EP4182557A1

EP4182557A1 - Device for identifying an accumulation of ice on rotor blades of a wind turbine and method for teaching such a device

Info

Publication number: EP4182557A1
Application number: EP21739393.3A
Authority: EP
Inventors: Daniel Brenner; Christian Kühnert; Stefan Reimann
Original assignee: Weidmueller Monitoring Systems GmbH
Current assignee: Weidmueller Monitoring Systems GmbH
Priority date: 2020-07-15
Filing date: 2021-07-07
Publication date: 2023-05-24
Also published as: DE102020118646A1; CA3181438A1; US20230272783A1; WO2022013032A1

Abstract

The invention relates to a method for teaching a device (6) for identifying an accumulation of ice on at least one rotor blade (41) of a wind turbine (1), said method having the following steps: - determining operating and ambient conditions during operation of the wind turbine (1); - determining an electrical output of the wind turbine (1) that is to be expected under the operating and ambient conditions determined; - measuring an electrical output that is actually generated by the wind turbine (1); - comparing the expected electrical output of the wind turbine (1) with the electrical output actually generated by same; - depending on the result of the comparison, determining with a high level of probability whether the at least one rotor blade (41) is free of ice; and - detecting vibrations of the at least one rotor blade (41), deriving characteristic properties of the vibrations and storing the characteristic properties as a reference for the device (6) for identifying an accumulation of ice if, in the comparison, it was determined with a high level of probability that the at least one rotor blade (41) is free of ice. The invention also relates to a device for identifying an accumulation of ice on at least one rotor blade (41) of a wind turbine (1), which device is designed to carry out a method of this kind.

Description

Device for detecting ice build-up on rotor blades of a wind turbine and method for teaching such a system

contraption

The invention relates to a method for teaching a device for recognizing ice build-up on rotor blades of a wind turbine, with ice being detected on the basis of natural vibration measurements of the rotor blades. The invention further relates to such a device for detecting an ice buildup, hereinafter also referred to as an ice detection device.

The evaluation of vibrations of a rotor blade of a wind turbine either via vibration sensors that are arranged in the rotor blade itself and/or via vibration sensors that are arranged on components that are connected to the rotor blade, such as a drive train or a nacelle of the wind turbine, is a tried and tested means of detecting the accumulation of additional masses and, in particular, ice on the rotor blade. Large amounts of ice can accumulate on rotor blades, up to the tens or hundreds of kilograms (kg). Reliable detection of accumulated ice is of great interest in order to avoid danger from falling or thrown ice and to avoid damage to the drive train of the wind turbine.

The publication DE 102016 124554 A1 describes a method for detecting ice on a rotor blade of a wind turbine, in which ice accumulation is inferred based on a change in the natural frequency. In order to achieve reliable detection results, this method is combined with measurement results from sensors, which can be used to directly infer ice growth. Such sensors are, for example, conductivity sensors on the surface of the rotor blade or optically or acoustically working sensors that can determine the layer thickness of an ice growth. The disadvantage of this combined method is that ice is only detected locally and immediately in the area of the sensor and that, in addition to vibration sensors, other sensors must be arranged directly on the rotor blade, which are correspondingly maintenance-intensive due to their position.

In the publication WO 2004/104412 A1, an operating parameter of a wind turbine, in particular the power produced by it, is used for ice detection as a function of boundary conditions such as a wind speed. speed is recorded and compared with previously known values for this operating parameter. This is based on the knowledge that ice accumulation leads to a reduction in the output of the wind turbine. If the power actually produced by this system is known at a given wind speed, a comparison of the power currently supplied with a known wind speed can indicate ice formation. In order to be able to carry out the procedure with sufficient informative value, comparative values of the system in question are required.

According to publication DE 102017 129 112 A1, the detection reliability of the method described in the aforementioned publication WO 2004/104412 A1 is increased by using it in combination with another method for detecting ice. Each of the methods used issues a warning if there is a certain probability that ice has formed. Each of the methods can also output a signal that indicates with a certain probability that the system is ice-free. An intervention in the operation of the wind energy installation, for example braking of the wind energy installation, takes place as soon as one of the systems issues a warning signal with regard to possible ice accumulation. As soon as one of the systems then emits an ice-free signal, intervention in the operation of the wind turbine is deactivated again. In this way, the effects of a possibly unjustified intervention in the operation of the wind turbine are to be reduced. The disadvantage is that the operation can be impaired at all, even if ice accumulation is not predicted with certainty, for example if only one of two systems has recognized ice accumulation.

When using ice warning systems, which are based on an evaluation of the natural vibrations of the blade, it has been shown that a high level of detection reliability can be achieved if reliable reference data, e.g. in the form of reference spectra, about the vibration behavior of the blades in an ice-free state for various Operating and environmental conditions (e.g. wind speed, outside and/or blade temperature, rotor speed, angle of attack of the blades) are present.

If a wind turbine is installed in a cold season and in a region prone to icing, it must be ensured that the rotor blades of the wind turbine are free of ice in order to record the reference spectra. A well-known option is to wait for a minimum outside temperature, for example at least +5°C, before recording reference spectra. Depending on However, depending on the region and season, it can take months for such a condition to be met. Within this time, the ice detection device actually provided for the wind turbine cannot be replaced, or cannot be reliably replaced.

It is an object of the present invention to specify a teaching method of the type mentioned at the outset that enables reliable operation of the device for ice detection, even if the ambient temperature cannot be used to conclude that there is no ice. It is another object to provide an ice detection device that can be appropriately calibrated.

This object is achieved by a method and a device with the features of the respective independent claim. Advantageous refinements and developments are the subject of the dependent claims.

A method according to the invention of the type mentioned has the fol lowing steps: Operating and environmental conditions in the loading operation of the wind turbine are determined and an expected electrical power of the wind turbine under the specific operating and environmental conditions is determined. When the wind turbine is in operation, the electrical power actually generated by the wind turbine is measured and compared with the expected electrical power of the wind turbine. Depending on a result of the comparison, it is determined whether there is a high probability that the at least one rotor blade is ice-free. If the comparison determined that there is a high probability that the at least one rotor blade is ice-free, vibrations of the at least one rotor blade are recorded and their characteristic properties are recorded and stored, with the characteristic properties serving as a reference for the device for detecting ice accumulation .

According to the invention, a further method for ice detection is thus used in order to detect the absence of ice with at least a high degree of probability. The method according to the application allows the duration of the learning phase to be shortened since, after a performance comparison has taken place, an oscillation reference for the ice detection device can also be recorded if freedom from ice cannot be derived from the ambient temperature (outside temperature). Another method of determining whether the blades are ice-free is to use the power of the wind turbine under the given operating conditions. This is advantageous because it provides information about the absence of ice can be done without the need for additional sensors. Current actual performance data is usually available.

A "high probability" of recognizing the absence of ice is understood to mean a probability of more than about 80%, for example, within the scope of the registration.

In an advantageous embodiment of the method, the operating and environmental conditions include a wind speed, an environmental and/or blade temperature, an angle of attack of at least one rotor blade and/or a speed of a rotor of the wind energy installation. The electrical power to be expected is advantageously determined using a model that reflects measured power under measured operating and environmental conditions.

A comparison of the performance to be expected with the actual performance can easily achieve a sufficient level of significance for the statement "ice-free" in the case of a model-based determination of the performance to be expected. The model is preferably based on performance data that is measured on the specific wind turbine itself. Alternatively, it is also possible to measure the performance of a wind energy installation that is comparable to the wind energy installation and to use this as a basis for the model.

In a further advantageous embodiment of the method, in the comparison step, a quotient is formed between the electrical power actually generated and the electrical power to be expected from the wind turbine and compared with a predetermined threshold value. If the threshold value is exceeded, it is assumed that the at least one rotor blade is free of ice. The threshold value is preferably between 60% and 95% and in particular between 80% and 95%.

A frequency and/or amplitude of at least one vibration state, e.g. It is also conceivable that the characteristic properties of the vibrations relate to at least one frequency range from a spectrum of the vibrations. In this case, the vibration spectrum or a section of it is saved as a reference spectrum.

A device according to the invention for detecting an accumulation of ice on at least one rotor blade of a wind turbine detects ice on the basis of Natural vibration measurements on the at least one rotor blade and is characterized in that the device is set up to carry out such a training process. The advantages mentioned in connection with the learning process result.

The invention is explained in more detail below using an exemplary embodiment with the aid of figures. The figures show:

Fig. 1 is a schematic sectional view of part of a wind energy plant;

FIG. 2 shows a diagram for representing natural frequency states in the case of a rotor blade of a wind turbine; FIG.

3 shows a representation of an amplitude spectrum of vibration states of a rotor blade of a wind turbine;

FIG. 4 shows a diagram to show the influence of an accumulation of ice on the efficiency of the wind turbine; FIG. and

5 shows a flow chart of a method for training a device for detecting ice accumulation on a rotor blade.

In Fig. 1 a sectional drawing of a part of a wind energy plant 1 is shown as an example, which has a device 6 for detecting ice accumulation on a rotor blade. The wind energy installation 1 shown in FIG. 1 is suitable and set up for carrying out a teaching method for the device 6 according to the application. The device 6 is also referred to below as an ice detection device 6 .

The wind energy installation 1 has a nacelle 3 which is rotatably mounted on a tower 2 and carries a rotor 4 . The rotor 4 has at least one rotor blade 41 which is connected to a hub 42 with a rotor shaft 51. The area of the hub 42 and the base of the rotor blades 41 is covered by a spinner 43 .

In FIG. 1, two rotor blades 41 shown cut to length are shown as an example. This is purely an example, wind turbines often have three rotor blades 41 . Said rotor shaft 51 is part of a drive train 5. It transmits the rotational movement of the rotor 4 to a gear 52. This in turn is coupled via a gear shaft 53 and a clutch 54 to a generator 55, which converts the mechanical energy of the rotor 4 into electrical energy converts. The representation of the wind turbine 1 with gear 55 is also purely by way of example. The device according to the application and the method according to the application can be implemented just as well with a gearless wind energy installation.

The ice detection device 6 for detecting an accumulation of ice on one or more of the rotor blades 41 comprises at least one vibration sensor 61 , hereinafter referred to as sensor 61 for short.

In the present case, a sensor 61 is arranged in each of the rotor blades 41 shown. Each sensor 61 is connected to an evaluation unit 63 via a sensor line 62 . The type of connection is shown in FIG. 1 purely as an example. As a rule, there is a connection between the sensors 61 and the evaluation unit 63 via sensor lines running in the rotor blade 41 to the spinner 43, from where transmission to the evaluation unit 63, which is usually wireless, takes place. In alternative configurations, the sensors 61 can be coupled to energy generation units (“energy harvesting”) so that they obtain energy, for example, from the rotation of the rotor 4 and transmit data directly from the rotor blade 41 to the evaluation unit 63 via radio. A power supply of the sensors 61 via optical fibers within the rotor blades 41, as well as optical data transmission from the sensors 61 to the evaluation unit 63 or at least to a radio relay station in the spinner 43 is conceivable.

The sensors 61 are vibration pickups that detect vibration of the rotor blade 41 . The sensors 61 can be acceleration, strain or yaw rate sensors. Vibration is then detected as a change in a measured acceleration value, a measured velocity, or a measured extension. The arrangement of the sensors 61 within the rotor blade 51 can be such that vibrations in the pivoting direction (“edge”) and/or in the flapping direction (“flap”) and/or in the torsional direction of the respective rotor blade 41 are detected.

Purely by way of example, the two sensors 61 shown in FIG. 1 are arranged approximately in a lower third of the rotor blade 41 . The sensors 61 can However, it can also be arranged at other positions in the rotor blade 41 . In addition, it is possible to arrange a plurality of sensors 61 in each rotor blade 41, which are evaluated jointly or independently of one another.

Furthermore, it is possible to also detect vibrations of the rotor blades 41 on other components of the wind energy installation 1, on which corresponding vibration sensors are then arranged. For example, sensors can be arranged in the hub 42 and/or along the drive train 5, with vibrations of the rotor blade 41 that show up in these sensors, based on their frequency range, for example, of inherent vibrations on the drive train 5, for example due to gear meshing in the transmission 42, can be distinguished.

2 shows a schematic representation of possible vibration states 7 of a rotor blade, for example one of the rotor blades 41 according to FIG. 1. A vibration amplitude is shown on the vertical axis of the diagram as a function of a position along the rotor blade on the horizontal axis.

Each of the curves 71-74 represents an instantaneous deflection that is characteristic of the vibration state 7 in question. The "0" position on the horizontal axis corresponds to the position of the blade root and the "max" position on the horizontal axis corresponds to the position of the blade tip.

In Fig. 2 four vibrational states 7 are shown, a basic state in curve 71, a first harmonic in curve 72, which is characterized by a vibration node along the extension of the rotor blade, a second harmonic in curve 73, which is characterized by two vibration nodes and one third harmonic in curve 74, which is characterized by three vibration nodes along the rotor blade. In the following, the fundamental according to curve 71 is referred to as the first natural frequency state and the first, second and third harmonic as the second, third and fourth natural frequency state.

2 shows transverse vibrations, ie vibrations in the pivoting direction or flapping direction of the rotor blade. A comparable picture also emerges for torsional vibrations, i.e. twisting of the rotor blade around its longitudinal axis. During the operation of the ice detection device 6, the time-dependent vibration deflection derived from its measurement signals is recorded for each of the sensors 61 for a specific period of time.

An amplitude spectrum is then preferably determined from the oscillation recorded in the time domain. The transformation into the frequency range, ie the representation as a spectrum, can take place, for example, by means of a Fast Fourier Transform (FFT) or a Wavelet Transform. Alternatively, instead of a transformation into the frequency domain, natural frequency states can also be determined in the time domain by appropriate filtering or by stochastic methods, for example using the so-called "Stochastic-Subspace Identification" (SSI).

3 shows a spectrum transformed, for example via FFT, from the vibration recordings in the time domain into the frequency domain in a spectral curve 75. The amplitude of the vibration is shown on the vertical axis as a function of the frequency plotted on the horizontal axis.

In this representation, natural frequency states can be identified in a simple manner as maxima of the spectral curve 75 . The assignment of the maxima to the different natural frequency states is possible through the increasing frequency.

The vibration measurement and formation of a spectrum described is repeated at regular time intervals. The natural frequency states determined from the spectrum according to FIG. 3 change as a result of ice accretion. These are characterized by their frequency and an associated maximum amplitude. In order to detect ice accumulation, a currently measured spectrum is compared with a reference spectrum previously recorded in an ice-free state. Deviations greater than a specified value are indicative of ice accumulation, and the specified value can be varied in size to indicate ice accumulation with different levels of significance. A comparison of the current spectrum with the reference spectrum over the entire accessible frequency range is preferred. However, it is also conceivable to only evaluate certain frequency ranges or to take them into account in comparison, up to a selective evaluation for only one frequency or several selected frequencies. Irrespective of which frequency range is taken into account in the comparison, for reliable detection of ice accumulation it is necessary to have a reference, e.g. in the form of a reference spectrum, which was recorded when the rotor blade 41 was in an ice-free state and which describes the vibration behavior in characterized in the ice-free state. As a rule, provision is made for using a set with different reference spectra that were recorded under different conditions, ie for example different speeds or speed ranges of the rotor of the wind energy installation. Here, too, there is a need for the reference spectra to be recorded when the rotor blades were in an ice-free state. The recording of the reference spectrum or spectra is referred to as a teach-in procedure.

In order to get the ice detection device ready for use as quickly as possible after installing a wind turbine with an ice detection device for detecting ice build-up or after retrofitting such an ice detection device, an ice-free state of the rotor blades of the wind turbine is detected independently of an outside temperature in the training process according to the application. If the outside temperature is higher, for example if the outside temperature is above 5°C, ice formation can be ruled out. Conversely, however, there is not necessarily an accumulation of ice just because the outside temperature is below this value. In the method according to the invention, therefore, an indicator for freedom from ice that is independent of the outside temperature is used in order to be able to record reference spectra, possibly even at a lower outside temperature.

The training method is based on a comparison of the electrical power produced by the wind energy plant with an expected electrical power. The quotient of the electrical power produced and the expected electrical power is also referred to as the efficiency of the wind turbine. To determine the efficiency, the operating conditions of the wind energy plant must be taken into account, in particular a wind speed and possibly also an ambient and/or blade temperature.

In the form of a schematic diagram, FIG. 4 shows a dependency of an efficiency e, i.e. the quotient between the electrical power actually produced and the expected electrical power, as a function of the amount of ice m on the rotor of the wind turbine horizontal axis increasing to the right in arbitrary Units shown. The efficiency e is shown on the vertical axis in a value range from 0 to 100%. The diagram shows the decrease in efficiency from a value of 100% with an ice-free rotor as the amount of ice m increases. A predetermined threshold value eo of efficiency e is shown, which is around 65% in the example shown. If the efficiency e of the wind turbine is above this threshold value eo, it can be assumed that there is no or only a very small amount of ice on the rotor, at most a quantity mo. If such an operating state is identified, vibration spectra recorded by the monitoring device can be viewed as reference spectra and stored accordingly.

FIG. 5 shows an exemplary embodiment of a training method in more detail in the form of a flow chart. The method shown in FIG. 5 can be carried out, for example, with the wind energy installation 1 according to FIG. 1 and is therefore explained below by way of example with reference to FIG.

In a first step S1, for the wind turbine, z. B. the wind energy plant 1 according to Figure 1, a model created that an expected performance under different operating conditions such. B. wind speed, tempera ture, speed of the rotor 4 with ice-free rotor blades 41 and the angle of attack of the rotor blades 41 created. Existing measured values that were recorded for an already installed wind energy installation of the same type are preferably used as a basis. If the monitoring device 6 is retrofitted, it may also be possible to fall back on previously recorded measured values on the specific wind energy installation 1 . If measured values are not available over the entire required parameter range of the operating and ambient conditions, the range can be extended from measured values by regression calculation.

In a next step S2, the operating and environmental conditions on which the model is based are determined at the wind turbine 1 during operation.

In a subsequent step S3, the expected power of the wind energy plant 1 can be estimated from the model generated in step S1 on the basis of the operating and environmental conditions measured in step S2.

In the following step S4, a difference between the power actually measured and the power expected is determined, preferably as a ratio, possibly in other ren embodiments as a difference. The ratio corresponds to the efficiency e of the wind turbine shown in FIG. Furthermore, in step S4, the determined efficiency e is compared with a predefined threshold value eo. Possible limit values are in the range of about 60 - 95%. Falling below the threshold value eo indicates that the rotor blades 41 are not free of ice. In this case, the method branches back to step S2 in order to determine operating parameters again and carry out a power measurement. Since the absence of ice does not change within a very short time, a pause of, for example, a few hours can be provided before the method continues with the steps S2 ff. repeated.

If, on the other hand, it is determined in step S4 that the predefined threshold value eo for the efficiency e has been exceeded, the method is continued with a step S5, in which the monitoring device 6 uses the sensors 61 to record vibrations of the rotor blades 41, the spectrum of which serves as a reference spectrum is stored for the operating and environmental conditions recorded in step S2.

In a step S6, it is then checked whether reference spectra are available in sufficient number and quality for a sufficiently large range of operating and environmental conditions. If this is not the case, the method branches back to step S2 again in order—again, possibly after a waiting time—to be able to record further reference spectra under different operating and environmental conditions. If it is determined in step S6 that a sufficiently large and qualitatively suitable set of reference spectra is present, the training phase for the monitoring device 6 is complete and the monitoring device 6 can be operated in the regular monitoring mode in a step S7.

The method shown can be combined with a known training method in which reference spectra are recorded when the outside temperature is so high, for example above 5° C., that it can be assumed with a high degree of probability that the rotor blades 41 are free of ice.

The method according to the application allows the duration of the learning phase to be shortened, since under certain circumstances reference spectra for the monitoring device can also be recorded after the performance comparison has taken place if freedom from ice cannot be derived from the outside temperature. Reference sign

1 wind turbine

2 tower 3 nacelle

4 rotors

41 sheets

42 hub 43 spinner

5 drive train

51 rotor shaft

52 gear 53 gear shaft

54 clutch

55 Generator

6 monitoring device 61 sensor

62 sensor line

63 evaluation unit

7 vibrational state 71-74 curve

75 spectral curve

S1-S7 step

Claims

Expectations

1 . Method for teaching a device (6) for detecting an accumulation of ice on at least one rotor blade (41) of a wind turbine (1), with the following steps:

- Determining operating and environmental conditions during operation of the wind turbine (1);

- Determining an electrical output of the wind turbine (1) to be expected given the specific operating and environmental conditions;

- Measuring an electrical power actually generated by the wind turbine (1);

- Comparing the expected electrical power of the wind energy plant (1) with the electrical power actually generated by it;

- As a function of a result of the comparison, determine whether the at least one rotor blade (41) has a high probability of being ice-free; and

- Detecting vibrations of the at least one rotor blade (41), deriving characteristic properties of the vibrations and storing the characteristic properties as a reference for the device (6) for detecting ice accumulation if it was determined in the comparison that the at least one rotor blade ( 41 ) is ice-free with a high probability.

2. The method as claimed in claim 1, in which the operating and environmental conditions are a wind speed, an environmental and/or blade temperature, an angle of attack of at least one rotor blade (41) and/or a speed of a rotor (4) of the wind turbine (1st ) include.

3. The method as claimed in claim 1 or 2, in which the electrical power to be expected is determined using a model which reflects measured power under measured operating and environmental conditions.

4. The method as claimed in claim 3, in which the performance of a wind energy installation which is comparable to the wind energy installation (1) is measured.

5. The method as claimed in claim 3, in which the powers are measured at the wind energy plant (1) itself.

6. The method as claimed in one of claims 1 to 5, in which, in the comparison step, a quotient is formed between the electrical power actually generated and the electrical power to be expected from the wind turbine (1) and is compared with a predetermined threshold value .

7. The method as claimed in claim 6, in which, if the threshold value is exceeded, it is assumed that the at least one rotor blade (41) is free of ice.

8. The method according to claim 6 or 7, wherein the threshold is between 60% and 95% and preferably between 80% and 95%.

9. The method as claimed in one of claims 1 to 8, in which the characteristic properties of the oscillations relate to a frequency and/or amplitude of an oscillation state.

10. The method as claimed in claim 9, in which the vibrational state corresponds to that of a maximum in a vibrational spectrum of the vibrations.

11. The method as claimed in one of claims 1 to 8, in which the characteristic properties of the vibrations relate to at least one frequency range from a spectrum of the vibrations.

12. A device for detecting the accumulation of ice on at least one rotor blade (41) of a wind turbine (1), in which ice is detected on the basis of natural vibration measurements on the at least one rotor blade (41), characterized in that the device for carrying out a Training method according to one of claims 1 to 11 is set up.