CN112177864A - Method and device for identifying extreme wind shear of wind turbine generator - Google Patents
Method and device for identifying extreme wind shear of wind turbine generator Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention provides a method for identifying extreme wind shear of a wind turbine generator, which comprises the following steps: respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology; calculating the flapping bending moment of the root of each blade according to the reflection wavelength data; filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, an overturning moment of the wind wheel and a yawing moment of the wind wheel; calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment; and comparing the wind shear index calculation value with a preset maximum wind shear index, and judging whether the wind turbine generator is in an extreme wind shear working condition. According to the method, the air-out shear strength is calculated by monitoring the flapping bending moment of the blade root, so that the extreme wind shear working condition is identified, and the investment of expensive equipment and maintenance cost in the traditional wind shear measuring mode is avoided.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method and a device for identifying extreme wind shear of a wind turbine generator.
Background
Wind shear is an atmospheric phenomenon, also commonly referred to as wind shear from the wind resource analysis of wind turbines, and is a parameter used to characterize the relationship between wind speeds at different altitudes. Wind shear is divided into horizontal shear, vertical shear and vertical wind shear of horizontal wind, and the vertical wind shear of the horizontal wind mainly affects the wind turbine generator, especially the height of a tower of the wind turbine generator is increased, and under the condition that the diameter of a wind wheel is larger and larger, the wind shear affects the wind turbine generator more and more severely.
Wind shear is one of important factors threatening the safety of the wind turbine, the larger the wind shear index is, the faster the wind speed is increased along with the increase of the height, and the overturning moment of the wind turbine can be increased when the wind wheel bears the unbalanced wind load, especially the extreme wind shear, so that the adaptability of the wind turbine can be greatly limited. Therefore, the wind turbine generator set has to meet a certain wind shear range during design, and when the wind shear index exceeds the design range, the wind turbine generator set has safety threat, and the wind turbine generator set may be damaged or blades are broken in the past.
With the refinement of the design of the wind turbine generator, a new control mode (such as capacity reduction, pitch angle adjustment and the like) is developed for the purpose of improving the adaptability of the wind turbine generator, so that it is necessary to reduce the influence of extreme wind shear on the wind turbine generator, but on the premise of effectively identifying the extreme wind shear and avoiding the influence on the wind turbine generator under normal working conditions. The traditional wind shear measurement adopts a wind measuring tower, a laser radar or a wind profile radar, and achieves the purpose of measuring wind shear by measuring wind speeds at different heights. But the dependence on the terrain is high, the service life of measuring equipment and high cost are difficult to meet the scale use requirement of the wind turbine generator.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the method and the device for identifying the extreme wind shear of the wind turbine generator, and aims to solve the technical problems that the existing wind shear measurement technology has high dependence on terrain, the service life of measurement equipment and high cost are difficult to meet the large-scale use requirement of the wind turbine generator.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method for identifying extreme wind shear of a wind turbine generator comprises the following steps:
respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology;
calculating the flapping bending moment of the root of each blade according to the reflection wavelength data;
filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, an overturning moment of the wind wheel and a yawing moment of the wind wheel;
calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment;
and comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is greater than the preset maximum wind shear index, judging that the wind turbine generator set is in an extreme wind shear working condition.
Optionally, the acquiring reflected wavelength data of three blades of the wind turbine generator based on the ultraviolet exposure technology includes:
at least four data acquisition points are arranged at the root of each blade, and the measurement wavelength and the central wavelength of each data acquisition point are acquired.
Optionally, the flapping bending moment of each blade root is calculated according to the reflection wavelength data, and the following formula is satisfied:
wherein λ is the measurement wavelength, λ0Is a central wavelength, K∈Belongs to the strain coefficient, calculated values of strain of each data acquisition point,
is a matrix of bending moment relationships, MflagAnd waving bending moment for the root of the blade.
Optionally, the filtered blade root flapping bending moment is projected to a wind wheel rotation plane based on a multi-blade transformation algorithm, and a wind wheel thrust moment, a wind wheel overturning moment and a wind wheel yaw moment are calculated, and the following formulas are satisfied:
wherein m isflag1、mflag2、mflag3Flapping bending moment psi for the filtered trailing blade root of each blade1Is the wind wheel azimuth angle, m0As a thrust moment, m1cIs the wind wheel overturning moment, m1sThe yaw moment of the wind wheel.
Optionally, the wind shear index calculation value is calculated according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yaw moment, and the following formula is satisfied:
wherein, VshearFor the final wind shear index calculation, A is the dimensionless value of the imbalance degree of the wind wheel, KshearIs a third order matrix of model coefficients.
An identification device for extreme wind shear of a wind turbine generator, comprising:
the measuring module is used for respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology;
the processing module is used for calculating the flapping bending moment of the root of each blade according to the reflected wavelength data; then filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, a capsizing moment of the wind wheel and a yawing moment of the wind wheel; then calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment; and finally, comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is larger than the preset maximum wind shear index, judging that the wind turbine generator is in an extreme wind shear working condition.
Optionally, the measurement module includes fiber grating strain sensors, and at least four fiber grating strain sensors are disposed at each blade root.
According to the technical scheme, the invention has the beneficial effects that:
in one aspect, the invention provides a method for identifying extreme wind shear of a wind turbine generator, which comprises the following steps: respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology; calculating the flapping bending moment of the root of each blade according to the reflection wavelength data; filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, an overturning moment of the wind wheel and a yawing moment of the wind wheel; calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment; and comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is greater than the preset maximum wind shear index, judging that the wind turbine generator set is in an extreme wind shear working condition. According to the method, the air-out shear strength is calculated by monitoring the flapping bending moment of the blade root, so that the extreme wind shear working condition is identified, the investment of expensive equipment and maintenance cost in the traditional wind shear measuring mode is avoided, and the unit operation optimization technology under the extreme wind shear working condition is favorably applied.
On the other hand, the invention also provides a wind turbine extreme wind shear identification device, which comprises: the measuring module is used for respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology; the processing module is used for calculating the flapping bending moment of the root of each blade according to the reflected wavelength data; then filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, a capsizing moment of the wind wheel and a yawing moment of the wind wheel; then calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment; and finally, comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is larger than the preset maximum wind shear index, judging that the wind turbine generator is in an extreme wind shear working condition. The device monitors the flapping bending moment of the blade root through the measuring module to calculate the air-out shear strength, further identifies the extreme wind shear working condition, avoids the investment of expensive equipment and maintenance cost of the traditional wind shear measuring mode, and is beneficial to applying the unit operation optimization technology under the extreme wind shear working condition.
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In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a flowchart of a method for identifying extreme wind shear of a wind turbine.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
The invention is based on the first-order harmonic response of the blade, and through blade model simulation, the blade harmonic has specific dependence on the wind characteristic, and each wind state, such as vertical wind shear or horizontal wind shear, leaves a specific recognizable mark on the harmonic response of the blade. By this feature, the wind conditions can be reliably inferred, only the first harmonic of the blade loading being observed. These quantities can be readily obtained by processing on-line measurements obtained from suitable sensors, such as strain gauges or optical fibers.
Referring to fig. 1, the present invention provides a method for identifying extreme wind shear of a wind turbine, including the following steps:
s1, respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology;
specifically, at least four data acquisition points are arranged at the root of each blade, the measurement wavelength and the central wavelength of each data acquisition point are acquired, specifically, a fiber grating strain sensor can be arranged at the data acquisition points, and the fiber grating strain sensor can utilize an ultraviolet exposure technology to cause the periodic change of the refractive index in the optical fiber core to measure the change of the reflection wavelength.
S2, calculating the flapping bending moment of the root of each blade according to the reflection wavelength data;
specifically, the measurement wavelength and center wavelength collected at each data acquisition point can be calculated in combination with the following formula:
wherein λ is the measurement wavelength, λ0Is a central wavelength, K∈The strain coefficient belongs to the strain calculation value of the data acquisition point;
and according to the strain calculation values of the four data acquisition points of each blade root, the flapping bending moment of each blade root can be obtained through the following formula:
wherein
Is a matrix of bending moment relationships, MflagWaving the blade root with a bending moment1、∈2、∈3、∈4Calculated strain values for four data acquisition points respectively.
S3, filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a wind wheel rotation plane based on a multi-blade transformation algorithm, and calculating to obtain a wind wheel thrust moment, a wind wheel overturning moment and a wind wheel yawing moment;
specifically, the blade root flapping bending moment M is filtered by a second-order low-pass filterflagAnd then calculating necessary amplitude by utilizing multi-blade coordinate transformation multi-coordinate transformation (MBC) of Coleman/Feingold, namely projecting the filtered blade root flapping bending moment to a wind wheel rotation plane, and specifically calculating a wind wheel thrust moment, a wind wheel overturning moment and a wind wheel yawing moment by the following formulas:
wherein m isflag1、mflag2、mflag3Flapping bending moment psi for the filtered trailing blade root of each blade1Is the wind wheel azimuth angle, m0As a thrust moment, m1cIs the wind wheel overturning moment, m1sThe yaw moment of the wind wheel.
Specifically, the rotor azimuth angle ψ1The position signal of the wind wheel blade is measured by a wind wheel encoder, and the vertical downward direction of the No. 1 blade of the wind turbine generator is 0 DEG, and the variation range is 0-360 DEG
S4, calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment;
specifically, through wind wheel thrust moment, wind wheel overturning moment and wind wheel yawing moment, a dimensionless value A of the unbalance degree of the wind wheel can be calculated, and the following formula is specifically adopted:
then, a wind shear derivation model is obtained by adopting complete machine simulation, and a third-order matrix K of model coefficients is identified by adopting a least square methodshearAnd calculating the final wind shear index of the wind turbine generator set by the following formula:
wherein, VshearFor the final wind shear index calculation, A is the dimensionless value of the imbalance degree of the wind wheel, KshearIs a third order matrix of model coefficients.
And S5, comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is larger than the preset maximum wind shear index, judging that the wind turbine generator is in an extreme wind shear working condition.
According to the method, the air-out shear strength is calculated by monitoring the flapping bending moment of the blade root, so that the extreme wind shear working condition is identified, the investment of expensive equipment and maintenance cost in the traditional wind shear measuring mode is avoided, and the unit operation optimization technology under the extreme wind shear working condition is favorably applied. The invention provides a reliable and low-cost extreme wind shear identification method, and the identification result can be used for operation of a unit, a safety control strategy or data statistics, so that necessary foundation is provided for optimizing the unit action under the extreme wind shear working condition and reducing the unit key load under the extreme wind shear wind condition.
The invention also provides a device for identifying extreme wind shear of the wind turbine generator, which comprises:
the measuring module is used for respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology;
the processing module is used for calculating the flapping bending moment of the root of each blade according to the reflected wavelength data; then filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, a capsizing moment of the wind wheel and a yawing moment of the wind wheel; then calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment; and finally, comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is larger than the preset maximum wind shear index, judging that the wind turbine generator is in an extreme wind shear working condition. The identification device is used for implementing the identification method of the extreme wind shear of the wind turbine generator.
As a further improvement to the above scheme, the measurement module includes fiber grating strain sensors, and at least four fiber grating strain sensors are disposed at each blade root.
The device monitors the flapping bending moment of the blade root through the measuring module to calculate the air-out shear strength, further identifies the extreme wind shear working condition, avoids the investment of expensive equipment and maintenance cost of the traditional wind shear measuring mode, and is beneficial to applying the unit operation optimization technology under the extreme wind shear working condition.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (7)
1. A method for identifying extreme wind shear of a wind turbine generator is characterized by comprising the following steps:
respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology;
calculating the flapping bending moment of the root of each blade according to the reflection wavelength data;
filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, an overturning moment of the wind wheel and a yawing moment of the wind wheel;
calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment;
and comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is greater than the preset maximum wind shear index, judging that the wind turbine generator set is in an extreme wind shear working condition.
2. The method for identifying extreme wind shear of a wind turbine generator according to claim 1, wherein the collecting the reflected wavelength data of three blades of the wind turbine generator based on the ultraviolet exposure technology comprises:
at least four data acquisition points are arranged at the root of each blade, and the measurement wavelength and the central wavelength of each data acquisition point are acquired.
3. The method for identifying extreme wind shear of a wind turbine generator set according to claim 2, wherein the calculation of the flap bending moment of each blade root according to the reflection wavelength data satisfies the following formula:
wherein λ is the measurement wavelength, λ0Is a central wavelength, K∈Belongs to the strain coefficient, calculated values of strain of each data acquisition point,
is a matrix of bending moment relationships, MflagAnd waving bending moment for the root of the blade.
4. The method for identifying extreme wind shear of a wind turbine generator set according to claim 1, wherein the filtered blade root flapping bending moment is projected to a wind turbine rotation plane based on a multi-blade transformation algorithm, and a wind turbine thrust moment, a wind turbine overturning moment and a wind turbine yaw moment are calculated, and the following formulas are satisfied:
wherein m isflag1、mflag2、mflag3Flapping bending moment psi for the filtered trailing blade root of each blade1Is the wind wheel azimuth angle, m0As a thrust moment, m1cIs the wind wheel overturning moment, m1sThe yaw moment of the wind wheel.
5. The method for identifying extreme wind shear of a wind turbine generator set according to claim 1, wherein a wind shear index calculation value is calculated according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yaw moment, and the following formula is satisfied:
wherein, VshearFor the final wind shear index calculation, A is the dimensionless value of the imbalance degree of the wind wheel, KshearIs a third order matrix of model coefficients.
6. An identification device for extreme wind shear of a wind turbine generator system, comprising:
the measuring module is used for respectively acquiring reflected wavelength data of three blades of the wind turbine generator based on an ultraviolet exposure technology;
the processing module is used for calculating the flapping bending moment of the root of each blade according to the reflected wavelength data; then filtering the waving bending moment of the blade root, projecting the waving bending moment of the blade root after filtering to a rotating plane of the wind wheel based on a multi-blade transformation algorithm, and calculating to obtain a thrust moment of the wind wheel, a capsizing moment of the wind wheel and a yawing moment of the wind wheel; then calculating a wind shear index calculation value according to the wind wheel thrust moment, the wind wheel overturning moment and the wind wheel yawing moment; and finally, comparing the wind shear index calculation value with a preset maximum wind shear index, and if the wind shear index calculation value is larger than the preset maximum wind shear index, judging that the wind turbine generator is in an extreme wind shear working condition.
7. The wind turbine generator extreme wind shear identification device according to claim 6, wherein said measurement module comprises fiber grating strain sensors, and at least four of said fiber grating strain sensors are disposed at each blade root.
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