WO2024002381A1 - 风力发电机独立变桨调整方法及调整系统 - Google Patents

风力发电机独立变桨调整方法及调整系统 Download PDF

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Publication number
WO2024002381A1
WO2024002381A1 PCT/CN2023/107865 CN2023107865W WO2024002381A1 WO 2024002381 A1 WO2024002381 A1 WO 2024002381A1 CN 2023107865 W CN2023107865 W CN 2023107865W WO 2024002381 A1 WO2024002381 A1 WO 2024002381A1
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Prior art keywords
blade
load
blade root
wind turbine
wind
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PCT/CN2023/107865
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English (en)
French (fr)
Inventor
钟少龙
韦俊
王新中
俞为
杨浩赟
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上海拜安传感技术有限公司
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Publication of WO2024002381A1 publication Critical patent/WO2024002381A1/zh

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/022—Adjusting aerodynamic properties of the blades
    • F03D7/0224—Adjusting blade pitch
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/022—Adjusting aerodynamic properties of the blades
    • F03D7/024—Adjusting aerodynamic properties of the blades of individual blades
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/047—Automatic control; Regulation by means of an electrical or electronic controller characterised by the controller architecture, e.g. multiple processors or data communications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the invention relates to the technical field of wind power generation, and in particular to an independent pitch adjustment method and an adjustment system for a wind turbine.
  • the pitch angle of the three blades is adjusted synchronously based on the real-time monitored wind speed. That is, during the operation of the wind turbine, the three blades adjust the pitch angle in real time based on the wind speed, and The adjustment angle values of the three blades are consistent.
  • the advantage of this propeller adjustment method is that the adjustment strategy is simple and convenient, easy to operate, and within a certain error range, it can effectively ensure the normal operation and efficiency control of the wind turbine.
  • the difference in wind speed distribution within the rotor rotation plane becomes more and more obvious. For example, for large wind turbines, the difference between the wind speed at the top of the rotor surface and the wind speed at the bottom can reach 3 to 5m/s.
  • the purpose of the present invention is to provide an independent pitch adjustment method and an adjustment system for a wind turbine, which can design an independent pitch strategy in a targeted manner, so that the blades are always in the best efficiency state and the power generation of the wind turbine is increased.
  • the present invention provides a wind turbine independent pitch adjustment method, which includes:
  • the current blade will be independently pitched based on the blade root load monitoring results of each blade and the control function
  • the wind turbine If the wind turbine is currently in a full power state, it will be judged based on the blade root load monitoring results of each blade whether the blade root loads of all blades are consistent. If they are inconsistent, the current blade will be independently evaluated in real time based on the blade root load monitoring results of the blades. Pitch operation to maintain uniform blade root loading on all blades.
  • the steps to monitor and obtain the root load of each blade include:
  • the blade root load of each blade is calculated based on the measurement values of all the MEMS fiber optic load sensors and the load coefficient.
  • several fiber optic temperature sensors are configured to perform temperature compensation on the MEMS fiber optic load sensors.
  • the pitch angle of the blade is monitored through an acceleration sensor or an inclination sensor that measures an inclination angle.
  • the wind turbine independent pitch adjustment method also includes obtaining and displaying the current total input wind power value of the wind turbine in real time, specifically including:
  • the input wind power of each blade is summed to obtain the total input wind power value of the wind turbine.
  • the present invention also provides an independent pitch adjustment system for wind turbines, including:
  • the blade root load monitoring module is used to monitor and obtain the blade root load of each blade of the wind turbine within a period of time;
  • the programming module is connected to the blade root load monitoring module and is used 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 compare the blade root load with the optimal blade
  • the correspondence between distance angles is written as a control function
  • a frequency rotation extraction module connected to the blade root load monitoring module, is used to extract frequency rotation information from the real-time monitored blade root load conditions of each blade, and determine whether the wind turbine is currently at full capacity based on the frequency rotation information. hair status;
  • the main controller is connected to the blade root load monitoring module, the programming module and the frequency conversion extraction module, and is used to monitor the blade root load of each blade according to the blade root load monitoring results and The control function performs an independent pitch operation on the current blade;
  • the main controller is also used to determine whether the blade root loads of all blades are consistent based on the blade root load monitoring results of each blade when the wind turbine is currently in a full power state.
  • the load monitoring results perform independent pitch operations on the current blades in real time to ensure that the root loads of all blades remain consistent.
  • the blade root load monitoring module includes a plurality of MEMS fiber optic load sensors arranged circumferentially at the blade root of each blade.
  • the wind turbine independent pitch adjustment system also includes a number 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.
  • the wind turbine independent pitch adjustment system further includes an acceleration sensor or an inclination sensor arranged at the blade root, and the acceleration sensor or inclination sensor is used to monitor the pitch angle of the blade.
  • the invention provides an independent pitch adjustment method and an adjustment system for a wind turbine, which has at least one of the following beneficial effects:
  • a load sensor based on MEMS optical fiber sensing technology is used to realize real-time monitoring of blade root load and enable long-term accurate measurement;
  • Figure 1 is a flow chart of an independent pitch adjustment method for a wind turbine provided by an embodiment of the present invention
  • Figure 2 is a schematic diagram of a sensor arrangement provided by an embodiment of the present invention.
  • Figure 3 is a schematic diagram of an independent pitch adjustment system for a wind turbine provided by an embodiment of the present invention.
  • wind speed monitoring of wind turbines relies on a single wind speed and direction meter on the top of the wind turbine hub. This 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 based on how much pitch the blades should adjust.
  • the angle is optimal, so it is difficult to adjust the propeller in a targeted manner, and it is impossible to ensure that the power of the blades is in the optimal state, making it difficult to maximize the power generation of the wind turbine.
  • the present invention proposes a wind turbine independent pitch adjustment method and adjustment system, which can monitor the status of the wind turbine blades in real time during the operation of the wind turbine, thereby designing an independent pitch strategy in a targeted manner to achieve
  • the purpose of adjusting the blades to always be at optimal efficiency is to increase the power generated by the wind turbine.
  • the present invention uses real-time monitoring of blade root load as a means of blade status monitoring, and designs an independent pitch strategy based on the real-time blade root load monitored by blade root.
  • independent pitch Although there are many researches and inventions on independent pitch on the market, they are basically based on An independent pitch method developed for wind turbine aerodynamics related simulation, wind turbine wind speed real-time monitoring, and wind turbine main control SCADA data real-time monitoring.
  • the present invention focuses on an independent pitch method based on blade root load monitoring. That is to say, the independent pitch adjustment in the present invention is based on blade root load monitoring, which is completely different from the independent pitch control basis of other wind turbines.
  • the singular terms “a,” “an,” and “the” may include plural referents unless the content clearly dictates otherwise.
  • the term “or” is generally used in its sense including “and/or” unless the content clearly dictates otherwise.
  • the term “several” is generally used in its sense including “at least one” unless the content clearly dictates otherwise.
  • the term “at least two” used in the present invention is generally used in the sense of including “two or more” unless the content clearly indicates otherwise.
  • 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 quantity of indicated technical features. Thus, features defined as “first”, “second”, and “third” may explicitly or implicitly include one or at least two of these features.
  • FIG. 1 is a flow chart of an independent pitch adjustment method of a wind turbine provided by an embodiment of the present invention.
  • This embodiment provides a method for independent pitch adjustment of a wind turbine, including:
  • step S1 is first performed to monitor and obtain the blade root load of each blade of the wind turbine within a period of time and perform machine learning to determine the optimal pitch angle of each blade at each wind speed, and compare the blade root load with the optimal pitch angle.
  • the correspondence between the distance angles is written as a control function.
  • the steps to monitor and obtain the root load of each blade include:
  • 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.
  • real-time monitoring of the blade root load of blade 1 is used as a means of blade status monitoring, and an independent pitch strategy is designed based on the real-time load monitored at the blade root of blade 1, so as to achieve the purpose of independent pitch change to increase the power generation of the wind turbine.
  • the conditions for sensors that can be used for blade root load monitoring are very harsh.
  • the wind turbine is a power generation device, and its blades 1 cannot use live equipment, otherwise safety failures are extremely likely to occur. Therefore, the sensors used must be uncharged and wireless.
  • this embodiment uses the MEMS optical fiber load sensor 2 as the sensor for blade root load monitoring.
  • MEMS optical fiber sensing technology is a 21st century cutting-edge technology based on micro/nano mechanics and optics.
  • the technology's magnetic blocks, elastic supports, optical reflective micromirrors, light entrance and exit waveguide systems are all directly integrated on a tiny chip, truly realizing all-optical detection and transmission of current and other signals.
  • the manufactured MEMS chip has a compact structure, integrated packaging, good parameter consistency, high sensitivity, large dynamic range and good linearity. The phase changes linearly, and the performance is stable and reliable.
  • MEMS optical fiber sensing technology overcomes the mutual constraints of "wideband” and "high precision" of existing sensing technologies. It has the characteristics of passive, wide temperature, miniaturization, anti-electromagnetic interference, lightweight, easy to network and maintenance-free. Fully meets the monitoring requirements of this application.
  • the wind turbine includes three blades 1.
  • This embodiment takes one of the blades 1 as an example to illustrate.
  • the circular cross-section near the blade root of the blade 1 (for example, about 1.5m ⁇ 1.8m), four MEMS optical fiber load sensors 2 are evenly distributed on the blade.
  • These MEMS optical fiber load sensors 2 can monitor the blade root load at the blade root of the blade 1 in real time.
  • this application does not place any restrictions on the number and distribution of the MEMS optical fiber load sensors 2, and they can be adjusted according to actual conditions.
  • the MEMS optical fiber load sensor 2 is calibrated to obtain the load coefficient of each MEMS optical fiber load sensor 2, and then each blade is calculated based on the measured values of all the MEMS optical fiber load sensors 2 and the blade root load coefficient. Blade root load of 1.
  • MEMS optical fiber load sensors 2 are arranged at the blade root of the blade 1
  • several optical fiber temperature sensors 3 are configured to perform temperature compensation on the MEMS optical fiber load sensors 2 to reduce the impact of changes in ambient temperature on the blade root load. The impact of measurement results ensures the accuracy of data.
  • the number of optical fiber temperature sensors 3 is between 1 and 4. That is to say, one optical fiber temperature sensor 3 can be used to pair one or more MEMS optical fiber load sensors 2 according to actual needs. Compensation is provided, and this application does not impose specific restrictions on this.
  • the optical fiber temperature sensor 3 is an optical fiber temperature sensor 3 .
  • the MEMS optical fiber load sensor 2 After installing the MEMS optical fiber load sensor 2, first collect the blade root load condition of each blade 1 within a period of time (such as 1000h) and perform machine learning.
  • the load condition needs to cover as many wind speed values as possible, and the wind speed value is usually between Within the wind speed range of 3-13m/s, then find the best position for the wind turbine operation at each wind speed (that is, the best pitch angle of each blade 1), and write a control function based on this to guide subsequent steps. Adjustment of blade 1 pitch angle.
  • each blade 1 has an optimal power value under different loads, that is, the value with the largest power generation, and each optimal power value corresponds to an optimal pitch angle. Therefore, according to the machine learning method, Obtain the optimal pitch angle corresponding to each blade 1 under different loads. Of course, it can also be implemented through simulation, and this application does not limit this.
  • the blade root load of blade 1 is 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 wind rotor can be calculated, and then based on the contribution of each blade 1
  • the value and the rotation frequency information can be used to calculate the input wind power of each blade 1, thereby establishing a corresponding relationship between blade root load-input wind power-pitch angle.
  • the rotation frequency information mentioned here can be understood as the rotation speed of the blade 1, which can be extracted from the blade root load condition according to the discrete Fourier transform.
  • the pitch angle of the blade 1 is monitored through an acceleration sensor or an inclination sensor 4 that measures the inclination angle, so that the attitude of the blade 1 can be monitored synchronously with the MEMS optical fiber load sensor 2, thereby improving the reliability of the system.
  • the acceleration sensor or inclination sensor 4 can also monitor the azimuth angle of the blade 1 for calculation of the instantaneous torque of each blade 1 on the wind wheel.
  • step S2 is performed to monitor and obtain the blade root load of each blade 1 in real time, extract the frequency rotation information therefrom, and determine whether the wind turbine is currently in a full power state based on the frequency rotation information.
  • step S3 is executed to perform an independent pitch operation on the current blade 1 based on the blade root load monitoring results of each blade 1 and the control function.
  • 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 instantaneous load of each blade 1 on the wind rotor can be calculated.
  • the input wind power of each blade 1 can be calculated based on the contribution value of each blade 1 multiplied by the rotation speed, and then the pitch angle of the current blade 1 is adjusted based on this information in conjunction with the control function,
  • the pitch angle of the current blade 1 is adjusted based on this information in conjunction with the control function,
  • the maximum torque in the rotation plane of the wind wheel can be obtained, ensuring that the input wind power of the blade 1 is in the best state, which is equivalent to increasing
  • the wind turbine generates power, thereby increasing the power generation of the wind turbine.
  • the wind turbine independent pitch adjustment method also includes obtaining and displaying the current total input wind power value of the wind turbine in real time. After the input wind power of each blade 1 is calculated according to the above method, each blade can be directly The input wind power of 1 is summed to obtain the total input wind power value of the fan.
  • step S4 is executed to determine whether the blade root loads of all blades 1 are consistent based on the blade root load monitoring results of each blade 1.
  • the current blade 1 is independently pitched in real time to keep the blade root load of all blades 1 consistent.
  • it can be determined based on the amplitude and period information of the blade root load whether the blade root loads of all blades 1 are consistent. If they are inconsistent, it means that the fan has an aerodynamic imbalance problem.
  • Root load monitoring results provide real-time analysis of blades with inconsistent current blade root loads. Perform an independent pitch operation to adjust the pitch angle of blade 1 so that the blade root loads of all blades 1 remain consistent.
  • the blade root load monitoring results can also be used to determine whether the blade 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 on the blade 1. By reducing the load, Increase the running time of the wind turbine and increase the power generation.
  • the present invention also provides an independent pitch adjustment system for wind turbines, including:
  • the blade root load monitoring module 10 is used to monitor and obtain the blade root load condition of each blade 1 of the wind turbine within a period of time;
  • the programming module 20 is connected to the blade root load monitoring module 10 and is used 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 set the blade root load of each blade 1 to The corresponding relationship between the root load and the optimal pitch angle is written as a control function;
  • 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 real-time monitoring of the blade root load of each blade 1, and determine the current status of the wind turbine based on the rotation frequency information. Whether it is in full hair condition;
  • the main controller 40 is connected to the blade root load monitoring module 10, the programming module 20 and the frequency extraction module 30, and is used to control the blade root load of each blade 1 when the wind turbine is currently in a less than full power state.
  • the monitoring results and the control function perform independent pitching operations on the current blade 1;
  • the main controller 40 is also used to determine whether the blade root loads of all blades 1 are consistent according to the blade root load monitoring results of each blade 1 when the wind turbine is currently in a full power state.
  • the blade root load monitoring results of 1 perform an independent pitch operation on the current blade 1 in real time to ensure that the blade root loads of all blades 1 remain consistent.
  • the main controller 40 can also determine whether the blade root load is in a risk state such as overload based on the blade root load monitoring results. If a certain blade 1 is overloaded, the pitch angle of the blade 1 can be adjusted. Load reduction, through load reduction, increases the running time of the wind turbine and increases the power generation.
  • the blade root load monitoring module 10 includes a plurality of MEMS optical fiber load sensors 2 arranged circumferentially at the blade root of the blade 1 .
  • the wind turbine includes three blades 1.
  • This embodiment takes one of the blades 1 as an example to illustrate.
  • the circular cross-section near the blade root of the blade 1 (for example, about 1.5m ⁇ 1.8m), four MEMS optical fiber load sensors 2 are evenly distributed on the blade.
  • These MEMS optical fiber load sensors 2 can monitor the blade root load at the blade root of the blade 1 in real time.
  • Ben The application does not place any restrictions on the number and distribution method of the MEMS optical fiber load sensors 2, which can be adjusted according to the actual situation.
  • 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 calculated based on the measured values of all the MEMS fiber optic load sensors 2 and the blade root load coefficient. The bending moment load on the blade 1 is described.
  • the wind turbine independent pitch adjustment system also includes a number of optical fiber temperature sensors arranged near the MEMS optical fiber load sensor 2.
  • the optical fiber temperature sensors are used to perform temperature compensation on the MEMS optical fiber load sensor 2 to reduce the environmental impact. The influence of temperature changes on blade root load measurement results to ensure the accuracy of the data.
  • the number of optical fiber temperature sensors 3 is between 1 and 4. That is to say, one optical fiber temperature sensor 3 can be used to pair one or more MEMS optical fiber load sensors 2 according to actual needs. Compensation is provided, and this application does not impose specific restrictions on this.
  • the optical fiber temperature sensor 3 is an optical fiber temperature sensor.
  • the wind turbine independent pitch adjustment system also includes an acceleration sensor or an inclination sensor 4 arranged at the blade root of the blade 1 .
  • the acceleration sensor or inclination sensor 4 is used to monitor the movement of the blade 1 . Pitch angle and azimuth angle.
  • each embodiment of the present invention provides an independent pitch adjustment method and an adjustment system for a wind turbine.
  • the independent pitch can be designed in a targeted manner. Strategy to achieve the purpose of adjusting the blade pitch angle to always be in the best efficiency state and increase the power generation of the wind turbine.

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Abstract

本发明提供了一种风力发电机独立变桨调整方法及调整系统，调整方法包括：监测并获取一段时间内风机各叶片的叶根载荷情况并进行机器学习，确定每一个风速下的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数；实时监测并获取各叶片的叶根载荷情况，从中提取出转频信息，并根据转频信息判断风机当前是否处于满发状态：若风机当前处于未满发状态，则根据各叶片的叶根载荷监测结果及控制函数对当前叶片进行独立变桨操作；若风机当前处于满发状态，则根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致，若不一致，则根据叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作。从而达到了提升风机发电量的目的。

Description

风力发电机独立变桨调整方法及调整系统 技术领域
本发明涉及风力发电技术领域，尤其涉及一种风力发电机独立变桨调整方法及调整系统。
背景技术
目前主流的风力发电机设计上，其三只叶片的桨距角根据实时监测的风速同步调桨，即风力发电机在运行过程中，三只叶片以风速为依据，实时调整桨距角，且三只叶片调整的角度值一致。这种调桨方式的优势在于调整策略简单方便，易于操作，且在一定误差范围内，可以有效保证风机的正常运行及效率控制。但是，随着风力发电机越来越大型化，风轮旋转平面内的风速分布差异越来越明显，比如对于大型风机，风轮面顶部的风速与底部风速相差可以达到3～5m/s，故而风机的三只叶片在风轮旋转平面不同高度处，其所受风速差别较大，因此对于处于风轮旋转平面内不同位置的叶片，其对应的最佳桨距角存在较大差别。因此，根据叶片所处风轮旋转平面位置不同而独立变桨的需求也越来越多。
但是，风机的风速监测依赖于风机轮毂顶部的单台风速风向仪，这导致无法判断风机运行过程中各个叶片所处的实际风速是多少，无法有根据地判断叶片应该调整多少桨距角为最佳，因而难以针对性的进行调桨，进而无法保证叶片的功率处于最佳状态，导致风机的发电量难以最大化。
发明内容
本发明的目的在于提供一种风力发电机独立变桨调整方法及调整系统，能够针对性地设计独立变桨策略，使叶片始终处于最佳效率状态，提升风机的发电量。
为了达到上述目的，本发明提供了一种风力发电机独立变桨调整方法，包括：
监测并获取一段时间内风机各叶片的叶根载荷情况并进行机器学习，确定每一个风速下各叶片的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数；
实时监测并获取各叶片的叶根载荷情况，从中提取出转频信息，并根据所述转频信息判断所述风机当前是否处于满发状态；
若所述风机当前处于未满发状态，则根据各叶片的叶根载荷监测结果及所述控制函数对当前叶片进行独立变桨操作；
若所述风机当前处于满发状态，则根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致，若不一致，则根据所述叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作，以使所有叶片的叶根载荷保持一致性。
可选的，监测并获取各叶片的叶根载荷情况的步骤具体包括：
在各叶片的叶根处布置若干MEMS光纤载荷传感器；
对所述MEMS光纤载荷传感器进行标定，得到各个MEMS光纤载荷传感器的载荷系数；
根据所有所述MEMS光纤载荷传感器的测量值及所述载荷系数计算各叶片的叶根载荷。
可选的，在所述叶片的叶根处布置若干MEMS光纤载荷传感器的同时，配置若干光纤温度传感器以对所述MEMS光纤载荷传感器进行温度补偿。
可选的，通过测倾角的加速度传感器或倾角传感器来监测所述叶片的桨距角。
可选的，所述风力发电机独立变桨调整方法还包括实时获取并显示当前所述风机的总输入风功率值，具体包括：
根据风轮倾角及各叶片的桨距角对各叶片的叶根载荷进行矢量分解，计算各叶片对风轮瞬时扭矩的贡献值；
根据各叶片的所述贡献值及所述转频信息计算各叶片的输入风功率；
将各叶片的输入风功率进行求和得到所述风机的总输入风功率值。
可选的，根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致的同时，还根据各叶片的叶根载荷监测结果判断各叶片的叶根载荷是否过载，若过载，则对当前叶片进行独立变桨操作以进行降载。
基于此，本发明还提供了一种风力发电机独立变桨调整系统，包括：
叶根载荷监测模块，用于监测并获取一段时间内风机各叶片的叶根载荷情况；
编程模块，与所述叶根载荷监测模块连接，用于对各叶片的叶根载荷情况进行机器学习，确定每一个风速下各叶片的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数；
转频提取模块，与所述叶根载荷监测模块连接，用于从实时监测的各叶片的叶根载荷情况中提取出转频信息，并根据所述转频信息判断所述风机当前是否处于满发状态；
主控制器，与所述叶根载荷监测模块、所述编程模块及所述转频提取模块连接，用于在所述风机当前处于未满发状态时，根据各叶片的叶根载荷监测结果及所述控制函数对当前叶片进行独立变桨操作；
所述主控制器，还用于在所述风机当前处于满发状态时，根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致，若不一致，则根据所述叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作，以使所有叶片的叶根载荷保持一致性。
可选的，所述叶根载荷监测模块包括若干沿周向布置在各叶片叶根处的MEMS光纤载荷传感器。
可选的，所述风力发电机独立变桨调整系统还包括若干配置在所述MEMS光纤载荷传感器附近的光纤温度传感器，所述光纤温度传感器用于对所述MEMS光纤载荷传感器进行温度补偿。
可选的，所述风力发电机独立变桨调整系统还包括布置在所述叶片叶根处的加速度传感器或倾角传感器，所述加速度传感器或倾角传感器用于监测所述叶片的桨距角。
本发明提供了一种风力发电机独立变桨调整方法及调整系统，至少具有以下有益效果之一：
1)通过在风机运行过程中实时监测叶片叶根载荷作为叶片状态监测的手段，进而针对性地设计独立变桨策略，以使叶片始终处于最佳效率状态，从而提升风机的发电量；
2)采用了基于MEMS光纤传感技术的载荷传感器，实现了叶根载荷的实时监测，可长期精准测量；
3)根据各叶片的叶根载荷监测结果还能够判断所有叶片的叶根载荷是否一致，进而判断风机是否存在气动不平衡的问题，并根据所述叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作，以使所有叶片的叶根载荷保持一致性；
4)根据各叶片的叶根载荷监测结果还能够判断叶片是否过载，以便及时变桨进行降载处理。
附图说明
本领域的普通技术人员将会理解，提供的附图用于更好地理解本发明，而不对本发明的范围构成任何限制。其中：
图1为本发明一实施例提供的风力发电机独立变桨调整方法的流程图；
图2为本发明一实施例提供的传感器布置的示意图；
图3为本发明一实施例提供的风力发电机独立变桨调整系统的示意图。
附图中：
1-叶片；2-MEMS光纤载荷传感器；3-光纤温度传感器器；4-倾角传感器；
10-叶根载荷监测模块；20-编程模块；30-转频提取模块；40-主控制器。
具体实施方式
根据背景技术所述，风机的风速监测依赖于风机轮毂顶部的单台风速风向仪，这导致无法判断风机运行过程中各个叶片所处的实际风速是多少，无法有根据地判断叶片应该调整多少桨距角为最佳，因而难以针对性的进行调桨，进而无法保证叶片的功率处于最佳状态，导致风机的发电量难以最大化。
基于此，本发明提出了一种风力发电机独立变桨调整方法及调整系统，可以在风机运行过程中实时监测风力发电机叶片所处的状态，从而针对性地设计独立变桨策略，以达到调整叶片使其始终处于最佳效率状态，从而提升风机发电量的目的。进一步的，本发明通过实时监测叶片叶根载荷作为叶片状态监测的手段，并以叶片叶根监测的实时叶根载荷为依据设计独立变桨策略。
虽然目前市场上对于独立变桨的研究与发明也有很多，但是基本上是基于 风机的空气动力学相关仿真、风机风速实时监测、风机主控SCADA数据实时监测开发的独立变桨方法。而本发明聚焦在基于叶片的叶根载荷监测的独立变桨方法。也就是说，本发明中的独立变桨调整的依据是叶片叶根载荷监测，这与其他风机独立变桨控制依据完全不同。
此外，目前市场上提出许多基于叶根载荷监测的风力发电机的健康状态监测方法，这些方法总体目标是为了监测叶片是否受到损伤，有没有过载、受到雷击、有覆冰等等健康状态上的问题，并针对这些目标展开了大量的实验及实战测试，开发了一套相对比较成熟的控制策略，这些监测方法的目标是为了提升风机的安全性，与本发明中提升风机发电量的目标也有本质上的不同。
为使本发明的目的、优点和特征更加清楚，以下结合附图和具体实施例对本发明作进一步详细说明。需说明的是，附图均采用非常简化的形式且未按比例绘制，仅用以方便、明晰地辅助说明本发明实施例的目的。此外，附图所展示的结构往往是实际结构的一部分。特别的，各附图需要展示的侧重点不同，有时会采用不同的比例。
本发明中所使用的单数形式术语“一”、“一个”以及“该”可包括复数对象，除非内容另外明确指出外。本发明中所使用的术语“或”通常是以包括“和/或”的含义而进行使用的，除非内容另外明确指出外。本发明中所使用的术语“若干”通常是以包括“至少一个”的含义而进行使用的，除非内容另外明确指出外。本发明中所使用的术语“至少两个”通常是以包括“两个或两个以上”的含义而进行使用的，除非内容另外明确指出外。此外，术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”、“第三”的特征可以明示或者隐含地包括一个或者至少两个该特征。
请参照图1，图1为本发明一实施例提供的风力发电机独立变桨调整方法的流程图。本实施例提供了一种风力发电机独立变桨调整方法，包括：
S1、监测并获取一段时间内风机各叶片的叶根载荷情况并进行机器学习，确定每一个风速下各叶片的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数；
S2、实时监测并获取各叶片的叶根载荷情况，从中提取出转频信息，并根据 所述转频信息判断所述风机当前是否处于满发状态；
S3、若所述风机当前处于未满发状态，则根据各叶片的叶根载荷监测结果及所述控制函数对当前叶片进行独立变桨操作；
S4、若所述风机当前处于满发状态，则根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致，若不一致，则根据所述叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作，以使所有叶片的叶根载荷保持一致性。
具体的，先执行步骤S1，监测并获取一段时间内风机各叶片的叶根载荷情况并进行机器学习，确定每一个风速下各叶片的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数。
其中，请结合图2，监测并获取各叶片的叶根载荷情况的步骤具体包括：
在各叶片1的叶根处布置若干MEMS光纤载荷传感器2；
对所述MEMS光纤载荷传感器2进行标定，得到各个MEMS光纤载荷传感器2的载荷系数；
根据所有所述MEMS光纤载荷传感器2的测量值及所述载荷系数计算所述各叶片1的叶根载荷。
本实施例以叶片1的叶根载荷实时监测作为叶片状态监测的手段，并以叶片1叶根处监测的实时载荷为依据设计独立变桨策略，从而达到独立变桨提升风机发电量的目的。而可以用于叶根载荷监测的传感器，其条件非常苛刻，一方面风机作为发电装置，其叶片1不可以使用带电设备，否则极其容易发生安全故障，因此所使用的传感器必须是不带电的无源器件；另一方面，在风机叶片1运行过程中需要实时独立变桨，因此载荷监测必须实时进行，且可以捕捉到叶片1的叶根处细微的载荷变化，这就意味着载荷传感器的灵敏度等指标必须达到很高的要求。
基于此，本实施例采用MEMS光纤载荷传感器2作为叶根载荷监测的传感器，MEMS光纤传感技术是建立在微米/纳米机械学、光学基础上的21世纪前沿技术。该技术的磁块、弹性支撑体、光学反射微镜、光入射及出射波导系统都直接集成在一个微小的芯片上，真正实现了对电流等信号的全光检测和传输。制造出的MEMS芯片结构紧凑，一体式封装，参数一致性好，灵敏度高，动态范围大和线性度好等优点，且相位呈线性变化，性能稳定、可靠。
MEMS芯片的硅基敏感结构采用微机电技术集成制造，信号采用光纤检测技术探测和读取，因此其具有MEMS传感技术与光纤传感技术的共同优点。且MEMS光纤传感技术克服了现有传感技术“宽频”与“高精度”的互相制约，其具有无源、宽温、微型化、抗电磁干扰、轻便、易组网和免维护特性，完全符合本申请的监测需求。
本实施例中，结合图2，所述风机包含三个叶片1，本实施例以其中一个叶片1为例进行说明，所述叶片1叶根处附近的圆形横截面(比如距离叶根约1.5m～1.8m处)上均布4只MEMS光纤载荷传感器2，这些MEMS光纤载荷传感器2可以实时监测叶片1叶根处的叶根载荷。当然，本申请对于所述MEMS光纤载荷传感器2的数量及分布方式不作任何限制，可根据实际情况进行调整。
安装完成后，对所述MEMS光纤载荷传感器2进行标定，得到每只MEMS光纤载荷传感器2的载荷系数，再根据所有所述MEMS光纤载荷传感器2的测量值及所述叶根载荷系数计算各叶片1的叶根载荷。
进一步的，在所述叶片1叶根处布置若干MEMS光纤载荷传感器2的同时，配置若干光纤温度传感器器3以对所述MEMS光纤载荷传感器2进行温度补偿，以减少环境温度变化对叶根载荷测量结果的影响，保证数据的准确性。
本实施例中，所述光纤温度传感器器3的数量介于1～4个之间，也就是说，可根据实际需求采用1个光纤温度传感器器3对1个或多个MEMS光纤载荷传感器2进行补偿，本申请对此不作具体限制。本实施例中，所述光纤温度传感器器3为光纤温度传感器器3。
安装好所述MEMS光纤载荷传感器2之后，先收集一段时间内(如1000h)各叶片1的叶根载荷情况并进行机器学习，该载荷情况需覆盖尽可能多的风速值，该风速值通常介于3-13m/s的风速区间内，然后寻找每一个风速下风机运行的最佳位置(即各叶片1的最佳桨距角)，并以此为依据编写成控制函数，以指导后续各叶片1桨距角的调整。
应当理解的是，每个叶片1在不同的载荷下具有一个最佳功率值，即发电量最大的值，而每个最佳功率值对应一个最佳桨距角，故根据机器学习的方式可以获取每个叶片1在不同的载荷下对应的最佳桨距角。当然也可以通过仿真的方式实现，本申请对此不作限制。
具体的，根据风轮倾角及各叶片1的桨距角对叶片1的叶根载荷进行矢量分解，可以计算出各叶片1对风轮瞬时扭矩的贡献值，然后根据各叶片1的所述贡献值及所述转频信息可以计算各叶片1的输入风功率，由此可建立叶根载荷-输入风功率-桨距角的对应关系。此处提及的转频信息可以理解为所述叶片1的转速，该转速可根据离散傅里叶变换从叶根载荷情况中提取出来。
本实施例中，通过测倾角的加速度传感器或倾角传感器4来监测所述叶片1的桨距角，这样可以配合MEMS光纤载荷传感器2同步监测所述叶片1的姿态，提高系统的可靠性。此外，所述加速度传感器或倾角传感器4还可以监测所述叶片1的方位角，以用于各叶片1对风轮瞬时扭矩的计算。
接着，执行步骤S2，实时监测并获取各叶片1的叶根载荷情况，从中提取出转频信息，并根据所述转频信息判断所述风机当前是否处于满发状态。
若所述风机当前处于未满发状态，则执行步骤S3，根据各叶片1的叶根载荷监测结果及所述控制函数对当前叶片1进行独立变桨操作。具体的，当所述风机当前处于未满发状态时，可根据风轮倾角及各叶片1的桨距角对各叶片1的叶根载荷进行矢量分解，可以计算出各叶片1对风轮瞬时扭矩的贡献值，然后根据各叶片1的所述贡献值与转速相乘可以算出各叶片1的输入风功率，然后以这些信息为依据配合所述控制函数来调整当前叶片1的桨距角，使所述叶片1在风轮旋转平面内的不同位置处时，通过桨距角调整，可以获得风轮旋转平面内最大扭矩，保证叶片1的输入风功率处于最佳状态，相当于增大了风机发电功率，从而提高了风机的发电量。
进一步的，所述风力发电机独立变桨调整方法还包括实时获取并显示当前所述风机的总输入风功率值，当根据上述方法计算出各叶片1的输入风功率后，可直接将各叶片1的输入风功率进行求和得到所述风机的总输入风功率值。
若所述风机当前处于满发状态，则执行步骤S4，根据各叶片1的叶根载荷监测结果判断所有叶片1的叶根载荷是否一致，若不一致，则根据所述叶片1的叶根载荷监测结果实时对当前叶片1进行独立变桨操作，以使所有叶片1的叶根载荷保持一致性。本实施例中，可根据叶根载荷的幅值和周期信息判断所有叶片1的叶根载荷是否一致，若不一致，说明所述风机存在气动不平衡的问题，则可根据所述叶片1的叶根载荷监测结果实时对当前叶根载荷不一致的叶片1进 行独立变桨操作，调整该叶片1的桨距角，以使所有叶片1的叶根载荷保持一致性。此外，还可以根据叶根载荷监测结果判断叶根载荷是否存在过载等风险状态，若某个叶片1存在过载，则可以调整该叶片1的桨距角，为该叶片1降载，通过降载增加风机运行时间，提升发电量。
基于此，请参照图3，并结合图1-图2，本发明还提供了一种风力发电机独立变桨调整系统，包括：
叶根载荷监测模块10，用于监测并获取一段时间内风机各叶片1的叶根载荷情况；
编程模块20，与所述叶根载荷监测模块10连接，用于对各叶片1的叶根载荷情况进行机器学习，确定每一个风速下各叶片1的最佳桨距角，并将叶片1叶根载荷与最佳桨距角之间的对应关系编写成控制函数；
转频提取模块30，与所述叶根载荷监测模块10连接，用于从实时监测的各叶片1的叶根载荷情况中提取出转频信息，并根据所述转频信息判断所述风机当前是否处于满发状态；
主控制器40，与叶根载荷监测模块10、所述编程模块20及所述转频提取模块30连接，用于在所述风机当前处于未满发状态时，根据各叶片1的叶根载荷监测结果及所述控制函数对当前叶片1进行独立变桨操作；
所述主控制器40，还用于在所述风机当前处于满发状态时，根据各叶片1的叶根载荷监测结果判断所有叶片1的叶根载荷是否一致，若不一致，则根据所述叶片1的叶根载荷监测结果实时对当前叶片1进行独立变桨操作，以使所有叶片1的叶根载荷保持一致性。此外，所述主控制器40还可以根据叶根载荷监测结果判断叶根载荷是否存在过载等风险状态，若某个叶片1存在过载，则可以调整该叶片1的桨距角，为该叶片1降载，通过降载增加风机运行时间，提升发电量。
所述叶根载荷监测模块10包括若干沿周向布置在所述叶片1叶根处的MEMS光纤载荷传感器2。本实施例中，结合图2，所述风机包含三个叶片1，本实施例以其中一个叶片1为例进行说明，所述叶片1叶根处附近的圆形横截面(比如距离叶根约1.5m～1.8m处)上均布4只MEMS光纤载荷传感器2，这些MEMS光纤载荷传感器2可以实时监测叶片1叶根处的叶根载荷。当然，本 申请对于所述MEMS光纤载荷传感器2的数量及分布方式不作任何限制，可根据实际情况进行调整。
安装完成后，需要对所述MEMS光纤载荷传感器2进行标定，得到每只MEMS光纤载荷传感器2的载荷系数，再根据所有所述MEMS光纤载荷传感器2的测量值及所述叶根载荷系数计算所述叶片1受到的弯矩载荷。
所述风力发电机独立变桨调整系统还包括若干配置在所述MEMS光纤载荷传感器2附近的光纤温度传感器，所述光纤温度传感器用于对所述MEMS光纤载荷传感器2进行温度补偿，以减少环境温度变化对叶根载荷测量结果的影响，保证数据的准确性。本实施例中，所述光纤温度传感器器3的数量介于1～4个之间，也就是说，可根据实际需求采用1个光纤温度传感器器3对1个或多个MEMS光纤载荷传感器2进行补偿，本申请对此不作具体限制。本实施例中，所述光纤温度传感器器3为光纤温度传感器。
本实施例中，所述风力发电机独立变桨调整系统还包括布置在所述叶片1叶根处的加速度传感器或倾角传感器4，所述加速度传感器或倾角传感器4用于监测所述叶片1的桨距角和方位角。
综上，本发明各实施例提供了一种风力发电机独立变桨调整方法及调整系统，通过在风机运行过程中实时监测叶片叶根载荷作为叶片状态监测的手段进而针对性地设计独立变桨策略，以达到调整叶片桨距角使其始终处于最佳效率状态，提升风机发电量的目的。
上述仅为本发明的优选实施例而已，并不对本发明起到任何限制作用。任何所属技术领域的技术人员，在不脱离本发明的技术方案的范围内，对本发明揭露的技术方案和技术内容做任何形式的等同替换或修改等变动，均属未脱离本发明的技术方案的内容，仍属于本发明的保护范围之内。

Claims (10)

  1. 一种风力发电机独立变桨调整方法，其特征在于，包括：
    监测并获取一段时间内风机各叶片的叶根载荷情况并进行机器学习，确定每一个风速下各叶片的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数；
    实时监测并获取各叶片的叶根载荷情况，从中提取出转频信息，并根据所述转频信息判断所述风机当前是否处于满发状态；
    若所述风机当前处于未满发状态，则根据各叶片的叶根载荷监测结果及所述控制函数对当前叶片进行独立变桨操作；
    若所述风机当前处于满发状态，则根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致，若叶根载荷不一致，则根据所述叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作，以使所有叶片的叶根载荷保持一致性。
  2. 如权利要求1所述的风力发电机独立变桨调整方法，其特征在于，监测并获取各叶片的叶根载荷情况的步骤具体包括：
    在各叶片的叶根处布置若干MEMS光纤载荷传感器；
    对所述MEMS光纤载荷传感器进行标定，得到各个MEMS光纤载荷传感器的载荷系数；
    根据所有所述MEMS光纤载荷传感器的测量值及所述载荷系数计算各叶片的叶根载荷。
  3. 如权利要求2所述的风力发电机独立变桨调整方法，其特征在于，在所述叶片的叶根处布置若干MEMS光纤载荷传感器的同时，配置若干光纤温度传感器以对所述MEMS光纤载荷传感器进行温度补偿。
  4. 如权利要求1所述的风力发电机独立变桨调整方法，其特征在于，通过测倾角的加速度传感器或倾角传感器来监测所述叶片的桨距角。
  5. 如权利要求1所述的风力发电机独立变桨调整方法，其特征在于，所述风力发电机独立变桨调整方法还包括实时获取并显示当前所述风机的总输入风功率值，具体包括：
    根据风轮倾角及各叶片的桨距角对各叶片的叶根载荷进行矢量分解，计算 各叶片对风轮瞬时扭矩的贡献值；
    根据各叶片的所述贡献值及所述转频信息计算各叶片的输入风功率；
    将各叶片的输入风功率进行求和得到所述风机的总输入风功率值。
  6. 如权利要求1所述的风力发电机独立变桨调整方法，其特征在于，根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致的同时，还根据各叶片的叶根载荷监测结果判断各叶片的叶根载荷是否过载，若过载，则对当前叶片进行独立变桨操作以进行降载。
  7. 一种风力发电机独立变桨调整系统，其特征在于，包括：
    叶根载荷监测模块，用于监测并获取一段时间内风机各叶片的叶根载荷情况：
    编程模块，与所述叶根载荷监测模块连接，用于对各叶片的叶根载荷情况进行机器学习，确定每一个风速下各叶片的最佳桨距角，并将叶根载荷与最佳桨距角之间的对应关系编写成控制函数；
    转频提取模块，与所述叶根载荷监测模块连接，用于从实时监测的各叶片的叶根载荷情况中提取出转频信息，并根据所述转频信息判断所述风机当前是否处于满发状态；
    主控制器，与所述叶根载荷监测模块、所述编程模块及所述转频提取模块连接，用于在所述风机当前处于未满发状态时，根据各叶片的叶根载荷监测结果及所述控制函数对当前叶片进行独立变桨操作；
    所述主控制器，还用于在所述风机当前处于满发状态时，根据各叶片的叶根载荷监测结果判断所有叶片的叶根载荷是否一致，若不一致，则根据所述叶片的叶根载荷监测结果实时对当前叶片进行独立变桨操作，以使所有叶片的叶根载荷保持一致性。
  8. 如权利要求7所述的风力发电机独立变桨调整系统，其特征在于，所述叶根载荷监测模块包括若干沿周向布置在各叶片叶根处的MEMS光纤载荷传感器。
  9. 如权利要求8所述的风力发电机独立变桨调整系统，其特征在于，所述风力发电机独立变桨调整系统还包括若干配置在所述MEMS光纤载荷传感器附近的光纤温度传感器，所述光纤温度传感器用于对所述MEMS光纤载荷传感器 进行温度补偿。
  10. 如权利要求8所述的风力发电机独立变桨调整系统，其特征在于，所述风力发电机独立变桨调整系统还包括布置在所述叶片叶根处的加速度传感器或倾角传感器，所述加速度传感器或倾角传感器用于监测所述叶片的桨距角。
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