JP2022168865A - Method for diagnosing wind turbine blade - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for diagnosing wind turbine blades with real-time measurement of distortion of wind turbine blades in an existing wind power generation device.

SOLUTION: A method for diagnosing wind turbine blades according to at least one embodiment of the present disclosure is a method for diagnosing wind turbine blades of an existing wind turbine generation device, and comprises the steps of: additionally installing sensors for detecting distortion of wind turbine blades; obtaining a detection signal from the sensors; calculating a load acting on the wind turbine blades based on the detection signal; and detecting the presence or absence of an abnormality in the wind turbine blades based on the detection signal. The sensors additionally installed in the step of additionally installing the sensors include an abnormality detection sensor that detects the distortion of the wind turbine blades in order to detect the presence or absence of an abnormality. In the step of detecting the presence or absence of an abnormality, the presence or absence of an abnormality is detected based on the detection signal from the abnormality detection sensor.

SELECTED DRAWING: Figure 5

COPYRIGHT: (C)2023,JPO&INPIT

Description

The present disclosure relates to a method for diagnosing a wind turbine blade.

It is known to detect damage to the wind turbine blades by measuring strain in the wind turbine blades of a wind turbine generator (see Patent Document 1, for example).

JP 2016-156674 A

In Patent Document 1, the strain of the wind turbine blade can be measured in real time by a sensor attached to the wind turbine blade. Therefore, it is possible to grasp the load acting on the wind turbine blades during operation of the wind turbine generator.
Therefore, for example, when detecting that an abnormal load acts on the wind turbine blade during operation of the wind turbine blade, the operation of the wind turbine blade is stopped. It is also conceivable to use sensors.

However, if the wind turbine blades of existing wind power generators are not provided with such sensors as described above, operation management using the sensors cannot be performed.

In view of the above circumstances, at least one embodiment of the present disclosure aims to provide a method for diagnosing wind turbine blades by measuring the distortion of wind turbine blades in an existing wind power generator in real time.

(1) A method for diagnosing a wind turbine blade according to at least one embodiment of the present disclosure includes:
A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
with
In the step of additionally installing the sensor, the first sensor is positioned at one of two positions facing each other along the chord direction of the wind turbine blade, among circumferential positions in the blade root centered on the blade axis of the wind turbine blade. and placing a second sensor at one of two positions facing each other along a direction perpendicular to the code direction,
In the step of calculating the load, the load is calculated based on the detection signal from the first sensor and the detection signal from the second sensor.

(2) A method for diagnosing a wind turbine blade according to at least one embodiment of the present disclosure includes:
A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
Prepare.

According to at least one embodiment of the present disclosure, it is possible to provide a method for diagnosing a wind turbine blade by measuring distortion of the wind turbine blade in real time in an existing wind turbine generator.

1 is a schematic diagram showing the overall configuration of a wind turbine of a wind turbine generator according to some embodiments; FIG. FIG. 2 is a schematic diagram showing a cross section of the blade root portion of the wind turbine blade according to one embodiment, perpendicular to the longitudinal direction of the wind turbine blade. FIG. 10 is a schematic diagram showing a cross section orthogonal to the longitudinal direction of the wind turbine blade at the blade root portion of the wind turbine blade according to another embodiment. FIG. 4 is a block diagram of the configuration of an embodiment for processing a converted signal; FIG. 10 is a block diagram of the configuration of another embodiment regarding processing of a converted signal; 3C is a perspective view schematically showing the inside of the hub in the embodiment shown in FIG. 3B; FIG. 4 is a flow chart showing a processing procedure of a wind turbine blade diagnosis method according to some embodiments. FIG. 4 is a block diagram relating to calculation of a leeward bending moment per blade and a torque component per blade; Figure 7 is a more detailed block diagram of a portion of the block diagram of Figure 6; FIG. 4 is a block diagram for calculation of a synthetic torque component; FIG. 4 is a block diagram of the calculation of the bending moment for the wind turbine rotor and the bending moment for the nacelle; FIG. 4 is a diagram for explaining a bending moment of a wind turbine blade; FIG. 4 is a diagram for explaining a bending moment about a wind turbine rotor; FIG. 4 is a diagram for explaining a bending moment about a nacelle; FIG. 4 is a flow chart showing a processing procedure for a sensor calibration process; 1 is an example of a schematic cross-sectional view of a wind turbine blade according to some embodiments in a cross section orthogonal to the blade axis; FIG.

Several embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, the dimensions, materials, shapes, relative arrangements, etc. of the components described as the embodiment or shown in the drawings are not meant to limit the scope of the present disclosure, but are merely illustrative examples. do not have.
For example, expressions denoting relative or absolute arrangements such as "in a direction", "along a direction", "parallel", "perpendicular", "center", "concentric" or "coaxial" are strictly not only represents such an arrangement, but also represents a state of relative displacement with a tolerance or an angle or distance to the extent that the same function can be obtained.
For example, expressions such as "identical", "equal", and "homogeneous", which express that things are in the same state, not only express the state of being strictly equal, but also have tolerances or differences to the extent that the same function can be obtained. It shall also represent the existing state.
For example, expressions that express shapes such as squares and cylinders do not only represent shapes such as squares and cylinders in a geometrically strict sense, but also include irregularities and chamfers to the extent that the same effect can be obtained. The shape including the part etc. shall also be represented.
On the other hand, the expressions "comprising", "comprising", "having", "including", or "having" one component are not exclusive expressions excluding the presence of other components.

(overall structure)
First, the configuration of a wind turbine generator to which a method for diagnosing a wind turbine blade according to some embodiments is applied will be described. FIG. 1 is a schematic diagram showing the overall configuration of a wind turbine of a wind turbine generator according to some embodiments. As shown in the figure, the wind turbine 1a includes a wind turbine rotor 5 including at least one wind turbine blade 3 and a hub 2 to which the wind turbine blade 3 is attached. The wind turbine rotor 5 is provided above the tower 6 and is rotatably supported by the nacelle 4 supported by the tower 6. When the wind turbine blades 3 receive wind, the wind turbine rotor 5 including the wind turbine blades 3 rotates. It has become.

In some embodiments the wind turbine 1 a is part of the wind power installation 1 . The nacelle 4 accommodates a generator (not shown) and a power transmission mechanism (not shown) for transmitting the rotation of the wind turbine rotor 5 to the generator. The wind turbine generator 1 is configured such that rotational energy transmitted from the wind turbine rotor 5 to the generator via the power transmission mechanism is converted into electrical energy by the generator.

FIG. 2A is a schematic diagram showing a cross section of the blade root portion of the wind turbine blade according to one embodiment, perpendicular to the longitudinal direction of the wind turbine blade.
FIG. 2B is a schematic diagram showing a cross section orthogonal to the longitudinal direction of the wind turbine blade at the blade root portion of the wind turbine blade according to another embodiment.
As shown in FIGS. 1 and 2A, the wind turbine 1a has four optical fiber sensors 12 (12A to 12D) attached to each blade root 3a of the wind turbine blades 3 as sensors for detecting distortion of the wind turbine blades 3. ). As shown in FIGS. 1 and 2A , the wind turbine 1 a includes a light source/signal processing unit 10 including a light incidence section 17 and a light detection section 18 .
As shown in FIG. 2B, the wind turbine 1a includes two optical fiber sensors 12 (12A and 12D) attached to each blade root portion 3a of the wind turbine blades 3 as sensors for detecting distortion of the wind turbine blades 3. may be As shown in FIG. 2B , the wind turbine 1 a may include a light source/signal processing unit 10 including a light incidence section 17 and a light detection section 18 .

The optical fiber sensor 12 has a diffraction grating section whose refractive index changes periodically in the longitudinal direction.
The optical fiber sensor 12 according to some embodiments is an FBG (Fiber Bragg Grating) sensor.
When light with a broadband spectrum is incident on the optical fiber sensor 12, the reflections at the diffraction grating part whose refractive index changes periodically are reflected only for specific wavelengths depending on the grating interval (grating period). interfere in a constructive way. This causes the fiber optic sensor 12 to reflect only specific wavelength components of its configuration and transmit other wavelengths.
When the strain applied to the optical fiber sensor 12 and the ambient temperature change, the refractive index and grating interval of the diffraction grating section change, and the wavelength λ _o of the reflected light changes accordingly.
The difference between the wavelength λ _o of the reflected light in the optical fiber sensor 12 and the initial wavelength λ _i is affected by the strain ε _z and the temperature ΔT of the optical fiber sensor 12 . Therefore, if the initial wavelength λ _i is a default value, the strain occurring in the optical fiber sensor 12 and the temperature of the optical fiber can be detected by measuring the wavelength λ _o of the reflected light.

The optical fiber sensor 12 and the light source/signal processing unit 10 are added to the existing wind turbine generator 1 later, as will be described later.
Four optical fiber sensors 12A to 12D are attached to the blade root portion 3a of each wind turbine blade 3 shown in FIG. 2A. As shown in FIG. 2A, the blade root portion 3a of the wind turbine blade 3 has a substantially circular cross section, and the optical fiber sensors 12A to 12D are attached to the wall surface of the blade root portion 3a at intervals of 90°. In each wind turbine blade 3 shown in FIG. 2A, the four optical fiber sensors 12A, 12B, 12C, and 12D are arranged in this order on the ventral side (HP side) 21, the trailing edge side 24, and the dorsal side (LP side) of the wind turbine blade 3, respectively. side) 22 and the leading edge side 23 are attached to the wall surface of the blade root portion 3a. More specifically, in each of the wind turbine blades 3 shown in FIG. 2A, the optical fiber sensor 12 has the position along the code C direction among the circumferential positions in the blade root portion 3a centered on the blade axis L0 of the wind turbine blade 3. A leading edge side sensor (optical fiber sensor 12D) and a trailing edge side sensor (optical fiber sensor 12B) respectively arranged at two positions on the opposite leading edge side 23 and trailing edge side 24, and along the direction orthogonal to the code C direction It includes a ventral sensor (optical fiber sensor 12A) and a dorsal sensor (optical fiber sensor 12C) placed at two positions on the ventral side 21 and the dorsal side 22 facing each other.

In each wind turbine blade 3 shown in FIG. 2A, when the blade root portion 3a is distorted, each optical fiber sensor 12A to 12D is distorted according to the distortion at the mounting location.
Also, these four optical fiber sensors 12A, 12B, 12C and 12D are connected in series by an optical fiber cable 16 in this order. Connectors (13, 15) are provided at both ends of the optical fiber cable 16, and an optical fiber cable 14 for connecting the optical fiber sensors 12A to 12D and the light source/signal processing unit 10, and the optical fiber sensors 12A to 12D. are connected in series via a connector 13 or a connector 15.

In addition, in each wind turbine blade 3 shown in FIG. 2A, four optical fiber sensors 12A to 12D are attached to one wind turbine blade 3, but in each wind turbine blade 3 shown in FIG. , two optical fiber sensors 12 are attached. In each wind turbine blade 3 shown in FIG. 2B , the optical fiber sensor 12 is connected to the leading edge side 23 of the wind turbine blade 3 facing along the direction of the chord C, among the circumferential positions in the blade root portion 3 a centered on the blade axis L0. An optical fiber sensor 12 arranged at one of two positions, the trailing edge side 24, and one of two positions, the ventral side 21 and the dorsal side 22, which face each other along the direction perpendicular to the direction of the code C. and a fiber optic sensor 12 located in the . Two optical fiber sensors 12 attached to each wind turbine blade 3 shown in FIG. The optical fiber sensor 12D arranged on the edge side 23 and the optical fiber sensor 12A arranged on the ventral side 21 of the two positions of the ventral side 21 and the dorsal side 22 facing each other along the direction perpendicular to the direction of the code C. and
One of the two optical fiber sensors 12 attached to each wind turbine blade 3 shown in FIG. It is possible to have the fiber optic sensor 12B located on the trailing edge side 24 instead of the fiber optic sensor 12D located on the leading edge side 23 of the two locations. Moreover, the other one of the two optical fiber sensors 12 attached to each wind turbine blade 3 shown in FIG. The optical fiber sensor 12C may be arranged on the dorsal side 22 instead of the optical fiber sensor 12A arranged on the ventral side 21 of the two positions.

In the above description, an example in which the optical fiber sensor 12 is attached to the blade root portion 3a of the wind turbine blade 3 has been described, but the position of the wind turbine blade 3 at which the optical fiber sensor 12 is attached is not limited to the blade root portion 3a. For example, the optical fiber sensor 12 may be attached to a hub structure (not shown) that supports the wind turbine blades 3 so that the pitch angle can be changed.

Also, for example, the optical fiber sensor 12 may be attached to a blade root bearing, that is, a pitch bearing 300 shown in FIG. 4 to be described later. Specifically, the optical fiber sensor 12 may be attached to a fastening member such as a bolt or nut (not shown) described below.
For example, in the wind turbine 1a according to some embodiments, the pitch bearing 300 has a configuration in which rolling elements (not shown) are held between an inner ring 304 and an outer ring 306 that can rotate relative to each other. The inner ring 304 is fixed to the wind turbine blade 3 with bolts (not shown), and the outer ring 306 is fixed to the hub 2 with bolts (not shown). By arranging such a pitch bearing 300 between the hub 2 and the wind turbine blades 3, the wind turbine blades 3 fixed to the inner ring 304 move in the pitch direction (wind turbine blades) with respect to the hub 2 fixed to the outer ring 306. 3 around the blade axis L0).

In some embodiments, the optical fiber sensor 12 may be attached to the above-described bolt (not shown) or a fastening member such as a nut used with this bolt.

In the above description, the optical fiber sensor 12 is used as an example of the sensor for detecting the distortion of the wind turbine blades 3, but the sensor for detecting the distortion of the wind turbine blade 3 is limited to the optical fiber sensor 12. not. A sensor for detecting the strain of the wind turbine blade 3 may be, for example, a metal resistance strain gauge or a semiconductor strain gauge.
If the sensor for detecting the strain of the wind turbine blade 3 is, for example, a metal resistance strain gauge or a semiconductor strain gauge, the strain gauge signal conditioner 11 (see FIG. 3B) is used instead of the light source/signal processing unit 10. use.

The light source and signal processing unit 10 including the light input portion 17 and the light detection portion 18 is installed inside the hub 2 in some embodiments.
The light input section 17 is configured to input light to the optical fiber sensors 12A-12D. The light incident part 17 has, for example, a light source capable of emitting light having a broadband spectrum.
Also, the light detection section 18 is configured to detect reflected light from the diffraction grating section of each of the optical fiber sensors 12A to 12D.

The broadband light emitted from the light incidence part 17 propagates through the optical fiber cables (14, 16) and is incident on each of the optical fiber sensors 12A-12D. In each of the optical fiber sensors 12A to 12D, of the incident light, only light having a specific wavelength corresponding to the strain and temperature of each diffraction grating is reflected by the diffraction grating. Reflected light reflected by the diffraction grating propagates through the optical fiber cables (16, 14) and is detected by the photodetector 18. FIG.
The optical fiber sensors 12A to 12D are connected in series, and the distance from the light incidence section 17 to each optical fiber sensor 12A to 12D and the distance from the optical fiber sensors 12A to 12D to the light detection section 18 are mutually different. different. For this reason, in the photodetector 18, the light is detected by the elapsed time from when the light is emitted by the light incident portion 17 until the reflected light from each of the optical fiber sensors 12A to 12D is detected by the photodetector 18. It is possible to determine which optical fiber sensor 12 is responsible for each reflected light.

In the following description, the reflected light from each diffraction grating portion of the optical fiber sensors 12A to 12D and the optical signal from the optical fiber sensors 12A to 12D are also referred to as detection signals.
If the sensor for detecting the strain of the wind turbine blade 3 is, for example, a metal resistance strain gauge or a semiconductor strain gauge, the signal from the metal resistance strain gauge or semiconductor strain gauge is also referred to as a detection signal.
Further, in the following description, the electric signal relating to the distortion of the wind turbine blade 3, which is output from the light source/signal processing unit 10, is also referred to as a conversion signal of the detection signal or simply as a conversion signal.
Similarly, if the sensor for detecting the strain of the wind turbine blade 3 is, for example, a metal resistance strain gauge or a semiconductor strain gauge, the strain gauge signal conditioner 11 outputs an electric signal related to the strain of the wind turbine blade 3. Also referred to as the transformed signal of the detection signal, or simply the transformed signal.

Further, in the following description, the wind turbine 1 a is described assuming that the wind turbine rotor 5 includes, for example, three wind turbine blades 3 . When it is necessary to distinguish between the three wind turbine blades 3 individually, one of the three wind turbine blades 3 is referred to as the first wind turbine blade 31, and the two wind turbine blades other than the first wind turbine blade 31 are referred to as the first wind turbine blades 31. One of the 3 is called a second wind turbine blade 32 and the remaining wind turbine blades 3 are called a third wind turbine blade 33 .

In the following description, the optical fiber sensors 12 (12A-12D) are also simply referred to as sensors 12 (12A-12D) in the specification and drawings of the present application.

FIG. 3A is a block diagram of the configuration of one embodiment for processing transform signals.
The embodiment shown in FIG. 3A is a block diagram in which the sensor for detecting distortion of the wind turbine blade 3 is the optical fiber sensor 12 .
The embodiment shown in FIG. 3A is also an embodiment that can be implemented mainly when the existing wind power generator 1 is a wind power generator manufactured by the company.
In the embodiment shown in FIG. 3A , the existing arithmetic device 50 included in the existing wind turbine generator 1 includes a hub PLC 51 arranged inside the hub 2 and a nacelle PLC 53 arranged inside the nacelle 4 .
In the embodiment shown in FIG. 3A, the optical fiber sensor 12 and the light source/signal processing unit 10 are added to the existing wind turbine generator 1 later, as described above. In the embodiment shown in FIG. 3A, a light source/signal processing unit 10 is provided for every three wind turbine blades 3 .

In the embodiment shown in FIG. 3A, power is supplied to the light source/signal processing unit 10 from a hub power supply 57 that is an existing power supply in the hub 2 .

In the embodiment shown in FIG. 3A, the converted signal from light source and signal processing unit 10 is output to hub PLC 51 .
In the embodiment shown in FIG. 3A, based on the converted signal from the light source/signal processing unit 10, the hub PLC 51 calculates the bending moment and the like acting on the wind turbine blade 3 due to the wind load, as will be described in detail later. can be

When the hub PLC 51 calculates the bending moment or the like acting on the wind turbine blades 3 due to the wind load, the hub PLC 51 outputs the calculation result to the nacelle PLC 53 .
In this case, the nacelle PLC 53 outputs the calculation result received from the hub PLC 51 to the SCADA 61 that is a device for remotely monitoring the wind turbine generator 1 .

Further, in the embodiment shown in FIG. 3A, the nacelle PLC 53 may calculate the bending moment or the like acting on the wind turbine blade 3 due to the wind load based on the converted signal from the light source/signal processing unit 10 . In this case, hub PLC 51 outputs the conversion signal from light source/signal processing unit 10 to nacelle PLC 53 .
The nacelle PLC 53 calculates the bending moment and the like acting on the wind turbine blades 3 due to the wind load based on the converted signal from the light source/signal processing unit 10 received via the hub PLC 51 . The nacelle PLC 53 then outputs the calculation result to the SCADA 61 .

FIG. 3B is a block diagram of the configuration of another embodiment for processing the transform signal.
The embodiment shown in FIG. 3B is a block diagram in which the sensor for detecting the strain of the wind turbine blade 3 is a sensor 42 such as a metal resistance strain gauge or a semiconductor strain gauge.
Further, in the embodiment shown in FIG. 3B, mainly when the existing wind turbine generator 1 is a wind turbine generator manufactured by another company, or even when the existing wind turbine generator 1 is a wind turbine generator manufactured by the company, This is also an embodiment that can be implemented when there is a demand to calculate the bending moment or the like acting on the wind turbine blades 3 due to the wind load with a computing device having higher processing capability than the existing computing device 50 .
In the embodiment shown in FIG. 3B, a wind turbine PLC 55, which is the existing arithmetic device 50 included in the existing wind turbine generator 1, is provided.
The wind turbine PLC 55 may be the hub PLC 51 described above, the nacelle PLC 53 , or the hub PLC 51 and the nacelle PLC 53 .

In the embodiment shown in FIG. 3B, the sensor 42 and the strain gauge signal conditioner 11 are added to the existing wind turbine generator 1 later.
In addition, in the embodiment shown in FIG. 3B, mainly, an arithmetic device (additional arithmetic device) 63 for calculating the bending moment and the like acting on the wind turbine blades 3 due to the wind load, and electric power is supplied to the additional arithmetic device 63. A possible power storage device 65 is added to the existing wind turbine generator 1 later.
In the embodiment shown in FIG. 3B, a communication device 67 and a relay 69 are added to the existing wind turbine generator 1 later.

In the embodiment shown in FIG. 3B, the additional computing device 63 may be, for example, a small personal computer, a so-called single board computer, or the like.
FIG. 4 is a perspective view schematically showing the inside of hub 2 in the embodiment shown in FIG. 3B. In the embodiment shown in FIG. 3B, an additional computing device 63 may be arranged, for example, in the vicinity of the existing computing device 50 of the hub 2, as shown in FIG. 4A.

Note that the computing power of a small personal computer or a so-called single board computer additionally installed as the additionally installed computing device 63 is considered to be higher than the computing power of the existing computing device 50 included in the existing wind turbine generator 1 . Therefore, as in the embodiment shown in FIG. You may make it set the sampling frequency in the sensor 12 to a high frequency compared with .

In the embodiment shown in FIG. 3B, the power storage device 65 is a device that can supply power to the strain gauge signal conditioner 11 and the additional arithmetic device 63 while receiving power from the hub power supply 57, for example. , is a device having a power storage function capable of supplying power to the strain gauge signal conditioner 11 and the additional arithmetic device 63 for a specified period of time even if the power supply from the hub power supply 57 is interrupted.
For example, the power storage device 65 may have a rechargeable secondary battery. Note that the power storage device 65 may have a primary battery instead of a secondary battery.

In the embodiment shown in FIG. 3B, the communication device 67 wirelessly transmits the calculation result of the bending moment acting on the wind turbine blades 3 due to the wind load calculated by the additional calculation device 63 to a device outside the wind turbine generator 1 . A device for transmitting.
In the embodiment shown in FIG. 3B, the relay 69 is used when outputting an alarm signal or the like to the wind turbine PLC 55 based on the calculation result of the bending moment or the like calculated by the additional calculation device 63, for example. In addition, in the embodiment shown in FIG. 3B , the relay 69 is not necessarily required, and for example, the calculation in the additional calculation device 63 may be directly output to the wind turbine PLC 55 .

In the embodiment shown in FIG. 3B, the converted signal from strain gauge signal conditioner 11 is output to additional computing device 63 .
In the embodiment shown in FIG. 3B, based on the converted signal from the strain gauge signal conditioner 11, the additional calculation device 63 is configured to calculate the bending moment and the like acting on the wind turbine blade 3 due to the wind load. .
In the embodiment shown in FIG. 3B, the additional calculation device 63 can wirelessly transmit the calculation results of the calculated bending moment and the like to devices outside the wind turbine generator 1 via the communication device 67 .
In addition, in the embodiment shown in FIG. 3B, the additional calculation device 63 determines whether or not to output an alarm signal or the like to the wind turbine PLC 55 based on the calculated bending moment or the like, and warns the wind turbine PLC 55. When it is determined to output a signal or the like, an alarm signal or the like can be output to the wind turbine PLC 55 via the relay 69 .

In the embodiment shown in FIG. 3B, even if some trouble occurs and the hub power supply 57 is cut off, power is still supplied from the power storage device 65 to the strain gauge signal conditioner 11 and the additional arithmetic device 63 for a prescribed period of time. can be supplied. That is, even if power supply from the wind turbine generator 1 side is cut off due to some problem, the power from the power storage device 65 can be used to operate the additionally installed arithmetic device 63 .

(Diagnostic method for wind turbine blades)
A method for diagnosing a wind turbine blade according to some embodiments will be described below.
FIG. 5 is a flow chart showing a processing procedure of a wind turbine blade diagnosis method according to some embodiments.
A method for diagnosing a wind turbine blade according to some embodiments is a method for diagnosing a wind turbine blade 3 of an existing wind turbine generator 1, comprising an additional installation step S10, a detection signal acquisition step S30, a load calculation step S50, Prepare. The wind turbine blade diagnostic method according to some embodiments may further include an external transmission step S70 and an abnormality detection step S90.

(Additional installation step S10)
In the wind turbine blade diagnostic method according to some embodiments, the additional installation step S10 is a step of additionally installing, for example, an optical fiber sensor 12 as a sensor for detecting distortion of the wind turbine blade 3 in the existing wind power generator 1. is. In the additional installation step S10 according to some embodiments, the sensor for detecting the strain of the wind turbine blade 3 may be a sensor 42 such as a metal resistance strain gauge or a semiconductor strain gauge.
As shown in FIGS. 1 and 2A, for example, the sensor additionally installed in the installation step S10 according to some embodiments has a position in the code C direction among circumferential positions in the blade root portion 3a centered on the blade axis L0. It may include a leading edge sensor (optical fiber sensor 12D) and a trailing edge sensor (optical fiber sensor 12B) located at two locations, respectively, on opposite leading edge side 23 and trailing edge side 24 along the . Sensors additionally installed in the additional installation step S10 according to some embodiments include, for example, as shown in FIGS. It may include a ventral sensor (optical fiber sensor 12A) and a dorsal sensor (optical fiber sensor 12C) located at two locations, respectively. A sensor 42 such as a strain gauge or a semiconductor strain gauge may be installed instead of the optical fiber sensors 12A to 12D.

In the additional installation step S10 according to some embodiments, two positions of the leading edge side 23 and the trailing edge side 24 facing each other along the direction of the chord C are selected from among the circumferential positions of the blade root portion 3a about the blade axis L0. For example, an optical fiber sensor 12 is arranged as a first sensor in one of the two positions, and a second sensor is arranged in one of the two positions of the ventral side 21 and the dorsal side 22 that face each other along the direction perpendicular to the direction of the code C. For example, an optical fiber sensor 12 may be installed as the . A sensor 42 such as a strain gauge or a semiconductor strain gauge may be installed instead of the optical fiber sensor 12 .

In the additional installation step S10 according to some embodiments, the additionally installed sensors 12 and 42 include an abnormality detection sensor that detects distortion of the wind turbine blades 3 to detect whether there is an abnormality in the wind turbine blades 3. Good. The abnormality detection sensors (sensors 12 and 42) may also be used as sensors 12 and 42 for outputting detection signals for calculating loads acting on the wind turbine blades 3, as will be described later. The sensors 12 and 42 may be dedicated sensors for detecting the presence or absence of the .

If the abnormality detection sensor is also used as the sensors 12 and 42 that output detection signals for calculating the load acting on the wind turbine blade 3, the number of additional sensors 12 and 42 can be reduced. can reduce the cost of additional installation.

If the abnormality detection sensors are the sensors 12 and 42 dedicated to detecting the presence or absence of an abnormality in the wind turbine blades 3, for example, the frequency of performing arithmetic processing for detecting the presence or absence of an abnormality in the wind turbine blades 3 can be suppressed. As a result, the processing of detection signals from the abnormality detection sensors can be simplified, and the calculation load can be reduced in the existing arithmetic device 50 and additional arithmetic device 63 of the existing wind turbine generator 1 such as the hub PLC 51 .

Moreover, in the additional installation step S10 according to some embodiments, as shown in FIGS. A device for receiving a detection signal from a sensor for detecting the strain of the wind turbine blade 3 and outputting a conversion signal, which is an electric signal related to the strain of the wind turbine blade 3, is additionally provided.

In the additional installation step S10 according to some embodiments, as shown in FIG. Alternatively, the communication device 67 may be added, and the relay 69 may be added.

In addition, in the additional installation step S10 according to some embodiments, a worker additionally installs each device to be additionally installed as described above to the existing wind turbine generator 1 .

(Detection signal acquisition step S30)
In the wind turbine blade diagnostic method according to some embodiments, the detection signal acquisition step S30 is a step of acquiring a detection signal from a sensor (for example, the optical fiber sensor 12 or the sensor 42) for detecting distortion of the wind turbine blade 3. is.
In the detection signal acquisition step S30 according to some embodiments, the light source/signal processing unit 10 or the strain gauge signal conditioner 11 acquires the detection signal from the sensor for detecting the strain of the wind turbine blade 3 . Then, in the detection signal acquisition step S30 according to some embodiments, the converted signals from the light source/signal processing unit 10 and the strain gauge signal conditioner 11 are processed by existing calculations provided in the existing wind turbine generator 1, such as the hub PLC 51. It outputs to the device 50 and the additionally installed arithmetic device 63 .

In addition, in the detection signal acquisition step S30), the sampling frequency for acquiring the detection signal may be changed based on the rotational speed of the wind turbine blades 3 .
If the sampling frequency for acquiring the detection signal is constant regardless of the rotation speed of the wind turbine blades 3, the higher the rotation speed of the wind turbine blades 3, the more the azimuth angle from acquisition of the detection signal to acquisition of the next detection signal. the amount of change increases. Therefore, as the rotation speed of the wind turbine blades 3 increases, the number of detection signals that can be obtained during one rotation of the wind turbine blades 3 decreases. Accuracy will decrease.
If the sampling frequency for acquiring the detection signal is changed based on the rotation speed of the wind turbine blade 3, more specifically, if the sampling frequency for acquiring the detection signal is increased as the rotation speed of the wind turbine blade 3 increases, the wind turbine blade Even if the rotation speed of 3 increases, it is possible to suppress the decrease in the accuracy of detecting the variation.

(Load calculation step S50)
In the wind turbine blade diagnostic method according to some embodiments, the load calculation step S50 is a step of calculating the load acting on the wind turbine blade based on the detection signal.
In the load calculation step S50 according to some embodiments, based on the detection signal from the sensor for detecting the strain of the wind turbine blade 3, more specifically, the light source/signal processing unit 10 and the strain gauge signal conditioning unit Based on the converted signal from the core 11, the hub PLC 51 and other existing computing devices 50 provided in the existing wind turbine generator 1 and the additional computing device 63 compute the bending moment and the like acting on the wind turbine blades 3 due to the wind load. do.
In addition, in the load calculation step S50 according to some embodiments, coordinate transformation is performed based on the calculated bending moment acting on the wind turbine blades 3 and the like, so that the bending moment acting on the wind turbine rotor 5 and the nacelle 4 Calculate the bending moment that acts.
The load calculation step S50 according to some embodiments may be performed by the existing calculation device 50 included in the existing wind turbine generator 1, such as the hub PLC 51, or may be performed by the additional calculation device 63. Details of the specific calculation processing in the load calculation step S50 will be described later.

The computation in the load computation step S50 according to some embodiments may be performed by the existing computation device 50 included in the existing wind turbine generator 1 or may be performed by the additionally installed computation device 63 .
When the calculation in the load calculation step S50 is performed by the existing calculation device 50 included in the existing wind turbine generator 1, the number of additional calculation devices (additional calculation device 63) can be suppressed, so that the existing wind power In providing the method for diagnosing the wind turbine blades 3 by measuring the distortion of the wind turbine blades 3 in the power generator 1 in real time, the additional installation cost of the arithmetic unit can be reduced.

In general, if the existing calculation device 50 included in the existing wind turbine generator 1 is not supposed to calculate the load acting on the wind turbine blade 3 based on the detection signal from the sensor 12, Computational capacity may not be sufficient to compute the load, compared to the computational capacity of a small personal computer or a so-called single board computer additionally installed as the additional computation device 63 .
If the additional calculation device 63 is added in the additional installation step S10 and the calculation in the load calculation step S50 is performed by the additional calculation device 63, the additional calculation device 63 has sufficient calculation capacity to calculate the load. An arithmetic unit 63 can be added to calculate the load.

(External transmission step S70)
In the wind turbine blade diagnostic method according to some embodiments, the external transmission step S70 is a step of transmitting the load information calculated in the load calculation step S50 to the outside of the wind turbine generator.
In the external transmission step S70 according to some embodiments, for example, as shown in FIG. device wirelessly. If the bending moment and the like are calculated in the existing calculation device 50 included in the existing wind turbine generator 1 in the load calculation step S50, the existing calculation device 50 transmits the communication device 67 in the external transmission step S70. You may make it transmit to the apparatus of the exterior of the wind power generator 1 by radio via.

If the load information calculated in the step of calculating the load (load calculation step S50) is stored in a storage device or the like arranged inside the wind turbine generator 1, an extreme example would be the wind turbine 1a. If an accident such as a collapse occurs and the storage device is destroyed, the information cannot be retrieved from the storage device, and the information cannot be used to investigate the cause of the accident.
By executing the external transmission step S70, if the above information is stored in a storage device or the like external to the wind turbine generator 1, even if the above accident occurs, the above information can be used to investigate the cause of the accident. can be used. That is, by performing the external transmission step S70, it is possible to reduce the possibility of loss of the information.

(Abnormality detection step S90)
In the wind turbine blade diagnostic method according to some embodiments, the abnormality detection step S90 is a step of detecting the presence or absence of an abnormality in the wind turbine blade 3 based on the detection signal.
In the abnormality detection step S90 according to some embodiments, more specifically, the light source/signal processing unit 10 and the strain gauge signal conditioning unit are operated based on the detection signal from the sensor for detecting the strain of the wind turbine blade 3. Based on the converted signal from the core 11, the hub PLC 51 and the additionally installed arithmetic unit 63 determine whether the wind turbine blade 3 is abnormal.
The abnormality detection step S90 according to some embodiments may be performed by the existing arithmetic device 50 included in the existing wind turbine generator 1, such as the hub PLC 51, or by the additionally installed arithmetic device 63. Details of specific processing for determining whether or not there is an abnormality in the wind turbine blades 3 in the abnormality detection step S90 will be described later.
In the wind turbine blade diagnostic method according to some embodiments, the presence or absence of an abnormality in the wind turbine blade 3 can be detected based on the distortion of the wind turbine blade 3 by including the abnormality detection step S<b>90 .

(Calculation of downwind bending moment per blade and torque component per blade)
Hereinafter, among specific contents of the calculation processing in the load calculation step S50 according to some embodiments, the calculation processing of the leeward direction bending moment per blade and the torque component per blade will be described.
FIG. 6 is a block diagram relating to calculation of the downwind bending moment per blade and the torque component per blade. The calculation of the leeward direction bending moment per blade and the torque component per blade is performed by the existing calculation device 50 provided in the existing wind turbine generator 1 such as the hub PLC 51 and the additional calculation device 63 . For convenience of explanation, in the following explanation, it is assumed that the addition calculation device 63 calculates the leeward direction bending moment per blade and the torque component per blade. The existing arithmetic device 50 included in the wind turbine generator 1 may be used.

6, for example, as shown in FIG. 2B, 1 It concerns the calculation of the downwind bending moment per blade and the torque component per blade. The two sensors may be, for example, the optical fiber sensors 12A and 12D, or the sensors 42 such as two strain gauges or semiconductor strain gauges.
For example, as shown in FIG. 2A, the calculation contents based on detection signals from four sensors attached to each blade root portion 3a of the wind turbine blade 3 will be described later. The four sensors may be, for example, the optical fiber sensors 12A to 12D, or the sensors 42 such as four strain gauges or semiconductor strain gauges.
In the following description, for the sake of convenience, the case where the sensor for detecting the strain of the wind turbine blade 3 is mainly the optical fiber sensor 12 will be described. The same applies to the sensor 42 such as a gauge.

FIG. 7 shows, in the block _diagram of FIG. FIG. 10 is a more detailed block diagram of the calculation of the bending moment M _EDGE about the axis of rotation;

First, description will be made based on FIG.
The additionally installed arithmetic unit 63 corrects the strain εflap of the sensor 12A detected by the sensor 12A using the temperature correction unit 101f, thereby removing the influence of the thermal expansion of the wind turbine blade 3 from the strain εflap.
The additional calculation device 63 removes the influence of the centrifugal force and the gravity acting on the wind turbine blade 3 by correcting the strain εflap excluding the influence of thermal elongation using the correction unit 102f.
The additionally installed arithmetic unit 63 corrects the strain εflap excluding the effects of centrifugal force and gravity by the correcting unit 103f, thereby converting the strain εflap into a bending moment M _FLAP Calculate

Note that the correction unit 103f calculates the bending moment M _FLAP by the following equation (1).
M _FLAP =k1×(εflap−b1) (1)
Here, k1 is a strain moment calibration coefficient, which is a coefficient for converting strain into bending moment.
b1 is the bending strain calibration factor ZERO adjustment, which is the strain when the bending moment is zero.

Next, the pitch angle correction unit 104f decomposes the bending moment M _FLAP into a leeward direction bending moment component and a torque component of the wind turbine rotor 5 based on the pitch angle.

Similarly, the additionally installed arithmetic unit 63 corrects the strain εedge of the sensor 12D detected by the sensor 12D by using the temperature correction unit 101e to remove the influence of the thermal expansion of the wind turbine blade 3 from the strain εedge.
The additional calculation device 63 removes the influence of the centrifugal force and the gravity acting on the wind turbine blade 3 by correcting the strain ε edge excluding the influence of thermal elongation with the correction unit 102e.
The additionally installed arithmetic unit 63 corrects the strain εedge excluding the effects of centrifugal force and gravity by the correcting unit 103e, so that the strain εedge is corrected by the code C acting on the wind turbine blade 3 and the axis perpendicular to the central axis of the wind turbine blade 3. Calculate the circumferential bending moment M _EDGE .

Note that the correction unit 103e calculates the bending moment M _EDGE by the following equation (2).
M _EDGE =k2×(εedge−b2) (2)
Here, k2 is a strain moment calibration coefficient, which is a coefficient for converting strain into bending moment.
b2 is the bending strain calibration factor ZERO adjustment, which is the strain when the bending moment is zero.

Next, the pitch angle correction unit 104e decomposes the bending moment M _EDGE into a leeward direction bending moment component and a torque component of the wind turbine rotor 5 based on the pitch angle.

The leeward bending moment per blade is obtained by adding the leeward bending moment component obtained by the pitch angle correction unit 104f and the leeward bending moment component obtained by the pitch angle correction unit 104e. .

The torque component of the wind turbine rotor 5 obtained by the pitch angle correction unit 104f and the torque component of the wind turbine rotor 5 obtained by the pitch angle correction unit 104e are added together, and the cone angle correction unit 105 performs correction based on the cone angle. By doing so, the torque component per blade can be obtained.

Next, based on FIG. 7, the bending moment M FLAP about the axis along the direction of the cord C acting on the wind turbine blade 3 and the bending moment M _FLAP about the axis orthogonal to the central axis (blade axis L0) of the cord C and the wind turbine blade 3 Calculation of the bending moment M _EDGE will be described.
The additional calculation device 63 subtracts the bending strain correction coefficient ZERO adjustment (b1) from the strain εflap of the sensor 12A detected by the sensor 12A using the subtractor 201f, and subtracts the strain corresponding to the thermal expansion of the wind turbine blade 3 using the subtractor 202f. Subtract.

The additional calculation device 63 subtracts the strain due to the centrifugal force with the subtractor 203f. Specifically, by utilizing the fact that the square of the rotation speed of the wind turbine rotor 5 and the centrifugal force acting on the wind turbine blades 3 are directly proportional, Obtain the relationship with the centrifugal force to be applied in advance.
More specifically, by utilizing the fact that the square of the rotation speed of the wind turbine rotor 5 and the strain generated in the wind turbine blades 3 due to the centrifugal force acting on the wind turbine blades 3 are directly proportional to each other, the rotation speed of the wind turbine rotor 5 can be As the relation between the square and the centrifugal force acting on the wind turbine blade 3, the tensile strain due to the centrifugal force per square of the rotation speed of 1 rpm of the wind turbine rotor 5 is obtained as a coefficient E in advance. Using this coefficient, the additional calculation device 63 calculates the tensile strain due to the centrifugal force in the multiplier 206 .
Then, the additionally installed arithmetic unit 63 subtracts the tensile strain calculated by the multiplier 206 using the subtractor 203f.

Note that the above coefficient E is obtained as follows.
The centrifugal force acting on the wind turbine blade 3 obtained from the detection signal from the sensor 12 at the azimuth angle at which the blade axis L0 of the wind turbine blade 3 is closest to the vertical direction during the period before the existing wind power generator 1 starts power generation. (More specifically, the relationship between the strain generated in the wind turbine blades 3 due to centrifugal force) and the square of the rotation speed of the wind turbine rotor 5 is obtained in advance. In other words, a plurality of points of strain data are acquired at different rotation speeds of the wind turbine rotor 5 . The square of the rotation speed of the wind turbine rotor 5 is plotted on the horizontal axis, and the strain generated in the wind turbine blade 3 due to the centrifugal force is plotted on the vertical axis. The gradient of the obtained straight line is assumed to be the coefficient E described above.
The operation of obtaining the coefficient E may be performed by an operator, or may be periodically performed by the additionally installed arithmetic device 63 .

The azimuth angle at which the blade axis L0 of the wind turbine blade 3 is closest to the vertical direction is the azimuth when the target wind turbine blade 3 is located at the 12 o'clock or 6 o'clock position when the wind turbine 1a is viewed from the front. It's about corners.

By calculating as described above, the influence of the centrifugal force acting on the wind turbine blades 3 can be eliminated when obtaining the bending moment acting on the wind turbine blades 3 due to the wind load.

The additionally installed arithmetic device 63 adds the strain due to the gravity acting on the wind turbine blade 3 with the azimuth angle, tilt angle, and cone angle taken into consideration by the adder 204f.
Then, the additional arithmetic unit 63 multiplies the strain moment calibration coefficient (k1) by the multiplier 205f to calculate the bending moment M _FLAP about the axis along the code C direction.

Similarly, the additionally installed arithmetic unit 63 subtracts the bending strain correction coefficient ZERO adjustment (b2) from the strain εedge of the sensor 12D detected by the sensor 12D using the subtractor 201e, and subtracts the strain corresponding to the thermal expansion of the wind turbine blade 3. subtracted by the unit 202e.

The additional calculation device 63 subtracts the strain due to the centrifugal force with the subtractor 203e. Specifically, as described above, the additional calculation device 63 subtracts the tensile strain calculated by the multiplier 206 using the subtractor 203e.

The adder 204e adds the strain caused by the gravity acting on the wind turbine blade 3 in consideration of the azimuth angle, tilt angle, and cone angle.
Then, the additionally installed arithmetic unit 63 calculates the bending moment M _EDGE around the axis perpendicular to the central axis of the cord C and the wind turbine blade 3 by multiplying the strain moment calibration coefficient (k2) by the multiplier 205e.

In this way, the additional calculation device 63 calculates the load acting on the wind turbine blade 3 based on the detection signal from the sensor 12A as the first sensor and the detection signal from the sensor 12D as the second sensor. can be done.

Here, the wind turbine blade 3 is generally made of fiber reinforced plastic such as GFRP (glass fiber reinforced plastic) or CFRP (carbon fiber reinforced plastic). Therefore, when installing the sensor 12 for detecting the distortion of the wind turbine blade at the manufacturing stage of the wind turbine blade 3, the work can be done on the ground, and it is relatively easy to embed the sensor 12 between the layers of fibers. Therefore, installation of the sensor 12 is relatively easy.
However, when the sensor 12 is added to the wind turbine blade 3 of the existing wind turbine generator 1, the wind turbine blade 3 remains attached to the wind turbine rotor 5, so the attitude of the wind turbine blade 3 is different from that in the manufacturing stage of the wind turbine blade 3. is difficult to be in a posture suitable for installation of the sensor 12, and the like. Therefore, the installation of the sensor 12, which involves the work of arranging the sensor 12 on the wind turbine blade 3 and then covering and fixing the sensor 12 with fiber-reinforced plastic, is a relatively difficult work. Therefore, when additionally installing the sensors 12 on the wind turbine blades 3 of the existing wind turbine generator 1, the number of the additionally installed sensors 12 is preferably as small as possible.

Therefore, as described above, the downwind bending moment per blade and the torque component per blade are calculated based on the detection signals from the two sensors attached to each blade root portion 3a of the wind turbine blades 3. Then, the number of sensors 12 to be installed in the step of additionally installing the sensors 12 (additional installation step S10) is only two per blade. Therefore, man-hours for additional installation work can be greatly reduced. As a result, in providing a method for diagnosing the wind turbine blades 3 by measuring the distortion of the wind turbine blades 3 in the existing wind turbine generator 1 in real time, the additional installation cost of the sensor 12 can be reduced.

When calculating the load acting on the wind turbine blade 3, it is preferable to calculate the load excluding the influence of the centrifugal force acting on the wind turbine blade 3 and the gravity acting on the wind turbine blade 3, as described above.
As a result, the bending moment acting on the wind turbine blades 3 due to the wind load, excluding the influence of the centrifugal force acting on the wind turbine blades 3 and the gravity acting on the wind turbine blades 3, can be obtained.

(Calculation contents based on detection signals from four optical fiber sensors 12A to 12D)
The bending moment M _FRAP acting on the wind turbine blade 3 about the axis along the direction of the cord C and the bending moment M _EDGE about the axis orthogonal to the central axis (blade axis L0) of the cord C and the wind turbine blade 3 are shown in FIG. A case of calculating based on detection signals from four optical fiber sensors 12 (12A to 12D) as shown in FIG.

Before and after the bending moment M _FRAP about the axis along the direction of the code C due to the wind load acts, the two sensors (the sensor 12D and the sensor 12B) arranged at two positions facing each other along the direction of the code C The strains of the wind turbine blades 3 obtained from the detection signals have the same amount of change in strain, but the increase and decrease in strain are opposite. Therefore, one-half of the difference between the two obtained strain change amounts is the strain change amount due to the bending moment _MFRAP . Moreover, when calculating the difference between the two strain change amounts obtained from the detection signals from the two sensors, the effects of centrifugal force and gravity acting on the wind turbine blade 3 are canceled out. Therefore, it becomes easy to calculate the bending moment _MFRAP . The same applies to the calculation of the chord C due to the wind load and the bending moment M _EDGE about the axis orthogonal to the central axis (blade axis L0) of the wind turbine blade 3 .

Description will be made below based on the block diagram of FIG.
The additional calculation device 63 corrects the strain εflapA of the sensor 12A detected by the sensor 12A using the temperature correction unit 101f, thereby removing the influence of the thermal expansion of the wind turbine blade 3 from the strain εflapA. Similarly, the additionally installed arithmetic unit 63 corrects the strain εflapC of the sensor 12C detected by the sensor 12C with the temperature correction unit 101f, thereby removing the influence of the thermal expansion of the wind turbine blade 3 from the strain εflapC.
Note that the additionally installed arithmetic unit 63 omits the correction in the correction unit 102f.

Then, the additional arithmetic unit 63 calculates the bending moment _MFRAP in the correction unit 103f from the strain εflapA and the strain εflapC from which the influence of thermal expansion is removed in the temperature correction unit 101f by the following equation (3).
M _FRAP =k3×(εflapA−εflapC)/2−b3) (3)
Here, k3 is a strain moment calibration coefficient, which is a coefficient for converting strain into bending moment.
b3 is the bending strain calibration factor ZERO adjustment, which is the strain when the bending moment is zero.

Similarly, the additionally installed arithmetic unit 63 corrects the strain εflapD of the sensor 12D detected by the sensor 12D using the temperature correction unit 101e, thereby removing the influence of the thermal expansion of the wind turbine blade 3 from the strain εflapD. Similarly, the additionally installed arithmetic device 63 corrects the strain εflapB of the sensor 12B detected by the sensor 12B by using the temperature correction unit 101e to remove the influence of the thermal expansion of the wind turbine blade 3 from the strain εflapB.
Note that the additionally installed arithmetic unit 63 omits the correction in the correction unit 102e.

Then, the additional calculation device 63 calculates the bending moment M _EDGE in the correction unit 103e from the strain εflapD and the strain εflapB from which the influence of thermal elongation is removed in the temperature correction unit 101e by the following equation (4).
M _EDGE =k4×(εflapD−εflapB)/2−b4) (4)
Here, k4 is a strain moment calibration coefficient, which is a coefficient for converting strain into bending moment.
b4 is the bending strain calibration factor ZERO adjustment, which is the strain when the bending moment is zero.

Although detailed description of the processing based on FIG. 7 is omitted, when performing calculations based on the detection signals from the four optical fiber sensors 12 (12A to 12D) as shown in FIG. 2A, the additional calculation device 63 omits the subtraction in the subtractors 203f, 204f and 203e.

(Calculation of Bending Moment Acting on Wind Turbine Rotor 5 and Nacelle 4 by Coordinate Transformation)
Calculation of the bending moment acting on the wind turbine rotor 5 and the nacelle 4 by performing the coordinate transformation will be described below.
FIG. 8 is a block diagram for calculating the combined torque component MXR, that is, the rotor torque of the wind turbine rotor 5. As shown in FIG.
FIG. 9 is a block diagram of calculation of the bending moment about the wind turbine rotor 5 and the bending moment about the nacelle 4 .
FIG. 10A is a diagram for explaining the bending moment about the wind turbine blade 3. FIG.
FIG. 10B is a diagram for explaining the bending moment about the wind turbine rotor 5. FIG.
FIG. 10C is a diagram for explaining the bending moment about the nacelle 4. FIG.
Each of the following calculations is performed by the existing calculation device 50 provided in the existing wind turbine generator 1, such as the hub PLC 51, or the additional calculation device 63. FIG. For convenience of explanation, in the following description, each calculation is performed by the additionally installed calculation device 63. may

(Calculation of synthetic torque component MXR)
In addition, in the load calculation step S50 according to some embodiments, as described below, the calculation processing of the combined torque component MXR, the calculation processing of the bending moment about the wind turbine rotor 5, and the bending moment of the nacelle 4 are performed. Arithmetic processing may be performed.
These calculation processes are performed by the existing calculation device 50 included in the existing wind turbine generator 1, such as the hub PLC 51, or the additional calculation device 63. FIG. For convenience of explanation, in the following description, it is assumed that the additionally installed arithmetic device 63 performs these arithmetic processes. may be implemented.

As shown in FIG. 8, the additional calculation device 63 calculates the torque components per blade (first blade torque component, second blade torque component, 3rd blade torque component) to calculate a composite torque component MXR.

(Bending moment about wind turbine rotor 5)
As shown in FIG. 9, the additional calculation device 63 calculates the leeward direction bending moment per blade for each of the three wind turbine blades 3 calculated as described above, based on the installation position of each wind turbine blade 3. By correcting and summing, the vertical resolved bending moment MYR and the horizontal resolved bending moment MZR of the wind turbine rotor 5 are calculated (see FIG. 10B).

(Bending moment about nacelle 4)
As shown in FIG. 9, the additionally installed calculation device 63 corrects the vertical resolved bending moment MYR and the horizontal resolved bending moment MZR of the wind turbine rotor 5 based on the azimuth angle, and adds them together. A resultant vertical bending moment Md for the nacelle 4 is calculated (see FIG. 10C).
Further, the additionally installed arithmetic device 63 corrects the vertical resolved bending moment MYR and the horizontal resolved bending moment MZR of the wind turbine rotor 5 based on the azimuth angle, adds them, and performs correction based on the tilt angle. , the resultant lateral bending moment Mq for the nacelle 4 is calculated (see FIG. 10C).

(Regarding calibration of sensor 12)
A method for diagnosing a wind turbine blade according to some embodiments may include a calibration step S200 for the sensor 12 described below.
FIG. 11 is a flow chart showing the processing procedure for the sensor 12 calibration step S200.
The calibration step S200 of the sensor 12 is a step of configuring the additionally installed sensor 12. As shown in FIG. a second acquisition step S230 of acquiring a detection signal from the sensor at the azimuth angle at which the blade axis L0 is closest to the vertical direction; and calibration of the sensor 12 based on the acquired detection signal. and a calibration step S250.
Note that the calibration step S200 of the sensor 12 according to this embodiment is performed by an operator.

The first obtaining step S210 is a step of fixing the wind turbine blade 3 at an azimuth angle at which the blade axis L0 of the wind turbine blade 3 is horizontal and obtaining a detection signal from the sensor 12 of the wind turbine blade 3 .
In the first obtaining step S210, the wind turbine blade 3 is fixed at an azimuth angle at which the blade axis L0 of the wind turbine blade 3 to which the sensor 12 to be calibrated is attached is horizontal, for example, at the 3 o'clock position when the wind turbine 1a is viewed from the front. do. Then, by changing the pitch angle of the wind turbine blade 3, the wavelength (detection signal) of the sensor 12 arranged along the direction of the code C is recorded at the pitch angle at which the wavelength of the sensor 12 is maximized or minimized. . Next, by changing the pitch angle of the wind turbine blades 3, the pitch angle at which the wavelength of the sensor 12 arranged along the direction orthogonal to the code C direction becomes maximum or minimum is determined as the wavelength of the sensor 12 (detection signal). record.

Next, the wind turbine blade 3 is moved to the 9 o'clock position when the wind turbine 1a is viewed from the front, and then fixed. The wavelength (detection signal) of sensor 12 is then recorded, as was done at the 3 o'clock position.

If the azimuth angle is taken on the horizontal axis and the wavelength is taken on the vertical axis, the wavelength curve will have a shape similar to that of a sine wave. Therefore, the zero point and the amplitude are tentatively determined from the wavelength data recorded as described above.

The second obtaining step S<b>230 is a step of fixing the wind turbine blade 3 at an azimuth angle at which the blade axis L<b>0 is closest to the vertical direction and obtaining a detection signal from the sensor 12 .
In the second acquisition step S230, the wind turbine blade 3 is fixed at the 12 o'clock position and the 6 o'clock position when the wind turbine 1a is viewed from the front, and the wavelength (detection signal) of the sensor 12 is recorded.
The azimuth angles at which the blade axis L0 approaches the vertical direction are specifically the 12 o'clock position and the 6 o'clock position when the wind turbine 1a is viewed from the front. For example, if both the cone angle and the tilt angle are 0 degrees, the blade axis L0 is oriented vertically at the 12 o'clock position and the 6 o'clock position when the wind turbine 1a is viewed from the front.

In addition, the wind turbine blade 3 is fixed at a plurality of positions with arbitrary azimuth angles, and the wavelength (detection signal) of the sensor 12 is recorded as was done at the 3 o'clock position. For example, the wavelength (detection signal) of the sensor 12 may be recorded while changing the azimuth angle by 30 degrees.
Note that the order of recording the wavelengths (detection signals) of the sensor 12 at each azimuth angle as described above is not limited to the order described above, and may be carried out in any order.
Also, the wavelengths (detection signals) of the sensors 12 for each of the three wind turbine blades 3 may be recorded in parallel. For example, when the first wind turbine blade 31 is at the 3 o'clock position and the second wind turbine blade 32 is at the 11 o'clock position, the wavelength (detection signal) of the sensor 12 of the first wind turbine blade 31 is first recorded, and then Instead of recording the wavelength (detection signal) of the sensor 12 at another position of the first wind turbine blade 31, the second wind turbine blade 32 is moved to the 12 o'clock position and the wavelength (detection signal) of the second wind turbine blade 32 sensor 12 is recorded. signal) may be recorded.

The calibration step S250 is a step of calibrating the sensor 12 based on the wavelength data obtained as described above. Specifically, in the calibration step S250, the wavelength data obtained as described above is fitted to a sine wave by, for example, the least-squares method. Then, assuming that the magnitude of the half amplitude of the sine wave obtained in this manner matches the wavelength due to the bending moment due to the weight of the wind turbine blade 3, the above-described distortion moment correction factor and bending distortion correction factor ZERO adjustment are obtained. .

If the additionally installed sensor is the sensor 42, "wavelength" in the above description is replaced with "strain".

The wind turbine blade diagnostic method according to some embodiments includes the calibration step S200 of the sensor 12 described above. The influence of drift of the sensor 12 can be reduced.

According to the wind turbine blade diagnosis method according to some embodiments, the wind turbine blade 3 is fixed at an azimuth angle at which the blade axis L0 of the wind turbine blade 3 is horizontal, and the detection signal from the sensor 12 is obtained. A detection signal can be obtained from the bending moment acting on the wind turbine blade 3 due to the wind turbine blade 3's own weight.
By fixing the wind turbine blade 3 at the azimuth angle at which the blade axis L0 is closest to the vertical direction and acquiring the detection signal from the sensor 12, the effect of the bending moment acting on the wind turbine blade 3 due to the weight of the wind turbine blade 3 It is possible to obtain a detection signal that eliminates as much as possible.
In each process, the wind turbine blades 3 are fixed so that the wind turbine rotor 5 does not rotate, and the detection signal from the sensor 12 is acquired. can be obtained.
Thereby, the calibration accuracy of the sensor 12 can be improved.

(Detection of abnormalities in wind turbine blades)
Hereinafter, the specific content of the arithmetic processing in the abnormality detection step S90 according to some embodiments will be described.
Arithmetic processing to be described below is performed by the existing arithmetic device 50 included in the existing wind turbine generator 1, such as the hub PLC 51, or the additionally installed arithmetic device 63. FIG. For convenience of explanation, in the following description, it is assumed that the additional calculation device 63 performs the calculation processing described below. It may be implemented in device 50 .

Based on the detection signal from the additionally installed sensor 12 as described above, it is possible to detect whether or not there is an abnormality in the wind turbine blades 3 such as damage to the wind turbine blades 3 .
For example, if delamination occurs between the layers of the fiber-reinforced plastic that constitutes the wind turbine blade 3, the tendency of the strain fluctuation that appears during one rotation of the wind turbine blade 3 will change, or the strain fluctuation range and the average strain fluctuation will change. Changes appear in values, etc.
By detecting such a change, it is possible to detect the presence or absence of an abnormality in the wind turbine blades 3 .

FIG. 12 is an example of a schematic cross-sectional view of the wind turbine blade 3 according to some embodiments in a cross section perpendicular to the blade axis L0. FIG. 12 shows a cross-section at a position spaced radially outward of the wind turbine rotor 5 from the base end of the wind turbine blade 3, and the cross-sectional shape of the wind turbine blade 3 is a slightly flattened cylindrical shape.
In the wind turbine blade 3 according to some embodiments, as shown in FIG. 12, a reinforcing member 301 for reinforcing the wind turbine blade 3 is provided inside the wind turbine blade 3 having a cylindrical shape. In the example shown in FIG. 12, the reinforcing member 301 is positioned on the inner peripheral surface 3b of the wind turbine blade 3 so as to divide the inside of the wind turbine blade 3 into a plurality of regions when viewed along the blade axis L0. It extends from the area on one side (for example, the lower area in the drawing) to the area on the other side (for example, the upper area in the drawing) across L0.
In the example shown in FIG. 12, both ends 301a of the reinforcing member 301 are connected to the inner peripheral surface 3b of the wind turbine blade 3 by bonding or the like.

(Detection of damage by comparison between wind turbine blades 3)
For example, in one wind turbine blade 3 among a plurality of wind turbine blades 3, if both end portions 301a of the reinforcing member 301 are separated from the inner peripheral surface 3b of the wind turbine blade 3, other wind turbine blades 3 where such separation does not occur Compared to , there is a difference in the magnitude of strain when a load is applied.
Therefore, damage to the wind turbine blades 3 can be detected by comparing the strains measured by the optical fiber sensors 12 and the sensors 42 between the wind turbine blades 3 as described above.

That is, the additionally installed arithmetic unit 63, for example, the wind turbine blade Compare the strain between the three. Then, if the compared strain difference is equal to or greater than a predetermined threshold value, the additionally installed arithmetic unit 63 may determine that the wind turbine blade 3 has an abnormality.
Here, for example, the sensors 12 and 42 additionally provided in the additional installation step S10 include first wind turbine blade sensors (sensors 12 and 42 ) and a second wind turbine blade sensor (sensors 12, 42) for detecting distortion of a second wind turbine blade 32 different from the first wind turbine blade 31 among the plurality of wind turbine blades.
Further, in the detection signal acquisition step S30, the light source/signal processing unit 10 or the strain gauge signal conditioner 11 receives the first wind turbine blade detection signal from the first wind turbine blade sensor (sensors 12, 42) and the second wind turbine blade and a second wind turbine blade detection signal from the sensors (sensors 12 and 42).
In the abnormality detection step S90, the additionally installed arithmetic device 63 detects the first wind turbine blade detection signal and the second wind turbine blade detection signal based on the first wind turbine blade detection signal and the second wind turbine blade detection signal. Based on the converted signal output from the light source/signal processing unit 10 or the strain gauge signal conditioner 11 derived from , the presence or absence of damage, that is, abnormality of the first wind turbine blade 31 may be detected.

In the abnormality detection step S90, the additional calculation device 63 calculates the strain of the first wind turbine blade 31 and the second wind turbine blade 32 as described above based on the converted signal, and then detects the abnormality of the first wind turbine blade 31. The presence/absence may be detected, and the presence/absence of an abnormality in the first wind turbine blade 31 may be detected based on the converted signal without calculating the distortion of the first wind turbine blade 31 and the second wind turbine blade 32 .

This makes it easier to detect whether there is an abnormality in the first wind turbine blade 31 by referring to the distortion of the second wind turbine blade 32 that is different from the first wind turbine blade 31 that is the target of detecting whether there is an abnormality.

(Detection of damage not based on comparison between wind turbine blades 3)
As described above, before and after a bending moment along the direction of code C acts on the wind turbine blade 3 due to the wind load, the distortion of the wind turbine blade obtained from the detection signals from the leading edge side sensor and the trailing edge side sensor is , the amount of change in strain is the same, but the increase and decrease in strain are opposite.
Therefore, if the above-described relationship no longer holds between the detection signal from the leading edge sensor and the detection signal from the trailing edge sensor, it is conceivable that the wind turbine blade 3 has some kind of malfunction, unless the sensor 12 malfunctions. be done.
Similarly, if the above relationship between the detection signal from the ventral sensor and the detection signal from the dorsal sensor no longer holds, it means that if the sensor 12 is not malfunctioning, the wind turbine blade 3 is malfunctioning. Conceivable.

Therefore, for example, as shown in FIGS. 1 and 2A, a leading edge sensor (optical fiber sensor 12D), a trailing edge sensor (optical fiber sensor 12B), a ventral sensor (optical fiber sensor 12A) and a dorsal sensor (optical fiber If the sensor 12C) is included, the presence or absence of an abnormality in the wind turbine blades 3 may be detected in the following manner.
In the detection signal acquisition step S30, the light source/signal processing unit 10 uses a leading edge sensor (optical fiber sensor 12D), a trailing edge sensor (optical fiber sensor 12B), a ventral sensor (optical fiber sensor 12A), and a dorsal sensor (optical fiber sensor 12A). and a second wind turbine blade detection signal from the fiber sensor 12C). Note that four sensors 42 may be used instead of these four sensors 12 .

In the abnormality detection step S90, the additionally installed arithmetic device 63 may detect whether or not there is an abnormality in the wind turbine blade 3 based on at least one of the following comparison results (a) and (b).
(a) Comparison result of the detection signal from the leading edge side sensor (optical fiber sensor 12D) and the detection signal from the trailing edge side sensor (optical fiber sensor 12B) (more specifically, the comparison result with the converted signal derived from these detection signals) Comparison result).
(b) Comparison result of the detection signal from the ventral sensor (optical fiber sensor 12A) and the detection signal from the dorsal sensor (optical fiber sensor 12C) (more specifically, the conversion signal derived from these detection signals) Comparison result).

In the abnormality detection step S90, the additionally installed arithmetic unit 63 may detect whether there is an abnormality in the wind turbine blades 3 without calculating the strain of the wind turbine blades 3 based on the converted signal. Then, after the strain of the wind turbine blades 3 is calculated as described above, the presence or absence of an abnormality in the wind turbine blades 3 may be detected.

As a result, a detection signal from the leading edge sensor (optical fiber sensor 12D) and a detection signal from the trailing edge sensor (optical fiber sensor 12B), or a detection signal from the ventral sensor (optical fiber sensor 12A) and the dorsal sensor The presence or absence of an abnormality in the wind turbine blade 3 can be detected based on at least one of the detection signal from (the optical fiber sensor 12C).

Further, as another embodiment, the additionally installed arithmetic device 63 receives the detection signal from the sensor (for example, the optical fiber sensor 12 or the sensor 42) for detecting the distortion of the wind turbine blade 3, which is acquired in the detection signal acquisition process. Based on this, changes in strain over time are stored in a storage device (not shown) over a relatively long period of time, such as several months or several years. Then, if the amount of change in strain over time as described above is equal to or greater than a predetermined threshold value, the additionally installed arithmetic unit 63 may determine that the wind turbine blade 3 has an abnormality.

When it is determined that an abnormality has occurred in the wind turbine blades 3 as described above, for example, the additionally installed arithmetic device 63 notifies a device external to the wind power generator 1 via the communication device 67 that the wind turbine blades 3 have an abnormality. A signal indicating the presence may be wirelessly transmitted.

(estimation of azimuth angle)
In some embodiments described above, information on the azimuth angle was acquired from the existing wind turbine generator 1 . However, a sensor for detecting the azimuth angle may be additionally provided, and information on the azimuth angle may be acquired from the additionally provided sensor.

For example, the above-described addition step S10 may include a step of additionally installing an azimuth angle estimation sensor for estimating the azimuth angle of the wind turbine blade 3 . In this process, the azimuth angle estimation sensor 44 (see FIG. 1) is attached to any member that rotates together with the wind turbine rotor 5, such as the hub 2 or the like.
The azimuth angle estimation sensor 44 preferably includes one acceleration sensor capable of detecting three-axis accelerations of the X-axis, Y-axis, and Z-axis. The acceleration sensor may be used to detect the direction in which gravity acts, and the additionally installed arithmetic unit 63 may estimate the azimuth angle from the detection result of the acceleration sensor.
If the azimuth angle estimating sensor 44 also includes one angular velocity sensor capable of detecting angular velocities around the three axes of the X, Y, and Z axes, for example, the acceleration sensor may If the acting direction of gravity cannot be detected from the detection result of , the azimuth angle may be estimated as follows. That is, the direction of gravity before it becomes undetectable due to vibration or the like, the angular velocity of the wind turbine rotor 5 detected by the angular velocity sensor, and the elapsed time after the direction of gravity becomes undetectable due to vibration or the like. , the additionally installed arithmetic device 63 may estimate the azimuth angle.
As a result, the azimuth angle information can be obtained by the azimuth angle estimation sensor 44 even if the azimuth angle information cannot be obtained from the existing wind turbine generator 1 .

(Estimation of pitch angle)
In some embodiments described above, the pitch angle information was acquired from the existing wind turbine generator 1 . However, a sensor for detecting the pitch angle may be additionally provided, and pitch angle information may be acquired from the additionally provided sensor.

For example, the above-described addition step S10 may include a step of additionally installing a pitch angle estimation sensor for estimating the pitch angle of the wind turbine blades. In this step, the pitch angle estimation sensor 46 (see FIG. 1) may be additionally installed on each of the plurality of wind turbine blades 3, for example. If the pitch angle difference between the plurality of wind turbine blades 3 does not pose a problem, the pitch angle estimation sensor 46 may be added to any one of the plurality of wind turbine blades 3 in this step. .
The pitch angle estimation sensor 46 preferably includes one acceleration sensor capable of detecting three-axis accelerations of the X-axis, Y-axis, and Z-axis.
When the wind turbine blade 3 is at the 3 o'clock and 6 o'clock positions, the direction in which gravity acts and the blade axis L0 of the wind turbine blade 3 are perpendicular to each other. Therefore, the direction in which the gravity acts at the 3 o'clock and 6 o'clock positions is detected by the acceleration sensor, and the detection result of the acceleration sensor and the pitch angle and gravity measured in advance at the 3 o'clock and 6 o'clock positions are detected. The pitch angle may be estimated by the additionally installed arithmetic device 63 from the relationship with the acting direction.
As a result, the pitch angle information can be obtained by the pitch angle estimation sensor 46 even if the pitch angle information cannot be obtained from the existing wind turbine generator 1 .

The present disclosure is not limited to the above-described embodiments, and includes modifications of the above-described embodiments and modes in which these modes are combined as appropriate.

The contents described in each of the above embodiments are understood as follows, for example.
(1) A method for diagnosing a wind turbine blade according to at least one embodiment of the present disclosure is a method for diagnosing a wind turbine blade 3 of an existing wind turbine generator 1, and includes sensors 12 and 42 for detecting distortion of the wind turbine blade 3. (addition step S10), obtaining detection signals from the sensors 12 and 42 (detection signal obtaining step S30), and calculating the load acting on the wind turbine blade 3 based on the detection signals. (Load calculation step S50). In the step of additionally installing the sensors 12 and 42 (additional installation step S10), among the circumferential positions in the blade root portion 3a around the blade axis L0 of the wind turbine blade 3, the sensors 12 and 42 face each other along the direction of the cord C of the wind turbine blade 3. A first sensor (sensors 12, 42) is placed at one of two positions, and a second sensor (sensors 12, 42) is placed at one of two positions facing each other along a direction orthogonal to the code C direction. to be installed. In the load calculation step (load calculation step S50), the load is calculated based on the detection signals from the first sensors (sensors 12 and 42) and the detection signals from the second sensors (sensors 12 and 42).

For example, sensors 12 for detecting the strain of the wind turbine blades 3 are additionally provided at two positions facing each other along the direction of the code C and two positions facing each other along the direction perpendicular to the direction of the code C. Think about the case.
Before and after the bending moment M _FRAP about the axis along the direction of the code C due to the wind load acts, the two sensors (the sensor 12D and the sensor 12B) arranged at two positions facing each other along the direction of the code C The strains of the wind turbine blades 3 obtained from the detection signals have the same amount of change in strain, but the increase and decrease in strain are opposite. Therefore, one-half of the difference between the two obtained strain change amounts is the strain change amount due to the bending moment _MFRAP . Moreover, when calculating the difference between the two strain change amounts obtained from the detection signals from the two sensors, the effects of centrifugal force and gravity acting on the wind turbine blade 3 are canceled out. Therefore, it becomes easy to calculate the bending moment _MFRAP . The same applies to the calculation of the chord C due to the wind load and the bending moment M _EDGE about the axis orthogonal to the central axis (blade axis L0) of the wind turbine blade 3 .

Here, the wind turbine blade 3 is generally made of fiber reinforced plastic such as GFRP (glass fiber reinforced plastic) or CFRP (carbon fiber reinforced plastic). Therefore, when installing the sensor 12 for detecting the distortion of the wind turbine blade at the manufacturing stage of the wind turbine blade 3, the work can be done on the ground, and it is relatively easy to embed the sensor 12 between the layers of fibers. Therefore, installation of the sensor 12 is relatively easy.
However, when the sensor 12 is added to the wind turbine blade 3 of the existing wind turbine generator 1, the wind turbine blade 3 remains attached to the wind turbine rotor 5, so the attitude of the wind turbine blade 3 is different from that in the manufacturing stage of the wind turbine blade 3. is difficult to be in a posture suitable for installation of the sensor 12, and the like. Therefore, the installation of the sensor 12, which involves the work of arranging the sensor 12 on the wind turbine blade 3 and then covering and fixing the sensor 12 with fiber-reinforced plastic, is a relatively difficult work. Therefore, when additionally installing the sensors 12 on the wind turbine blades 3 of the existing wind turbine generator 1, the number of the additionally installed sensors 12 is preferably as small as possible.

According to the above method (1), the number of sensors 12 and 42 to be installed in the step of additionally installing the sensors 12 and 42 (additional installation step S10) is only two, which greatly reduces the number of man-hours for the installation work. can. As a result, in providing a method for diagnosing the wind turbine blades 3 by measuring the distortion of the wind turbine blades 3 in the existing wind power generator 1 in real time, the additional installation cost of the sensors 12 and 42 can be reduced.

(2) In some embodiments, in the method (1) above, in the step of calculating the load (load calculating step S50), the centrifugal force acting on the wind turbine blade 3 and the gravity acting on the wind turbine blade 3 It is preferable to calculate the load excluding the influence.

As described above, in the method (1), in the step of additionally installing the sensors 12 and 42 (additional installation step S10), one of the two positions facing each other along the direction of the code C and the code C Two sensors 12, 42 are additionally arranged in one of two positions opposite each other along the direction perpendicular to the direction. Therefore, in order to obtain the bending moment acting on the wind turbine blades 3 due to the wind load, it is necessary to eliminate the influence of the centrifugal force acting on the wind turbine blades 3 and the gravity acting on the wind turbine blades.
According to the method (2) above, since the load is calculated by eliminating the influence of the centrifugal force acting on the wind turbine blade 3 and the gravity acting on the wind turbine blade 3, the wind load acts on the wind turbine blade 3 Bending moment can be determined.

(3) In some embodiments, in the method of (2) above, in the step of calculating the load (load calculating step S50), the wind turbine blades 3 The centrifugal force acting on the wind turbine blade 3 obtained from the detection signals from the sensors 12 and 42 obtained at the azimuth angle at which the blade axis L0 of the blade is closest to the vertical direction, and the rotational speed of the wind turbine rotor 5 provided with the wind turbine blade 3 A load excluding the influence of the centrifugal force acting on the wind turbine blades 3 may be calculated based on the relationship obtained in advance with respect to the square.

According to the method (3) above, the influence of the centrifugal force acting on the wind turbine blades 3 can be eliminated when obtaining the bending moment acting on the wind turbine blades 3 due to the wind load.

(4) A method for diagnosing a wind turbine blade according to at least one embodiment of the present disclosure is a method for diagnosing a wind turbine blade 3 of an existing wind turbine generator 1, and includes sensors 12 and 42 for detecting distortion of the wind turbine blade 3. (addition step S10), obtaining detection signals from the sensors 12 and 42 (detection signal obtaining step S30), and calculating the load acting on the wind turbine blade 3 based on the detection signals. (load calculation step S50), and a step of detecting the presence or absence of an abnormality in the wind turbine blade 3 based on the detection signal (abnormality detection step S90).

According to the method (4) above, it is possible to detect the presence or absence of an abnormality in the wind turbine blade 3 based on the distortion of the wind turbine blade 3 .

(5) In some embodiments, in the method of (4) above, the sensors 12 and 42 additionally installed in the step of additionally installing the sensors 12 and 42 (addition step S10) detect the presence or absence of an abnormality. Therefore, an abnormality detection sensor that detects the distortion of the wind turbine blades 3 may be included. In the step of detecting the presence or absence of an abnormality (abnormality detection step S90), it is preferable to detect the presence or absence of an abnormality based on the detection signal from the abnormality detection sensor.

According to the method (5) above, when the detection signal from the abnormality detection sensor is not necessary when calculating the load acting on the wind turbine blade 3, the processing of the detection signal can be simplified. It is possible to reduce the load on the arithmetic unit for processing the detection signal from.

(6) In some embodiments, in the method (4) or (5) above, the existing wind turbine generator 1 has a wind turbine rotor 5 having a plurality of wind turbine blades 3 . A first sensor (first Wind turbine blade sensors (sensors 12 and 42)) and a second sensor (second wind turbine blade sensor (sensor 12, 42)) should be included. In the step of acquiring the detection signal (detection signal acquisition step S30), the first detection signal (first wind turbine blade detection signal) from the first sensor (first wind turbine blade sensor (sensors 12, 42)) and the second sensor It is preferable to acquire a second detection signal (second wind turbine blade detection signal) from (second wind turbine blade sensors (sensors 12 and 42)). In the step of detecting the presence or absence of an abnormality (abnormality detection step S90), the first wind turbine blade 31 is detected based on the first detection signal (first wind turbine blade detection signal) and the second detection signal (second wind turbine blade detection signal). It is preferable to detect the presence or absence of an abnormality.

According to the method (6) above, the presence or absence of an abnormality in the first wind turbine blade 31 is detected by referring to the distortion of the second wind turbine blade 32 that is different from the first wind turbine blade 31 that is the target for detecting the presence or absence of an abnormality. becomes easier.

(7) In some embodiments, in the method (4) or (5) above, the sensors 12 and 42 additionally installed in the step of additionally installing the sensors 12 and 42 (additional installation step S10) include wind turbine blades The leading edge side 23 and the trailing edge side 24 of the wind turbine blade 3, which are arranged at two positions facing each other along the direction of the chord C, of the circumferential positions in the blade root portion 3a centered on the blade axis L0 of No. 3. A sensor (optical fiber sensor 12D), a trailing edge sensor (optical fiber sensor 12B), and ventral sides 21 and 22 that face each other along the direction perpendicular to the direction of the cord C. A side sensor (optical fiber sensor 12A) and a dorsal sensor (optical fiber sensor 12C) may be included. In the step of acquiring the detection signal, the detection signal from the leading edge sensor (optical fiber sensor 12D), the detection signal from the trailing edge sensor (optical fiber sensor 12B), and the detection signal from the ventral sensor (optical fiber sensor 12A). A signal and a detection signal from the dorsal sensor (optical fiber sensor 12C) may be acquired. In the step of detecting the presence or absence of an abnormality (abnormality detection step S90), the result of comparison between the detection signal from the leading edge side sensor (optical fiber sensor 12D) and the detection signal from the trailing edge side sensor (optical fiber sensor 12B), or the antinode The presence or absence of an abnormality in the wind turbine blade 3 is detected based on at least one of the comparison results of the detection signal from the side sensor (optical fiber sensor 12A) and the detection signal from the back sensor (optical fiber sensor 12C). do it.

As described above, before and after a bending moment along the direction of code C acts on the wind turbine blade 3 due to the wind load, the distortion of the wind turbine blade obtained from the detection signals from the leading edge side sensor and the trailing edge side sensor is , the amount of change in strain is the same, but the increase and decrease in strain are opposite.
Therefore, if the above-described relationship no longer holds between the detection signal from the leading edge sensor and the detection signal from the trailing edge sensor, it is conceivable that the wind turbine blade 3 has some kind of malfunction, unless the sensor 12 malfunctions. be done.
Similarly, if the above relationship between the detection signal from the ventral sensor and the detection signal from the dorsal sensor no longer holds, it means that if the sensor 12 is not malfunctioning, the wind turbine blade 3 is malfunctioning. Conceivable.
According to the method (7) above, the detection signal from the leading edge sensor (optical fiber sensor 12D) and the detection signal from the trailing edge sensor (optical fiber sensor 12B), or from the ventral sensor (optical fiber sensor 12A) and the detection signal from the dorsal sensor (optical fiber sensor 12C).

(8) In some embodiments, in any one of the above methods (1) to (7), information on the load calculated in the step of calculating the load (load calculation step S50) is (external transmission step S70).

If the load information calculated in the step of calculating the load (load calculation step S50) is stored in a storage device or the like arranged inside the wind turbine generator 1, an extreme example would be the wind turbine 1a. If an accident such as a collapse occurs and the storage device is destroyed, the information cannot be retrieved from the storage device, and the information cannot be used to investigate the cause of the accident.
According to the method (8) above, if the above information is stored in a storage device or the like external to the wind turbine generator 1, even if the above accident occurs, the above information can be used to investigate the cause of the accident. can be used. That is, according to the method (8) above, the possibility of loss of the information can be reduced.

(9) In some embodiments, in any one of the methods (1) to (8) above, in the step of calculating the load (load calculating step S50), the existing wind turbine generator 1 The load may be calculated by an existing calculation device 50 provided in .

According to the above method (9), since the number of additional arithmetic units 63 can be reduced, a method for diagnosing the wind turbine blades 3 by measuring the strain of the wind turbine blades 3 in the existing wind power generator 1 in real time is provided. In doing so, it is possible to reduce the additional installation cost of the arithmetic unit.

(10) In some embodiments, in any one of the above methods (1) to (8), the wind turbine generator 1 is provided with a load acting on the wind turbine blade 3 based on the detection signal. A step of additionally installing an arithmetic device (additional installation step S10) may be provided. In the step of calculating the load (load calculating step S50), the load may be calculated by the calculating device 63 additionally provided in the step of additionally installing the calculating device (additional step S10) based on the detection signal.

In general, if the existing arithmetic device 50 provided in the existing wind turbine generator 1 is not supposed to calculate the load acting on the wind turbine blade 3 based on the detection signals from the sensors 12 and 42, the calculation Capacity may not be sufficient to compute the load.
According to the method (10) above, the computing device 63 having sufficient computing power to compute the load can be added.

(11) In some embodiments, in the method of (10) above, power is supplied to the arithmetic device 63 additionally installed in the step of additionally installing the arithmetic device in the wind turbine generator 1 (addition step S10). It is preferable to include a step of additionally installing a possible power storage device 65 (additional installation step S10).

According to the method (11) above, even if the power supply from the wind turbine generator 1 side is cut off due to some kind of problem, the power from the power storage device 65 can be used to operate the computing device.

(12) In some embodiments, the method according to any one of (1) to (11) above may include a step S200 of calibrating the sensor.

According to the method (12) above, even if a phenomenon in which the output values of the sensors 12 and 42 deviate over time, that is, a so-called drift occurs, the influence of the drift of the sensors 12 and 42 can be reduced.

(13) In some embodiments, in the method of (12) above, in the step S200 of calibrating the sensor, the wind turbine blade 3 is fixed at least at an azimuth angle at which the blade axis L0 of the wind turbine blade 3 is horizontal. , a step S210 of acquiring detection signals from the sensors 12 and 42, and a step S230 of acquiring the detection signals from the sensors 12 and 42 by fixing the wind turbine blade 3 at an azimuth angle that makes the blade axis L0 closest to the vertical direction. It is preferable to calibrate the sensors 12 and 42 based on the detection signals from the sensors 12 and 42 obtained by performing .

According to the above method (13), the wind turbine blade 3 is fixed at an azimuth angle at which the blade axis L0 of the wind turbine blade 3 is horizontal, and the detection signals from the sensors 12 and 42 are obtained. A detection signal can be obtained from the bending moment acting on the wind turbine blade 3 due to its own weight.
By fixing the wind turbine blade 3 at an azimuth angle at which the blade axis L0 is closest to the vertical direction and obtaining detection signals from the sensors 12 and 42, the bending moment acting on the wind turbine blade 3 due to the weight of the wind turbine blade 3 is obtained. It is possible to obtain a detection signal that eliminates the influence of as much as possible.
In each process, the wind turbine blades 3 are fixed so that the wind turbine rotor 5 does not rotate, and the detection signals are acquired from the sensors 12 and 42. Therefore, the influence of the acceleration/deceleration of the rotation of the wind turbine blades 3 is eliminated. signal can be obtained.
Thereby, the calibration accuracy of the sensors 12 and 42 can be improved.

(14) In some embodiments, in any one of the methods (1) to (13) above, the step of additionally installing an azimuth angle estimation sensor for estimating the azimuth angle of the wind turbine blade 3; a step of additionally installing a pitch angle estimation sensor for estimating the pitch angle; a step of estimating an azimuth angle based on a detection signal from the azimuth angle estimation sensor; and a step of detecting a detection signal from the pitch angle estimation sensor. and estimating the pitch angle based on.

According to the above method (14), even if information on the azimuth angle and the pitch angle cannot be obtained from the existing wind turbine generator 1, the azimuth angle and the pitch angle can be estimated by the azimuth angle estimation sensor and the pitch angle estimation sensor. information is obtained.

(15) In some embodiments, in any one of the methods (1) to (14) above, in the step of acquiring the detection signal (detection signal acquisition step S30), detection is performed based on the rotation speed of the wind turbine blade 3. You may change the sampling frequency which acquires a signal.

If the sampling frequency for acquiring the detection signal is constant regardless of the rotation speed of the wind turbine blades 3, the higher the rotation speed of the wind turbine blades 3, the more the azimuth angle from acquisition of the detection signal to acquisition of the next detection signal. the amount of change increases. Therefore, as the rotation speed of the wind turbine blades 3 increases, the number of detection signals that can be obtained during one rotation of the wind turbine blades 3 decreases. Accuracy will decrease.
According to the above method (15), if the sampling frequency for acquiring the detection signal is increased as the rotation speed of the wind turbine blade 3 increases, the accuracy of detecting the above variation even if the rotation speed of the wind turbine blade 3 increases. can suppress the decrease in

1 Wind turbine generator 1a Wind turbine 2 Hub 3 Wind turbine blade 4 Nacelle 5 Wind turbine rotor 10 Light source/signal processing unit 11 Strain gauge signal conditioner 12 Optical fiber sensor (sensor)
42 sensor 50 existing computing device 51 hub PLC
53 Nacelle PLC
55 Windmill PLC
57 Hub power supply 63 Arithmetic device (additional arithmetic device)
65 power storage device 67 communication device

Claims (23)

A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
with
The sensor additionally installed in the step of additionally installing the sensor includes an abnormality detection sensor that detects the distortion of the wind turbine blade to detect the presence or absence of the abnormality,
In the step of detecting the presence or absence of the abnormality, the wind turbine blade diagnosis method detects the presence or absence of the abnormality based on a detection signal from the abnormality detection sensor. In the step of detecting the presence/absence of the abnormality, it is determined that the abnormality exists if the amount of change over time in the distortion of the wind turbine blade based on the detection signal from the abnormality detection sensor is equal to or greater than a predetermined threshold value. The method for diagnosing a wind turbine blade according to claim 1. In the step of calculating the load, the detection signal from the sensor obtained at an azimuth angle at which the blade axis of the wind turbine blade is closest to the vertical direction during a period before the existing wind power generator starts power generation. The effect of the centrifugal force acting on the wind turbine blade is eliminated based on the previously obtained relationship between the centrifugal force acting on the wind turbine blade obtained from and the square of the rotational speed of the wind turbine rotor including the wind turbine blade The method for diagnosing a wind turbine blade according to claim 1 or 2, wherein the load is calculated. The existing wind turbine generator has a wind turbine rotor including a plurality of the wind turbine blades,
The sensors to be additionally installed in the step of additionally installing the sensors include a first sensor for detecting distortion of a first wind turbine blade among the plurality of wind turbine blades; a second sensor for detecting distortion of a second wind turbine blade different from the first wind turbine blade;
In the step of acquiring the detection signal, a first detection signal from the first sensor and a second detection signal from the second sensor are acquired;
4. The step of detecting whether or not there is an abnormality detects whether or not there is an abnormality in the first wind turbine blade based on the first detection signal and the second detection signal. A method for diagnosing a wind turbine blade. A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
with
The existing wind turbine generator has a wind turbine rotor including a plurality of the wind turbine blades,
The sensors to be additionally installed in the step of additionally installing the sensors include a first sensor for detecting distortion of a first wind turbine blade among the plurality of wind turbine blades; a second sensor for detecting distortion of a second wind turbine blade different from the first wind turbine blade;
In the step of acquiring the detection signal, a first detection signal from the first sensor and a second detection signal from the second sensor are acquired;
In the step of detecting the presence or absence of an abnormality, the wind turbine blade diagnosis method detects whether or not the first wind turbine blade has an abnormality based on the first detection signal and the second detection signal. In the step of detecting the presence or absence of the abnormality, the strain of the first wind turbine blade is based on the strain of the first wind turbine blade based on the first detection signal and the strain of the second wind turbine blade based on the second detection signal. 6. The method for diagnosing a wind turbine blade according to claim 4 or 5, wherein the presence or absence of an abnormality is detected. In the step of detecting the presence or absence of the abnormality, the distortion of the second wind turbine blade is calculated based on the second detection signal without calculating the distortion of the first wind turbine blade based on the first detection signal. 6. The method for diagnosing a wind turbine blade according to claim 4 or 5, wherein the presence or absence of an abnormality in the first wind turbine blade is detected based on the first detection signal and the second detection signal. The sensors to be additionally installed in the step of additionally installing the sensors are provided on the front edge side and the rear edge side facing each other along the cord direction of the wind turbine blade, among the circumferential positions in the blade root centered on the blade axis of the wind turbine blade. A leading edge side sensor and a trailing edge side sensor respectively arranged at two positions on the edge side, and an ventral side and a dorsal side respectively arranged at two positions opposed along the direction perpendicular to the cord direction. a sensor and a dorsal sensor;
In the step of acquiring the detection signal, a detection signal from the leading edge sensor, a detection signal from the trailing edge sensor, a detection signal from the ventral sensor, and a detection signal from the dorsal sensor are acquired. ,
In the step of detecting the presence or absence of the abnormality, the result of comparison between the detection signal from the leading edge side sensor and the detection signal from the trailing edge side sensor, or the detection signal from the ventral side sensor and the detection signal from the dorsal side sensor. 4. The method for diagnosing a wind turbine blade according to any one of claims 1 to 3, wherein the presence or absence of an abnormality in the wind turbine blade is detected based on at least one of the comparison results of the above. transmitting information of the load calculated in the step of calculating the load to the outside of the wind turbine generator;
The method for diagnosing a wind turbine blade according to any one of claims 1 to 8, comprising: The method for diagnosing a wind turbine blade according to any one of claims 1 to 9, wherein, in the step of calculating the load, an existing calculation device included in an existing wind turbine generator calculates the load based on the detection signal. . A step of additionally installing a computing device for computing a load acting on the wind turbine blade based on the detection signal, to the wind turbine generator;
with
The wind turbine according to any one of claims 1 to 10, wherein in the step of calculating the load, the load is calculated by the calculating device additionally provided in the step of additionally installing the calculating device based on the detection signal. How to diagnose wings. adding to the wind turbine generator a power storage device capable of supplying electric power to the computing device added in the step of adding the computing device;
The diagnostic method for a wind turbine blade according to claim 11, comprising: calibrating the sensor;
The method for diagnosing a wind turbine blade according to any one of claims 1 to 12, comprising: In the step of calibrating the sensor,
at least,
a step of fixing the wind turbine blade at an azimuth angle at which the blade axis of the wind turbine blade is horizontal and acquiring a detection signal from the sensor;
a step of fixing the wind turbine blade at an azimuth angle in which the blade axis is closest to the vertical direction, and obtaining a detection signal from the sensor;
14. The method of diagnosing a wind turbine blade according to claim 13, wherein the sensor is calibrated based on the detection signal from the sensor acquired by performing the above. a step of additionally installing an azimuth angle estimation sensor for estimating the azimuth angle of the wind turbine blade;
a step of additionally installing a pitch angle estimation sensor for estimating the pitch angle of the wind turbine blade;
estimating the azimuth angle based on a detection signal from the azimuth angle estimation sensor;
estimating the pitch angle based on a detection signal from the pitch angle estimation sensor;
The method for diagnosing a wind turbine blade according to any one of claims 1 to 14, comprising: The wind turbine blade diagnosis method according to any one of claims 1 to 15, wherein in the step of acquiring the detection signal, a sampling frequency for acquiring the detection signal is changed based on the rotation speed of the wind turbine blade. A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
a step of transmitting information of the load calculated in the step of calculating the load to the outside of the wind turbine generator;
A method for diagnosing a wind turbine blade comprising: A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
calibrating the sensor;
A method for diagnosing a wind turbine blade comprising: In the step of calibrating the sensor,
at least,
a step of fixing the wind turbine blade at an azimuth angle at which the blade axis of the wind turbine blade is horizontal and acquiring a detection signal from the sensor;
a step of fixing the wind turbine blade at an azimuth angle in which the blade axis is closest to the vertical direction, and obtaining a detection signal from the sensor;
19. The method of diagnosing a wind turbine blade according to claim 18, wherein the sensor is calibrated based on the detection signal from the sensor acquired by performing the above. In the step of calibrating the sensor, the sensor is calibrated based on the output value curve obtained when the azimuth angle is plotted on the horizontal axis and the output value of the detection signal from the sensor is plotted on the vertical axis. The method for diagnosing a wind turbine blade according to claim 19. A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
a step of additionally installing an azimuth angle estimation sensor for estimating the azimuth angle of the wind turbine blade;
a step of additionally installing a pitch angle estimation sensor for estimating the pitch angle of the wind turbine blade;
estimating the azimuth angle based on a detection signal from the azimuth angle estimation sensor;
estimating the pitch angle based on a detection signal from the pitch angle estimation sensor;
A method for diagnosing a wind turbine blade comprising: The wind turbine blade diagnosis method according to claim 21, wherein in the step of calculating the load, the load acting on the wind turbine blade is calculated in consideration of the azimuth angle estimated in the step of estimating the azimuth angle. A method for diagnosing a wind turbine blade of an existing wind power generator, comprising:
a step of additionally installing a sensor for detecting distortion of the wind turbine blade;
obtaining a detection signal from the sensor;
calculating a load acting on the wind turbine blade based on the detection signal;
a step of detecting the presence or absence of an abnormality in the wind turbine blade based on the detection signal;
with
The diagnostic method for a wind turbine blade, wherein in the step of acquiring the detection signal, a sampling frequency for acquiring the detection signal is changed based on the rotational speed of the wind turbine blade.

Images