the wind turbine via individual pitch. However, the conditions of the sensors for monitoring the blade root loads are very harsh, on one hand, the wind turbine is used as a power generation apparatus, and electrified equipment cannot be connected to the blade 1 of the wind turbine, as this could easily lead to safety faults. Therefore, the sensors employed must be non-electrified passive devices. On the other hand, during the operating process of the blades 1 of the wind turbine, real-time individual pitch is required. Therefore, load monitoring must be performed in real time, with the ability to capture subtle changes in loads at the blade roots of the blades 1, meaning that the load sensors must meet the requirements of very high sensitivity and other indicators.

[58] Based on this, according to this embodiment, the MEMS fiber-optic load sensors 2 are used as sensors for monitoring the blade root loads, and an MEMS fiber-optic sensing technology is an advanced 21st-century technology based on micro / nano mechanics and optics. Magnetic blocks, elastic support bodies, optical reflective micro-mirrors, and optical incident and emergent waveguide systems of such a technology are all directly integrated into a tiny chip, truly achieving all-optical detection and transmission of signals such as currents. The MEMS chip manufactured has the advantages of compact structure, integrated packaging, good parameter consistency, high sensitivity, large dynamic range, good linearity, and stable and reliable performance, with a phase being linearly changed.

[59] A silicon-based sensitive structure of the MEMS chip is integrated and manufactured using a micro-electromechanical technology, with signals detected and read through a fiberoptic detection technology. Therefore, the MEMS chip combines the advantages of both an MEMS sensing technology and a fiber-optic sensing technology. Additionally, the MEMS fiberoptic sensing technology overcomes the mutual restriction of “broadband” and “high precision” in the existing sensing technology, is characterized by passivity, wide temperature range, miniaturization, electromagnetic interference resistance, portability, easy networking and maintenance-free, and completely meets the monitoring requirements of the present application. 16 01 25

[60] According to this embodiment, referring to FIG. 2, the wind turbine includes three blades 1. Taking one of the blades 1 as an example for description, 4 MEMS fiber-optic load sensors 2 are uniformly distributed on a circular cross section (for example, a position approx. 1.5 m to 1.8 m away from the blade root) near the blade root of the blade 1, and the MEMS fiber-optic load sensors 2 may monitor the blade root load at the blade root of the blade 1 in real time. Of course, the number and distribution method of the MEMS fiber-optic load sensors 2 are not limited in the present application, which can be adjusted according to actual situations.

[61] After being mounted, the MEMS fiber-optic load sensors 2 are 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 according to the measured values of all the MEMS fiber-optic load sensors 2 and the corresponding blade root load coefficient.

[62] Further, while arranging the plurality of MEMS fiber-optic load sensors 2 at the blade root of each blade 1, a plurality of fiber-optic temperature sensors 3 are arranged to perform temperature compensation on the MEMS fiber-optic load sensors 2, such that the impact of changes in environmental temperature on the measurement results of the blade root loads is relieved, thereby ensuring data accuracy.

[63] According to this embodiment, the number of the fiber-optic temperature sensors 3 is between 1 and 4, that is, 1 fiber-optic temperature sensor 3 may be used to compensate one or more MEMS fiber-optic load sensors 2 according to actual requirements, which is not specifically limited in the present application. According to this embodiment, the fiber-optic temperature sensor 3 is a fiber-optical temperature sensor 3.

[64] After the MEMS fiber-optic load sensors 2 are mounted, the blade root load condition of each blade 1 is collected within a period of time (for example, 1,000 h) and meanwhile, machine learning is performed. The load condition needs to cover the loads at as many wind speed values as possible, with the wind speed typically falling within a range of 3 to 13 m / s. The optimal position where the wind turbine operates at a finite number of wind speeds (i.e., the optimal pitch angle of each blade 1) is then searched, and the control function is written based on the optimal position to guide subsequent adjustment in the pitch angle of each blade

[65] It should be understood that each blade 1 has an optimal power value, i.e., a value at which the generating capacity is maximal, at different loads, and each optimal power value corresponds to one optimal pitch angle, such that the corresponding optimal pitch angle for each blade 1 at different loads can be obtained via machine learning. Of course, the method can also be implemented in a simulation manner, which is not limited to the present application.

[66] Specifically, the blade root load of each blade 1 is subjected to vector decomposition according to the rotor tilt angle and the pitch angle of each blade 1, such that the contribution value of the instantaneous torque exerted on the rotor by each blade 1 can be calculated, and then input wind power of each blade 1 can be calculated according to the contribution value and the rotating frequency information of each blade 1, such that the correspondence among the blade root load, the input wind power and the pitch angle can be established. It can be understood that the rotating frequency information mentioned here refers to a rotational speed of each blade 1, which can be extracted from the blade root load condition according to discrete Fourier transformation.

[67] According to this embodiment, the pitch angle of each blade 1 is monitored by the acceleration sensor or the tilt angle sensor 4 for measuring the rotor tilt angle, such that an attitude of the blade 1 can be synchronously monitored by matching with the MEMS fiber-optic 16 01 25 load sensors 2, thereby improving the reliability of the system. Furthermore, the acceleration sensor or the tilt angle sensor 4 can also monitor an azimuth angle of the blade 1 for calculating the instantaneous torque exerted on the rotor by each blade 1.

[68] Step S2 is then executed, that is, the blade root load condition of each blade 1 is monitored and obtained in real time, the rotating frequency information is extracted from the blade root load condition, and according to the rotating frequency information, whether the wind turbine is currently in the full-power state is determined.

[69] If the wind turbine is currently in the non-full-power state, step S3 is executed, and the individual pitch operation on the current blade 1 is performed according to the blade root load monitoring result of each blade 1 and the control function. Specifically, when the wind turbine is currently in the non-full-power state, the blade root load of each blade 1 is subjected to vector decomposition according to the rotor tilt angle and the pitch angle of each blade 1, such that the contribution value of the instantaneous torque exerted on the rotor by each blade 1 can be calculated, and the input wind power of each blade 1 can be then calculated by multiplying the contribution value of each blade 1 by the rotational speed of the blade, and then the pitch angle of the current blade 1 is adjusted by combining with the control function based on the information, such that when the blade 1 is positioned at different positions in the rotor rotation plane, the maximum torque in the rotor rotation plane can be obtained through an adjustment in the pitch angle, thus ensuring that the input wind power of the blade 1 is in the optimal state, that is, generated power of the wind turbine is increased, thus improving the generating capacity of the wind turbine.

[70] Further, the individual pitch adjustment method for the wind turbine further includes the step that the current total input wind power value of the wind turbine is obtained and displayed in real time, and 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.

[71] If the wind turbine is currently in the full-power state, step S4 is executed, according to the blade root load monitoring result of each blade 1, whether the blade root loads of all the blades 1 are consistent is determined, and if not, the individual pitch operation is performed on the current blade 1 in real time according to the blade root load monitoring result of the blade 1, such that the blade root loads of all the blades 1 are maintained consistent. According to this embodiment, whether the blade root loads of all the blades 1 are consistent can be determined according to an amplitude and period information of the blade root load, if not, it indicates that the wind turbine has the problem of aerodynamic imbalance, which, in turn, allows for the realtime individual pitch operation on the current blade 1 with the inconsistent blade root load according to the blade root load monitoring result of the corresponding blade 1 for an adjustment in the pitch angle of the corresponding blade 1, in order to maintain consistency of the blade root loads of all the blades 1. Additionally, whether the blade root load is in a risk state, such as overload can also be determined according to the blade root load monitoring result, if a certain blade 1 is overloaded, the pitch angle of the blade 1 can be adjusted to reduce the load of the blade 1, and operating time of the wind turbine is prolonged through load reduction, thus increasing the generating capacity.

[72] Based on this, referring to FIG. 3 in combination with FIG. 1 to FIG. 2, the present disclosure further provides an individual pitch adjustment system for a wind turbine, including:

[73] a blade root load monitoring module 10, configured to monitor and obtain a blade root load condition of each blade 1 of the wind turbine within a period of time; 16 01 25

[74] a programming module 20, connected with the blade root load monitoring module 10, and configured to perform machine learning on blade root load condition of each blade 1, determine an optimal pitch angle of each blade 1 at each wind speed, and write a correspondence between the blade root load and the optimal pitch angle of each blade 1 into a control function;

[75] a rotating frequency extraction module 30, connected with the blade root load monitoring module 10, and configured to extract rotating frequency information from the blade root load condition of each blade 1 that is monitored in real time, and according to the rotating frequency information, determine whether the wind turbine is currently in a full-power state; and

[76] a master controller 40, connected with the blade root load monitoring module 10, the programming module 20 and the rotating frequency extraction module 30, and configured to perform an individual pitch operation on the current blade 1 according to a blade root load monitoring result of each blade 1 and the control function if the wind turbine is currently in a non-full-power state; wherein

[77] the master controller 40 is further configured to determine whether the blade root loads of all the blades 1 are consistent according to the blade root load monitoring result of each blade 1 if the wind turbine is currently in the full-power state, and if not, to perform the individual pitch operation on the current blade 1 in real time according to the blade root load monitoring result of the blade 1, such that the consistency of the blade root loads of all the blades 1 is maintained. Additionally, the master controller 40 can also determine whether the blade root load is in a risk state, such as overload, according to the blade root load monitoring result, if a certain blade 1 is overloaded, the pitch angle of the blade 1 can be adjusted to reduce the load of the blade 1, and operating time of the wind turbine is prolonged through load reduction, thus increasing the generating capacity.

[78] The blade root load monitoring module 10 includes a plurality of MEMS fiber-optic load sensors 2 that are circumferentially arranged at a blade root of the blade 1. According to this embodiment, referring to FIG. 2, the wind turbine includes three blades 1. Taking one of the blades 1 as an example for description, 4 MEMS fiber-optic load sensors 2 are uniformly distributed on a circular cross section (for example, a position approx. 1.5 m to 1.8 m away from the blade root) near the blade root of the blade 1, and the MEMS fiber-optic load sensors 2 may monitor the blade root load at the blade root of the blade 1 in real time. Of course, the number and distribution method of the MEMS fiber-optic load sensors 2 are not limited in the present application, which can be adjusted according to actual situations.

[79] After being mounted, the MEMS fiber-optic load sensors 2 are calibrated to obtain a load coefficient of each MEMS fiber-optic load sensor 2, and then a bending moment load borne by each blade 1 is calculated according to measured values of all the MEMS fiber-optic load sensors 2 and the corresponding blade root load coefficient.

[80] The individual pitch adjustment system for the wind turbine further includes a plurality of fiber-optic temperature sensors disposed near the MEMS fiber-optic load sensors 2, wherein the fiber-optic temperature sensors are configured to perform temperature compensation on the MEMS fiber-optic load sensors 2 to reduce the impact of changes in environment temperature on the blade root load measuring results and ensure data accuracy. According to this embodiment, the number of the fiber-optic temperature sensors 3 is between 1 and 4, that is, 1 fiber-optic temperature sensor 3 may be used to compensate one or more MEMS fiber-optic load sensors 2 according to actual requirements, which is not specifically limited in the present application. According to this embodiment, the fiber-optic temperature sensor 3 is a fiberoptical temperature sensor.

[81] According to this embodiment, the individual pitch adjustment system for the wind turbine further includes an acceleration sensor or a tilt angle sensor 4 arranged at the blade root of each blade 1, wherein the acceleration sensor or the tilt angle sensor 4 is configured to monitor a pitch angle and an azimuth angle of the blade 1.

[82] In conclusion, the embodiments of the present disclosure provide the individual pitch adjustment method and adjustment system for the wind turbine. The blade root loads of the blades are monitored in real time during the operating process of the wind turbine as a means for monitoring the states of the blades, such that the targeted design of the individual pitch strategy is performed, where the blades are always in the optimal efficiency state by adjusting the pitch angles, thus increasing the generating capacity of the wind turbine.

[83] The above descriptions are merely preferred embodiments of the present disclosure, and are not intended to limit the present disclosure in any form. Any alterations, such as equivalent substitutions or modifications, on the technical solutions and technical contents disclosed in the present disclosure which are made by those skilled in the art without departing from the scope of the technical solutions of the present disclosure still fall within the protection scope of the present disclosure without departing from the technical solutions of the present disclosure. 16 01 25

Claims

1. An individual pitch adjustment method for a wind turbine, characterized by comprising the follow steps:monitoring and obtaining a blade root load condition of each blade of the wind turbine within a period of time, performing machine learning, determining an optimal pitch angle of each blade 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 turbine is currently in a fullpower state;if the wind turbine 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; andif the wind turbine 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, such that the consistency of the blade root loads of all the blades is maintained.

2. The individual pitch adjustment method for the wind turbine according to claim 1, characterized in that the monitoring and acquiring a blade root load condition of each blade specifically comprises the following steps:arranging a plurality of MEMS fiber-optic load sensors at a blade root of each blade;calibrating the MEMS fiber-optic load sensors to obtain a load coefficient of each MEMS fiber-optic load sensor; andcalculating the blade root load of each blade according to measured values of all the MEMS fiber-optic load sensors and the load coefficients.

3. The individual pitch adjustment method for the wind turbine according to claim 2, characterized in that while arranging the plurality of MEMS fiber-optic load sensors at the blade root of each blade, a plurality of fiber-optic temperature sensors are arranged to perform temperature compensation on the MEMS fiber-optic load sensors.

4. The individual pitch adjustment method for the wind turbine according to claim 1, characterized in that an acceleration sensor or a tilt angle sensor for measuring a rotor tilt angle is configured to monitor the pitch angle of each blade.

5. The individual pitch adjustment method for the wind turbine according to claim 1, characterized in that the individual pitch adjustment method for the wind turbine further comprises the step: obtaining and displaying a current total input wind power value of the wind turbine in real time, which specifically comprises the following steps:performing vector decomposition on the blade root load of each blade according to a rotor tilt angle and the pitch angle of each blade, and calculating a contribution value of an instantaneous torque exerted on the rotor by each blade;calculating input wind power of each blade according to the contribution value and the rotating frequency information of each blade; andsumming the input wind power of each blade to obtain the total input wind power value of the wind turbine.

6. The individual pitch adjustment method for the wind turbine according to claim 1, characterized in that while determining whether the blade root loads of all the blades are consistent according to the blade root load monitoring result of each blade, determining whether the blade root load of each blade is overloaded according to the blade root load monitoring result of each blade, and if each blade is overloaded, performing the individual pitch operation on the current blade for load reduction.

7. An individual pitch adjustment system for a wind turbine, characterized by comprising:a blade root load monitoring module, configured to monitor and obtain a blade root load condition of each blade of the wind turbine within a period of time;a programming module, connected with the blade root load monitoring module, and configured to perform machine learning on the blade root load condition of each blade, determine an optimal pitch angle of each blade at each wind speed, and write a correspondence between the blade root load and the optimal pitch angle into a control function;a rotating frequency extraction module, connected with the blade root load monitoring module, and configured to extract rotating frequency information from the blade root load condition of each blade that is monitored in real time, and according to the rotating frequency information, determine whether the wind turbine is currently in a full-power state; anda master controller, connected with the blade root load monitoring module, the programming module, and the rotating frequency extraction module, and configured to perform an individual pitch operation on the current blade according to a blade root load monitoring result of each blade and the control function if the wind turbine is currently in a non-full-power state; whereinthe master controller is further configured to determine whether the blade root loads of all the blades are consistent according to the blade root load monitoring result of each blade if the wind turbine is currently in the full-power state, and if not, to perform the individual pitch operation on the current blade in real time according to the blade root load monitoring result of the corresponding blade, such that the consistency of the blade root loads of all the blades is maintained.

8. The individual pitch adjustment system for the wind turbine according to claim 7, characterized in that the blade root load monitoring module comprises a plurality of MEMS fiber-optic load sensors that are circumferentially arranged at a blade root of each blade.

9. The individual pitch adjustment system for the wind turbine according to claim 8, characterized in that the individual pitch adjustment system for the wind turbine further comprises a plurality of fiber-optic temperature sensors disposed near the MEMS fiber-optic load sensors, wherein the fiber-optic temperature sensors are configured to perform temperature compensation on the MEMS fiber-optic load sensors.

10. The individual pitch adjustment system for the wind turbine according to claim 8, characterized in that the individual pitch adjustment system for the wind turbine further comprises an acceleration sensor or a tilt angle sensor arranged at the blade root of each blade, wherein the acceleration sensor or the tilt angle sensor is configured to monitor the pitch angle of the blade.

Images