阅读说明：本技术 使用机械风传感器确定结冰状况 (Determining icing conditions using mechanical wind sensors ) 是由 J·尼尔森 J·D·格林内特 于 2018-12-11 设计创作，主要内容包括：本文的实施例描述了一种用于风力涡轮机的方法和相关联的控制器。该方法包括：确定从风力涡轮机的机械传感器接收的传感器信号的一个或多个特征；基于所述传感器信号的所述一个或多个5特征来确定所述风力涡轮机的结冰状况；以及基于所确定的结冰状况来控制一个或多个风力涡轮机系统的操作。(Embodiments herein describe a method and associated controller for a wind turbine. The method comprises the following steps: determining one or more characteristics of a sensor signal received from a mechanical sensor of the wind turbine; determining an icing condition of the wind turbine based on the one or more 5 characteristics of the sensor signal; and controlling operation of one or more wind turbine systems based on the determined icing condition.)

1. A controller for a wind turbine, the controller comprising:

one or more computer processors; and

memory including computer readable code which, when executed using the one or more computer processors, performs operations comprising:

determining one or more characteristics of a sensor signal received from a mechanical sensor of the wind turbine;

determining an icing condition of the wind turbine based on the one or more characteristics of the sensor signal; and

controlling operation of one or more wind turbine systems based on the determined icing condition.

2. The controller of claim 1, wherein the sensor signal comprises one of a wind speed signal and a wind direction signal.

3. The controller of claim 1 or 2, wherein determining one or more characteristics of the sensor signal comprises:

determining frequency information included in the sensor signal.

4. The controller of claim 3, wherein determining the icing condition of the wind turbine comprises one or more of:

determining whether a predetermined percentage of power included in the sensor signal occurs below or above a predetermined threshold frequency;

determining whether a power included in the sensor signal exceeds a predetermined magnitude at a predetermined frequency;

determining whether a power included in the sensor signal exceeds a predetermined amplitude within a predetermined frequency interval;

determining whether an average power included in the sensor signal exceeds a predetermined magnitude within a predetermined frequency interval;

determining a maximum frequency value or a maximum frequency interval in which the magnitude of the power comprised in the sensor signal exceeds a predetermined magnitude;

determining a ratio of a first average of power included in the first frequency interval to a second average of power included in the second frequency interval; and

determining a shape of power included in the sensor signal.

5. A controller according to any of the preceding claims, wherein determining an icing condition of the wind turbine comprises one or more of:

a moving standard deviation of the sensor signal is determined.

6. A controller according to any of the preceding claims, wherein determining an icing condition of the wind turbine comprises:

it is determined whether ice accumulation is increasing or decreasing.

7. A controller according to any of the preceding claims, wherein determining an icing condition of the wind turbine is performed with respect to a reference signal.

8. A method, comprising:

determining one or more characteristics of a sensor signal received from a mechanical sensor of the wind turbine;

determining an icing condition of the wind turbine based on the one or more characteristics of the sensor signal; and

controlling operation of one or more wind turbine systems based on the determined icing condition.

9. The method of claim 8, wherein the sensor signal comprises one of a wind speed signal and a wind direction signal.

10. The method of claim 8 or 9, wherein determining one or more characteristics of the sensor signal comprises:

determining frequency information included in the sensor signal.

11. The method of claim 10, wherein determining the icing condition of the wind turbine comprises one or more of:

determining whether a predetermined percentage of power included in the sensor signal occurs below or above a predetermined threshold frequency;

determining whether a power included in the sensor signal exceeds a predetermined magnitude at a predetermined frequency;

determining whether a power included in the sensor signal exceeds a predetermined amplitude within a predetermined frequency interval;

determining whether an average power included in the sensor signal exceeds a predetermined magnitude within a predetermined frequency interval;

determining a maximum frequency value or a maximum frequency interval in which the magnitude of the power comprised in the sensor signal exceeds a predetermined magnitude;

determining a ratio of a first average of power included in the first frequency interval to a second average of power included in the second frequency interval; and

determining a shape of power included in the sensor signal.

12. The method of any of claims 8 to 11, wherein determining the icing condition of the wind turbine comprises one or more of:

a moving standard deviation of the sensor signal is determined.

13. The method of any of claims 8 to 12, wherein determining the icing condition of the wind turbine comprises:

it is determined whether ice accumulation is increasing or decreasing.

14. A method according to any of claims 8 to 13, wherein determining an icing condition of the wind turbine is performed with respect to a reference signal.

15. A wind turbine, comprising:

one or more mechanical sensors; and

the controller of claim 1.

Technical Field

Embodiments presented in the present disclosure relate generally to determining icing conditions on wind turbines, and more particularly to determining icing conditions on associated wind turbines using one or more mechanical wind sensors.

Background

Wind turbines are typically deployed in colder regions because the higher density of cold air corresponds to greater power production, and because other energy production systems may be impractical in these regions. However, in cold regions, the likelihood of ice formation on the wind turbine is also greater. Ice formation on the turbine reduces efficiency, and removing ice may require stopping the turbine, further reducing efficiency. Currently, wind turbines with anti-icing or de-icing systems use a significant reduction in the power curve to detect ice on the blades. However, this technique is generally unable to detect icing conditions before a significant amount of ice has accumulated on the blades. Furthermore, if the icing condition persists, the de-icing system may not be able to remove the ice, and/or the ice may accumulate again shortly after the de-icing is complete. Other ice detection embodiments may use accelerometers located inside the wind turbine blades, or may use dedicated ice detection instrumentation located on the nacelle, which is a relatively expensive embodiment.

Disclosure of Invention

One embodiment of the present disclosure is a controller for a wind turbine, the controller comprising one or more computer processors and memory including computer readable code that, when executed using the one or more computer processors, performs operations. The operations include determining one or more characteristics of a sensor signal received from a mechanical sensor of the wind turbine. The operations further include determining an icing condition of the wind turbine based on the one or more characteristics of the sensor signal. The operations also include controlling operation of one or more wind turbine systems based on the determined icing condition.

Advantageously, the controller allows determining an icing condition of the wind turbine without reducing the power production of the wind turbine. Further, the controller can determine whether the ice buildup on the wind turbine is increasing or decreasing. Still further, the controller may support a relatively simple implementation using mechanical sensors typically included on wind turbines, without requiring more expensive sensors dedicated to detecting ice.

Another embodiment described herein is a method comprising: determining one or more characteristics of a sensor signal received from a mechanical sensor of the wind turbine; determining an icing condition of the wind turbine based on the one or more characteristics of the sensor signal; and controlling operation of one or more wind turbine systems based on the determined icing condition.

Advantageously, the method allows determining an icing condition of the wind turbine without reducing the power production of the wind turbine. Furthermore, the method may be used to determine whether the ice accumulation on the wind turbine is increasing or decreasing. Still further, the method may be performed using a relatively simple implementation using mechanical sensors typically included on wind turbines, without the need for more expensive sensors dedicated to detecting ice.

Drawings

So that the manner in which the above recited features of the present disclosure can be understood in detail, a more particular description of the disclosure, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this disclosure and are therefore not to be considered limiting of its scope, for the disclosure may admit to other equally effective embodiments.

FIG. 1 illustrates a schematic view of an exemplary wind turbine in accordance with one or more embodiments.

FIG. 2 illustrates a schematic view of internal components of an exemplary nacelle and tower of a wind turbine in accordance with one or more embodiments.

FIG. 3 is a block diagram of an exemplary wind turbine in accordance with one or more embodiments.

FIG. 4 illustrates an exemplary method for controlling operation of a wind turbine based on a determined icing condition in accordance with one or more embodiments.

Fig. 5A and 5B illustrate exemplary sensor signals without and with ice accumulation, respectively, in accordance with one or more embodiments.

Fig. 6A and 6B illustrate frequency information of an exemplary sensor signal without and with ice accumulation, respectively, in accordance with one or more embodiments.

FIG. 7 illustrates determining moving standard deviations of exemplary sensor signals without and with ice accumulation in accordance with one or more embodiments.

FIG. 8 illustrates an exemplary method for controlling operation of a wind turbine based on whether ice buildup on the wind turbine is increasing in accordance with one or more embodiments.

To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized on other embodiments without specific recitation.

Detailed Description

Wind turbines use a rotor that includes one or more blades to convert kinetic energy of wind into electrical energy. More specifically, the speed of the wind rotates the blades, which in turn powers the generator. The accumulation of ice on the blades tends to reduce the power production of the wind turbine under given wind conditions. In addition, the accumulation of ice on the blades may cause an imbalance that may damage the wind turbine.

Different techniques may be used to remove ice accumulated on the wind turbine blades during power production (e.g., when the blades are rotating) or when the wind turbine is at a standstill. Although continued operation of a wind turbine with ice build-up may correspond to a reduction in efficiency, in some cases, this may be economically preferable compared to stopping the wind turbine rotor to perform a de-icing procedure.

Embodiments disclosed herein describe techniques for determining icing conditions of a wind turbine using mechanical sensors of the wind turbine. Advantageously, the determination of the icing condition using the mechanical sensor may be performed without reducing the power production of the wind turbine. The sensor signal of the mechanical sensor may be used to determine whether the ice accumulation on the wind turbine is increasing or decreasing. Furthermore, mechanical sensors may be included in typical wind turbine embodiments for wind measurement without the need for more expensive sensors dedicated to detecting ice.

Example embodiments

FIG. 1 illustrates a schematic view of an exemplary wind turbine 100. Although wind turbine 100 is shown as a horizontal-axis wind turbine, the principles and techniques described herein may be applied to other wind turbine embodiments (such as vertical-axis wind turbines). Wind turbine 100 generally includes a tower 102 and a nacelle 104 positioned atop tower 102. The rotor 106 may be coupled to the nacelle 104 via a low speed shaft that extends out of the nacelle 104. As shown, rotor 106 includes three rotor blades 108 mounted on a common hub 110 for rotation in a rotor plane, although rotor 106 may include any suitable number of blades (such as one, two, four, five, or more blades). The blades 108 (or airfoils) each generally have an aerodynamic shape with a leading edge 112 for facing into the wind, a trailing edge 114 at the opposite end of a chord line of the blade 108, a tip end 116, and a root 118 for attachment to the hub 110 in any suitable manner.

For some embodiments, blades 108 may be coupled to hub 110 using pitch bearings 120 such that each blade 108 may be rotated about its longitudinal axis to adjust the pitch of the blade. The pitch angle of the blades 108 with respect to the rotor plane may be controlled by, for example, a linear actuator, a hydraulic actuator, or a stepper motor connected between the hub 110 and the blades 108.

FIG. 2 shows a schematic view of typical components inside nacelle 104 and tower 102 of wind turbine 100. When wind 200 is blowing on blades 108, rotor 106 rotates and rotates low speed shaft 202. Gears in the gearbox 204 mechanically convert the low rotational speed of the low speed shaft 202 to a relatively high rotational speed of the high speed shaft 208 suitable for generating electrical power using the generator 206.

The controller 210 may sense the rotational speed of one or both of the low-speed shaft 202 and the high-speed shaft 208. If the controller 210 determines that the shaft is rotating too fast, the controller 210 may pitch the blades out, or slow the rotation of the rotor 106, i.e., reduce Revolutions Per Minute (RPM), by increasing the torque from the generator 206. When the hub is already in or very near a stationary state, braking system 212 may prevent damage to components of wind turbine generator 100 by holding hub 110 against rotation. Controller 210 may also receive input from an anemometer 214 (providing wind speed) and/or a wind vane 216 (providing wind direction). Based on the received information, the controller 210 may send control signals to one or more of the blades 108 to adjust the pitch 218 of the blades. By adjusting the pitch 218 of the blades, the rotational speed of the rotor (and thus the shafts 202, 208) may be increased or decreased. For example, based on the wind direction, controller 210 may send control signals to an assembly including yaw motor 220 and yaw drive 222 to rotate nacelle 104 relative to tower 102 such that rotor 106 may be positioned to face more (or in some cases less) upwind.

FIG. 3 is a block diagram of an exemplary wind turbine 300 in accordance with one or more embodiments. Wind turbine 300 may be used in conjunction with other embodiments described herein. For example, wind turbine 300 may represent one example of wind turbine 100 of FIG. 1.

Wind turbine 300 includes a controller 305, where controller 305 includes one or more computer processors 310 (or "processors") and memory 315. The one or more processors 310 represent any number of processing elements, each of which may include any number of processing cores. The memory 315 may include volatile memory elements (such as random access memory), non-volatile memory elements (such as solid-state, magnetic, optical, or flash-based memory), and combinations thereof. Further, memory 315 may be distributed on different media (e.g., network storage or external hard drives).

As shown, the one or more processors 310 are communicatively coupled with one or more mechanical sensors 330, ultrasonic wind sensors 345, and ambient temperature sensors 350. The one or more processors 310 are also coupled with one or more wind turbine systems, such as de-icing system 355, power generation system 360, and communication system 365. Other embodiments of wind turbine 300 having different numbers and/or types of sensors, and/or different numbers and/or types of wind turbine systems are also possible.

The mechanical sensor 330, the ultrasonic wind sensor 345, and the ambient temperature sensor 350 are each configured to provide one or more sensor signals to the controller 305. As shown, mechanical sensor 330 includes a wind vane 335 configured to generate a wind direction signal, and an anemometer 340 configured to generate a wind speed signal. The anemometer 340 may have any suitable mechanical implementation (such as a cup anemometer or a blade anemometer). Other embodiments of wind turbine 300 may include only one of a wind vane 335 and an anemometer 340 as mechanical sensor 330.

In some embodiments, each of the mechanical sensors 330 is configured to generate a respective sensor signal (e.g., a wind direction signal or a wind speed signal) by rotation of the respective mechanical sensor 330 about an axis. Taking FIG. 2 as an example, the anemometer 214 is configured to rotate about a substantially vertical axis as shown. The increased or decreased accumulation of ice on the mechanical sensor 330 affects its ability to rotate, which in turn is reflected in the generated sensor signal. Accordingly, in some embodiments, the accumulation of ice on the mechanical sensor 330 (or the icing condition of the mechanical sensor 330) may be determined based on one or more characteristics of the sensor signal. For example, the accumulation of ice increases the mass of the particular mechanical sensor 330, which affects the generated sensor signal due to mechanical damping. The affected sensor signals may then be used to infer icing conditions of wind turbine 300 (e.g., the likelihood that ice will also accumulate on the blades).

Although examples of mechanical sensors 330 are described in terms of rotating mechanical sensors, the techniques discussed herein may be applied to other mechanical sensors 330 having different types of physical actuation (e.g., linear reciprocating motion). It is contemplated that the accumulation of ice on these other mechanical sensors 330 may also exhibit a detectable effect on the generated sensor signal.

Memory 315 may include a number of "modules" for performing the various functions described herein. In one embodiment, each module includes program code that may be executed by one or more of the processors 310. However, other embodiments may include modules partially or fully implemented in hardware (i.e., circuitry) or firmware.

The memory 315 includes a signal analysis module 320, the signal analysis module 320 configured to determine one or more characteristics of a sensor signal received from the mechanical sensor 330. In some embodiments, the signal analysis module 320 is configured to determine one or more characteristics of the received sensor signal in the frequency domain. In one embodiment, the signal analysis module 320 is configured to determine frequency information (or "components") included in the received sensor signal, for example, by performing a Fast Fourier Transform (FFT) on the received sensor signal. Other frequency analysis techniques are also possible. In some embodiments, the signal analysis module 320 is configured to determine one or more characteristics of the received sensor signal in the time domain. In one embodiment, the signal analysis module 320 is configured to determine a moving standard deviation of the sensor signal (e.g., using time-based amplitude information of the sensor signal).

As will be discussed further, the signal analysis module 320 may be configured to perform further processing on the received sensor signals. In one example, the signal analysis module 320 may compare one or more characteristics of the sensor signal to one or more predetermined thresholds. In another example, the signal analysis module 320 may compare one or more characteristics of the sensor signal to one or more reference signals. The one or more reference signals may correspond to sensor signals acquired under a particular condition, such as a predetermined ice-free condition. Other techniques suitable for distinguishing one or more characteristics of the sensor signal are also possible.

The memory 315 also includes an icing condition module 325 configured to determine an icing condition of the wind turbine 300 based on one or more characteristics of the sensor signals. As described above, the accumulation of ice on mechanical sensor 330 may be used as an indicator that controller 305 infers that ice is accumulating on other surfaces of wind turbine 300 (such as blades).

The icing condition of the wind turbine 300 may be provided in any suitable form. Some non-limiting examples of icing conditions include a binary "yes" or "no" of whether ice has formed on the wind turbine 300, a likelihood that ice has formed on the wind turbine 300, an estimate of how much ice has formed on the wind turbine 330 (e.g., a total weight or weight distribution of ice, an ice-covered area of a surface of the wind turbine 300), whether an accumulation of ice on the wind turbine 300 is increasing or decreasing, a rate of change of the accumulation of ice, and so forth.

In some embodiments, other sensors (such as ultrasonic wind sensor 345 and/or ambient temperature sensor 350) may be used to confirm (or verify) the icing condition of wind turbine 300. The sensor signal acquired by the mechanical sensor 330 may be affected by physical conditions other than ice build-up, such as increased friction due to lubrication loss or damage to the mechanical sensor 330. For example, the controller 305 may use ambient temperature information from the ambient temperature sensor 350 to verify one or more characteristics of the sensor signal. For example, if the ambient temperature is sufficiently far from the freezing point of water (e.g., 6 ℃ (or 42.8 ° f)), the controller 305 may determine that one or more characteristics of the sensor signal more strongly indicate that the mechanical sensor 330 has a maintenance problem, rather than ice build-up.

In some embodiments, controller 305 may use icing conditions of wind turbine 300 to determine how (or whether) to employ non-mechanical sensors (such as ultrasonic wind sensor 345). In one example, during an ice-free condition of wind turbine 300, controller 305 only uses mechanical sensor 330 to acquire wind information, and when the sensor signal from mechanical sensor 330 indicates that there is an accumulation of ice on wind turbine 300, controller 305 (also) uses ultrasonic wind sensor 345 to acquire wind information. In another example, in acquiring wind information, the controller 305 provides relative weights to the wind measurements acquired by the mechanical sensor 330 and by the ultrasonic wind sensor 345. In this case, controller 305 may provide lower weight to the wind measurements from mechanical sensor 330 when there is an accumulation of ice on wind turbine 300 when compared to an ice-free condition. For example, assume that in a no ice condition, the controller 305 average weights the wind measurements (i.e., 50% of the wind measurements from the mechanical sensor 330 and 50% of the wind measurements from the ultrasonic wind sensor 345). Then, when there is an accumulation of ice, the controller 305 may weight the wind measurements of the mechanical sensor 330 differently (e.g., from 50% to 10%). Further, the value of the weights may be dynamically adjusted based on the determined icing conditions. For example, if the controller 305 determines that the accumulation of ice on the mechanical sensor 330 is decreasing (which corresponds to an increased confidence in the accuracy of the wind measurements), the weight associated with the wind measurements of the mechanical sensor 330 may be gradually increased.

Controller 305 is configured to control the operation of one or more wind turbine systems, such as de-icing system 355, power generation system 360, and/or communication system 365, based on the determined icing conditions. The controller 305 may be configured to operate one or more wind turbine systems simultaneously, such as to transmit icing condition information using the communication system 365 while operating the power generation system 360 at a reduced efficiency, to activate and operate the de-icing system 355 while operating the power generation system 360, and so forth.

The de-icing system 355 may perform de-icing on the blades of the wind turbine 300 using any suitable technique, such as electro-thermally heating the blades using heating elements disposed in or on the blades, applying chemicals (such as ethanol, glycol, or salts) to the blades, mechanical techniques (such as introducing vibrations into the blades or rotor speed changes, reintroducing hot air or exhaust gases from the nacelle into the blades), or combinations thereof.

In another embodiment, wind turbine 300 may include an anti-icing system configured to inhibit ice accumulation on blades and/or other surfaces of wind turbine 300. The anti-icing system may perform techniques similar to de-icing system 355, such as electrothermal heating, applying chemicals to cover a protective layer on a surface, or continuously applying a fluid such as glycol to a susceptible surface, applying a hydrophobic fluid or material to the blades, redirecting hot air or exhaust gases to the blades, and the like.

In some embodiments, the controller 305 is configured to activate the deicing system 355 for a predetermined icing condition (e.g., when the icing condition module 325 indicates that ice has accumulated on the wind turbine 300). The controller 305 may also be configured to control the rate of operation of the de-icing system 355 (e.g., the amount of power applied to the heating elements, the amount of chemicals applied to the blades, the amplitude of vibration, etc.) based on the icing conditions of the wind turbine 300.

The power generation system 360 of the wind turbine 300 includes the main wind turbine generator of the wind turbine 300 and other components associated with the generation, conversion and/or distribution of power to the grid (such as blade pitch systems, electrical converters, switching elements, filters, etc.). The power generation system 360 may also include any auxiliary power sources (e.g., diesel generators, batteries, etc.) included in the wind turbine 300. In some embodiments, controller 305 is configured to operate power generation system 360 at a reduced efficiency based on the determined icing condition.

In some embodiments, controller 305 is further configured to determine whether it is more economically beneficial (e.g., results in less power production loss) to operate power generation system 360 at a reduced efficiency than to operate deicing system 355. For example, in some embodiments of wind turbine 300, operating de-icing system 355 may require bringing wind turbine 300 to a standstill, and may correspond to a 100% production loss of up to 1 or 2 hours. However, when ice accumulation is not expected to last for more than some period of time (e.g., hours, days, weeks, etc., depending on the amount of ice and/or the amount of reduction in efficiency), it may be economically more advantageous to continue generating electricity at a reduced efficiency than to de-ice wind turbine 300.

Furthermore, performing de-icing on wind turbine 300 may have limited effect where icing condition information indicates that ice is continuing to accumulate. Even if the deicing system 355 is applied to the wind turbine 300, ice may accumulate on the wind turbine 300 again in a relatively short time due to environmental conditions. Thus, in some embodiments, the icing condition information provided by the icing condition module 325 may be used to determine which operation to perform using the wind turbine system. For example, when the icing condition information indicates that the accumulation of ice is increasing, the controller 305 may determine that it is more economically beneficial to perform the first predetermined operation (such as operating the power generation system 360 at a reduced efficiency). When the icing condition information indicates that the accumulation of ice is not increasing, the controller 305 may perform a second predetermined operation (such as operating the deicing system 355).

Communication system 365 includes any suitable means (e.g., wired, wireless, optical, etc.) for communicating icing condition information to one or more computing devices external to wind turbine 300. In some embodiments, the communication system 365 may be implemented, at least in part, using circuitry included within the controller 305 (such as an installed network interface card). In other embodiments, the communication system 365 is implemented using circuitry separate from the controller 305.

Consistent with the discussion above, the icing condition information may be communicated by the communication system 365 in any suitable form. Some non-limiting examples of icing condition information include whether ice has formed on wind turbine 300 as a binary "yes" or "no," the likelihood that ice has formed on wind turbine 300, whether the accumulation of ice on wind turbine 300 is increasing or decreasing, and the like. Advantageously, the transmission of icing condition information using communication system 365 may inform a customer, wind turbine operator, maintenance personnel, etc. of the cause of the reduction in power production of wind turbine 300 for a given wind condition.

FIG. 4 illustrates an exemplary method 400 for controlling operation of a wind turbine based on a determined icing condition in accordance with one or more embodiments. The method 400 may be performed in conjunction with embodiments disclosed herein, such as using the controller 305 of fig. 3.

The method 400 optionally begins at block 405 where the controller determines a reference signal. The reference signal may correspond to an acquired sensor signal acquired under a particular condition, such as a predetermined ice-free condition. At block 415, the controller determines one or more characteristics of the sensor signal received from the mechanical sensor. In some embodiments, the sensor signal comprises one of a wind speed signal and a wind direction signal. In some embodiments, the controller determines one or more characteristics of the sensor signal in the frequency domain. In some embodiments, the controller determines one or more characteristics of the sensor signal in the time domain.

At block 425, the controller determines an icing condition of the wind turbine based on one or more characteristics of the sensor signal. At block 435, the controller controls operation of one or more wind turbine systems based on the determined icing condition. In some embodiments, the controller controls one or more of the de-icing system, the power generation system, and the communication system based on the determined icing condition. After completion of block 435, the method 400 ends.

Fig. 5A and 5B illustrate exemplary sensor signals without and with ice accumulation, respectively, in accordance with one or more embodiments. In graph 500, sensor signal 505 represents a wind direction signal from a mechanical wind vane without ice accumulation. In graph 550, sensor signal 555 represents a wind direction signal from a mechanical wind vane having ice accumulation. In the graphs 500, 550, the horizontal axis depicts time in seconds(s) (or alternatively samples). The vertical axis depicts the direction of the rotor in degrees (°) ("relative direction") relative to the current wind direction. Due to the mechanical damping effect of the accumulated ice, most of the high frequency components included in the sensor signal 505 are not present in the sensor signal 555.

Fig. 6A and 6B illustrate frequency information of exemplary sensor signals without and with ice accumulation, respectively, in accordance with one or more embodiments. The frequency information may be acquired according to any suitable technique, such as performing an FFT on the sensor signal.

In graph 600, a frequency spectrum 605 represents frequency information included in a wind direction signal (such as sensor signal 505 of FIG. 5A) from a mechanical wind vane without ice accumulation. In graph 650, frequency spectrum 655 represents frequency information included in a wind direction signal (such as sensor signal 555 of FIG. 5B) from a mechanical wind vane having ice build-up. In the graphs 600, 650, the horizontal axis depicts frequency in hertz (Hz) and the vertical axis depicts amplitude.

In the graphs 600, 650, the frequency peak 610 occurs at approximately 0.7Hz, which generally corresponds to the effect of each rotor blade by a mechanical wind vane (or other mechanical sensor) during rotation of the rotor. In some embodiments, the effect comprises a 3P effect. In some embodiments, the higher harmonics of the contribution are also present in the frequency spectra 605, 655. In some embodiments, each contribution passing through the rotor blade is filtered out of the frequency spectrum 605, 655 prior to determining the icing condition of the wind turbine. In other embodiments, each through-rotor-blade effect remains in the frequency spectrum 605, 655 when determining the icing condition of the wind turbine.

The frequency spectrum 605 representing frequency information corresponding to a mechanical wind vane without ice accumulation is relatively evenly distributed over the frequency range shown, with many amplitude values around 0.1. On the other hand, the frequency spectrum 655 representing frequency information corresponding to a mechanical wind vane with ice accumulation has a distribution that is inclined to lower frequencies due to the effect of mechanical damping of the mechanical wind sensor. Based on the frequency information included in the frequency spectrums 605, 655, any suitable technique may be used to determine the icing condition of the wind turbine.

In one embodiment, determining an icing condition of the wind turbine includes determining whether a predetermined percentage of power (power) included in the sensor signal occurs below a predetermined threshold frequency. For example, the predetermined percentage of power may represent a predetermined percentage of the power spectral density of the sensor signal. Graphs 600, 650 depict a first threshold frequency 615-1 of approximately 1Hz and a second threshold frequency 615-2 of approximately 2 Hz. In graph 600, approximately 20% of the power in spectrum 605 occurs between 0Hz and the first threshold frequency 615-1, and approximately 60% of the power occurs between the second threshold frequency 615-2 and 5 Hz. However, in graph 650, a much larger percentage (e.g., about 70%) of the power in the frequency spectrum 655 occurs between 0Hz and the first threshold frequency 615-1, and a much smaller percentage (e.g., about 15%) of the power occurs between the second threshold frequency 615-2 and 5 Hz.

Assume that the controller has a power that occurs below a first threshold frequency 615-1 by a predetermined percentage value of 50% and/or above a second threshold frequency 615-2 by a predetermined percentage value of 25%. Thus, the frequency spectrum 605 may indicate that the icing condition of the wind turbine corresponds to an ice-free condition, and the frequency spectrum 655 may indicate that ice has accumulated on the wind turbine.

In one embodiment, determining the icing condition of the wind turbine comprises determining whether the power comprised in the sensor signal exceeds a predetermined magnitude at a predetermined frequency. E.g. at a first predetermined frequency f₁At this point, the spectrum 605 has an amplitude a1 of about 0.22, and the spectrum 655 has an amplitude a2 of about 0.68. Assuming that the controller has a predetermined amplitude value of 0.5, the frequency spectrum 605 may indicate that the icing condition of the wind turbine corresponds to an ice-free condition, and the frequency spectrum 655 may indicate that ice has accumulated on the wind turbine.

In one embodiment, determining the icing condition of the wind turbine comprises determining whether the power comprised in the sensor signal exceeds a predetermined magnitude within a predetermined frequency interval. For example, within a predetermined frequency interval 620 between about 0.6Hz and 0.8Hz, the power included in the frequency spectrum 605 is primarily concentrated at the frequency peak 610, which may indicate that no ice is accumulated on the wind turbine. Within the predetermined frequency interval 620, a significantly greater amount of power is included at frequencies near the frequency peak 610 in the frequency spectrum 655, which may indicate that ice has accumulated on the wind turbine.

In one embodiment, determining the icing condition of the wind turbine comprises determining whether the average power comprised in the sensor signal exceeds a predetermined magnitude within a predetermined frequency interval. As described above, the power included in the frequency spectrum 655 is much greater than the amount of power included in the frequency spectrum 605 over the predetermined frequency interval 620 between about 0.6Hz and 0.8 Hz. This corresponds to a greater average power at frequency 655, which may indicate that ice has accumulated on the wind turbine.

In one embodiment, determining the icing condition of the wind turbine comprises determining a maximum frequency value (or maximum frequency interval) wherein the magnitude of the power comprised in the sensor signal exceeds a predetermined magnitude. In some cases, the maximum frequency value (or maximum frequency interval) may be compared to a threshold value. In some cases, the frequency peaks 610 may be filtered out of the frequency spectrum 605, 655 before determining the maximum frequency value or maximum frequency interval. For example, maximum frequency values with amplitudes exceeding 0.3 are less than about 0.1Hz for spectrum 605 and about 0.5Hz for spectrum 655. Larger frequency values of frequency spectrum 655 may indicate that ice has accumulated on the wind turbine.

In one embodiment, determining the icing condition of the wind turbine comprises determining a ratio of a first average of the power comprised in the first frequency interval to a second average of the power comprised in the second frequency interval. The graphs 600, 650 include a first frequency interval 625-1 between approximately 0.5Hz and 1Hz, and a second frequency interval 625-2 between approximately 2Hz and 3 Hz. For spectrum 605, the average of both the first frequency interval 625-1 and the second frequency interval 625-2 is about 0.1, which results in a ratio of about 1 (0.1/0.1). However, for spectrum 655, the average value of the first frequency interval 625-1 is about 0.45 and the average value of the second frequency interval 625-2 is about 0.05, resulting in a ratio of about 9 (0.45/0.05). Thus, for this example, a relatively large ratio may indicate that ice has accumulated on the wind turbine. In some embodiments, the ratio may be compared to a threshold value to determine an icing condition of the wind turbine.

In one embodiment, determining the icing condition of the wind turbine comprises determining a shape of the power comprised in the sensor signal. For example, the controller may use feature detection to determine the shape of the power. The spectrum 605 appears substantially rectangular over the illustrated frequency range due to its relatively uniform distribution, while the spectrum 655 appears more like a triangle.

While each technique is described above as a separate embodiment, other embodiments may include a combination of techniques in determining icing conditions. For example, the controller may weight the results from the different techniques to determine icing conditions. Further, other techniques suitable for distinguishing frequency information included in different frequency spectrums 605, 655 are also contemplated.

Further, while the technique is described above as simply determining whether ice has accumulated on the wind turbine, the technique is also applicable to determining other icing condition information. For example, the techniques may be used to determine a likelihood that ice has formed on the wind turbine, estimate how much ice has formed on the wind turbine, whether the accumulation of ice on the wind turbine is increasing or decreasing, a rate of change of the accumulation of ice, and so forth.

In some embodiments, the controller is configured to determine whether the accumulation of ice on the wind turbine is increasing or decreasing based on a direction of displacement within a frequency spectrum of the plurality of sensor signals. The plurality of sensor signals may include a reference signal corresponding to a predetermined icing condition (e.g., an ice-out condition), and/or a previously acquired sensor signal. Generally, as shown in frequency spectrums 605, 655, a shift to the left (towards lower frequencies) indicates that the accumulation of ice on the wind turbine is increasing and a shift to the right (towards higher frequencies) indicates that the accumulation of ice on the wind turbine is decreasing.

Further, although the frequency information discussed above uses a specific example of a wind direction signal from a mechanical wind vane, other embodiments may obtain frequency information from other types of mechanical sensors (such as mechanical anemometers).

As described above, in some embodiments, the controller is configured to determine one or more characteristics of the received sensor signal in the time domain. FIG. 7 illustrates determining a moving standard deviation of an exemplary sensor signal without and with ice accumulation in accordance with one or more embodiments.

In the graph 700, the horizontal axis depicts time in seconds(s) (or alternatively samples). The vertical axis depicts the standard deviation of wind speed in meters per second (m/s). The first signal 705 represents the moving standard deviation of the wind speed signal from a mechanical anemometer without ice accumulation and the second signal 710 represents the moving standard deviation of the wind speed signal from a mechanical anemometer with ice accumulation. The graph 700 also depicts a threshold 715.

In one embodiment, determining the icing condition of the wind turbine includes determining whether the average of the moving standard deviations is greater than or less than a threshold 715. Generally, a larger standard deviation value corresponds to a larger difference in the amplitude of the sensor signal, which indicates less mechanical damping and therefore less (or no) ice accumulation. Conversely, a smaller standard deviation value indicates greater mechanical damping and therefore greater ice accumulation. The average value of the first signal 705 is greater than the threshold 715 and the average value of the second signal 710 is less than the threshold 715. Other techniques may also be used to determine icing conditions of the wind turbine. In one example, the mobile standard deviation value may be compared to a reference value. In another example, one or more other statistics may be calculated for the sensor signal.

In some embodiments, the controller is configured to determine whether the ice accumulation on the wind turbine is increasing or decreasing based on a direction of displacement within the moving standard deviation signal corresponding to the plurality of sensor signals. The plurality of sensor signals may include a reference signal corresponding to a predetermined icing condition (e.g., an ice-out condition), and/or a previously acquired sensor signal. Generally, as shown in the graph 700, a downward shift (toward a lower standard deviation value) indicates that the accumulation of ice on the wind turbine is increasing, while an upward shift (toward a larger standard deviation value) indicates that the accumulation of ice on the wind turbine is decreasing.

Further, although one or more features in the time domain discussed above use a particular example of a wind speed signal from a mechanical anemometer, other embodiments may determine one or more features in the time domain from other types of mechanical sensors (such as mechanical wind vanes).

FIG. 8 illustrates an exemplary method 800 for controlling operation of a wind turbine based on whether ice buildup on the wind turbine is increasing in accordance with one or more embodiments. The method 800 may be used in conjunction with other embodiments, such as the controller 305 of FIG. 3 that determines whether ice accumulation on a wind turbine is increasing or decreasing based on the shift direction described with respect to FIGS. 6A, 6B, and 7.

The method 800 begins at block 805, where the controller 305 determines that ice accumulation is increasing based on one or more characteristics of the sensor signal received from the mechanical sensor. At block 815, the controller performs a first predetermined operation using one or more wind turbine systems. In some embodiments, the controller is further configured to determine that the first predetermined operation is more economically beneficial (e.g., corresponds to less power production loss) than the one or more other predetermined operations. In some embodiments, the first predetermined operation comprises operating the power generation system of the wind turbine with reduced efficiency. Other embodiments may include operating the power generation system differently, operating the de-icing system, and/or operating the communication system of one or more wind turbine systems.

At block 825, the controller determines that ice build-up is not increasing. At block 835, the controller performs a second predetermined operation using one or more wind turbine systems. In some embodiments, the controller is further configured to determine that the second predetermined operation is more economically beneficial (e.g., corresponds to less power production loss) than the one or more other predetermined operations. In some embodiments, the second predetermined operation includes operating a de-icing system. Other embodiments may include operating the power generation system differently and/or operating the communication system of one or more wind turbine systems. After block 835 is complete, method 800 ends.

In the foregoing, reference is made to the embodiments presented in this disclosure. However, the scope of the present disclosure is not limited to the specifically described embodiments. Rather, any combination of the above-provided features and elements (whether related to different embodiments or not) is contemplated to implement and practice the contemplated embodiments. Moreover, although embodiments disclosed herein may achieve advantages over other possible solutions or over the prior art, whether or not a particular advantage is achieved by a given embodiment is not limiting of the scope of the disclosure. Thus, the aspects, features, embodiments and advantages described herein are merely exemplary and should not be considered elements or limitations of the appended claims (unless explicitly recited in a claim).

As will be appreciated by one skilled in the art, the embodiments disclosed herein may be embodied as a system, method or computer program product. Accordingly, aspects may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," module "or" system. Furthermore, aspects may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied therein.

The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium (e.g., a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing), having computer-readable program instructions thereon for causing a processor to perform various aspects of the present invention.

Aspects of the present disclosure are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments presented in the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In view of the foregoing, the scope of the present disclosure is to be determined by the claims that follow.